diff --git a/DataFit/.ipynb b/DataFit/.ipynb deleted file mode 100644 index 0639bf6..0000000 --- a/DataFit/.ipynb +++ /dev/null @@ -1,986 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Windows\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,8,24)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "plebiscito = datetime(2020,10,25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. RM region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "tstate = '13'\n", - "\n", - "# Import Data\n", - "RM = ImportData(tstate=tstate,initdate = initdate)\n", - "RM.importdata()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#RM.Hr = np.array(RM.UCI) + np.array(RM.UTI)\n", - "#RM.Hr_tot = np.array(RM.UCI_tot) + np.array(RM.UTI_tot)\n", - "RM.Hr = np.array(RM.UTI)\n", - "RM.Hr_tot = np.array(RM.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "beta = 0.12\n", - "mu = 1.6\n", - "k_I = 0\n", - "k_R = 0\n", - "SeroPrevFactor =0.07\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.85\n", - "rem_mob = 0.45\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.428 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.528 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.012 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.032# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "t_sp = 100" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "increasedays = 15\n", - "# Daily amount of people \n", - "dailyincrease = 2000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case we add 30.000 persons to the simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0.15,0.3,0.45,0.6,0.75]" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "#renewalFactor=0.6\n", - "initincrease=0\n", - "increasedays =initincrease+45\n", - "# Daily amount of people\n", - "dailyincrease = [RM.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [SeroPrevDynamics(t_sp,t_sp+increasedays*0.4,t_sp+increasedays,i) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chiplot = [[chi[i](t) for t in range(tsim)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(range(tsim),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = RM,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_qt5.py\", line 496, in _draw_idle\n", - " self.draw()\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\", line 393, in draw\n", - " self.figure.draw(self.renderer)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\", line 38, in draw_wrapper\n", - " return draw(artist, renderer, *args, **kwargs)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\", line 1735, in draw\n", - " mimage._draw_list_compositing_images(\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\", line 137, in _draw_list_compositing_images\n", - " a.draw(renderer)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\", line 38, in draw_wrapper\n", - " return draw(artist, renderer, *args, **kwargs)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 2630, in draw\n", - " mimage._draw_list_compositing_images(renderer, self, artists)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\", line 137, in _draw_list_compositing_images\n", - " a.draw(renderer)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\", line 38, in draw_wrapper\n", - " return draw(artist, renderer, *args, **kwargs)\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\", line 1227, in draw\n", - " ticks_to_draw = self._update_ticks()\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\", line 1103, in _update_ticks\n", - " major_locs = self.get_majorticklocs()\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\", line 1348, in get_majorticklocs\n", - " return self.major.locator()\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\dates.py\", line 1338, in __call__\n", - " self.refresh()\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\dates.py\", line 1364, in refresh\n", - " dmin, dmax = self.viewlim_to_dt()\n", - " File \"D:\\Usuarios\\tvelozg\\Anaconda3\\lib\\site-packages\\matplotlib\\dates.py\", line 1094, in viewlim_to_dt\n", - " raise ValueError('view limit minimum {} is less than 1 and '\n", - "ValueError: view limit minimum 0.0 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=8)]: Done 1 tasks | elapsed: 4.1s\n", - "[Parallel(n_jobs=8)]: Done 2 out of 5 | elapsed: 4.3s remaining: 6.5s\n", - "[Parallel(n_jobs=8)]: Done 3 out of 5 | elapsed: 4.3s remaining: 2.9s\n", - "[Parallel(n_jobs=8)]: Done 5 out of 5 | elapsed: 6.6s remaining: 0.0s\n", - "[Parallel(n_jobs=8)]: Done 5 out of 5 | elapsed: 6.6s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = t_sp" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 158, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(RM.I_ac_r_dates,RM.I_ac_r)\n", - "axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Hospitalized Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(RM.hosp_dates,RM.Br_hosp)\n", - "axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('UTI Usage')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].scatter(RM.sochimi_dates,RM.UTI)\n", - "axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(RM.sochimi_dates,RM.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.05011586540191056,\n", - " 0.05527300024222186,\n", - " 0.06042458795857955,\n", - " 0.06560629457612131,\n", - " 0.07077942070911006]" - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": 264, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'simulation1' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mS\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'S'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mE\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'E'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mI\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'I'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'R'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimulation1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mB\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'B'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'simulation1' is not defined" - ] - } - ], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(RM.sochimi_tr,RM.Hr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(RM.Ir==max(RM.Ir))[0][0]\n", - "datapeakdate = RM.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/05_Valparaiso_SEIRHVD5.ipynb b/DataFit/05_Valparaiso_SEIRHVD5.ipynb deleted file mode 100644 index 94d5299..0000000 --- a/DataFit/05_Valparaiso_SEIRHVD5.ipynb +++ /dev/null @@ -1,1316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n" - ] - } - ], - "source": [ - "tstate = '12'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "178362" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Región de Magallanes'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'ImportData' object has no attribute 'UTI'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUTI\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHr_tot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUTI_tot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'ImportData' object has no attribute 'UTI'" - ] - } - ], - "source": [ - "state.Hr = np.array(state.UTI)\n", - "state.Hr_tot = np.array(state.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "beta = 0.075\n", - "mu = 0.3\n", - "k_I = 0\n", - "k_R = 0\n", - "SeroPrevFactor =0.2\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.6\n", - "rem_mob = 0.35\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.3\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.433 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.005 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.002# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = (fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = t_sp + 0\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.01,t_sp_temp+increasedays,dailyincrease,form='quadratic')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = np.array([0.0,0.3,0.45,0.6,1.5])/5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "increasedays = 50\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(fiestaspatrias_day-10,fiestaspatrias_day+increasedays*0.50001,fiestaspatrias_day+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 5, 15, 0, 0)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'chi' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimulation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mSEIRHVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtsim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_I\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk_I\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_R\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_R\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeroPrevFactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSeroPrevFactor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mexpinfection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexpinfection\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRealIC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mImi_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImi_det\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mIas_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIas_det\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'chi' is not defined" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = state,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = t_sp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plt.title('VMI Usage')\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(state.sochimi_dates,state.Vr)\n", - "plt.scatter(state.sochimi_dates,state.Vr_tot)\n", - "plt.plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(state.Br_dates,state.Br,label='Muertes Hospitalizados')\n", - "plt.scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "plt.scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "#for i in range(len(simulation)): \n", - "# axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "#axs[1,0].set_title('UTI Usage')\n", - "#axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "#axs[1,0].scatter(state.sochimi_dates,state.UTI)\n", - "#axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[1,0].legend(loc=0)\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(state.sochimi_dates,state.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/09_Araucania_SEIRHVD5.ipynb b/DataFit/09_Araucania_SEIRHVD5.ipynb deleted file mode 100644 index f0a1950..0000000 --- a/DataFit/09_Araucania_SEIRHVD5.ipynb +++ /dev/null @@ -1,1246 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "tstate = '12'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.name = 'Región de Magallanes'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = np.array(state.UTI)\n", - "state.Hr_tot = np.array(state.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "beta = 0.075\n", - "mu = 0.3\n", - "k_I = 0\n", - "k_R = 0\n", - "SeroPrevFactor =0.2\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.6\n", - "rem_mob = 0.35\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.3\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.433 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.005 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.002# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = (fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = t_sp + 0\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.01,t_sp_temp+increasedays,dailyincrease,form='quadratic')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = np.array([0.0,0.3,0.45,0.6,1.5])/5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "increasedays = 50\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(fiestaspatrias_day-10,fiestaspatrias_day+increasedays*0.50001,fiestaspatrias_day+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = state,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = t_sp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plt.title('VMI Usage')\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(state.sochimi_dates,state.Vr)\n", - "plt.scatter(state.sochimi_dates,state.Vr_tot)\n", - "plt.plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(state.Br_dates,state.Br,label='Muertes Hospitalizados')\n", - "plt.scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "plt.scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "#for i in range(len(simulation)): \n", - "# axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "#axs[1,0].set_title('UTI Usage')\n", - "#axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "#axs[1,0].scatter(state.sochimi_dates,state.UTI)\n", - "#axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[1,0].legend(loc=0)\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(state.sochimi_dates,state.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5-final" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/10_LosLagos_SEIRHVD5.ipynb b/DataFit/10_LosLagos_SEIRHVD5.ipynb deleted file mode 100644 index f0a1950..0000000 --- a/DataFit/10_LosLagos_SEIRHVD5.ipynb +++ /dev/null @@ -1,1246 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "tstate = '12'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.name = 'Región de Magallanes'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = np.array(state.UTI)\n", - "state.Hr_tot = np.array(state.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "beta = 0.075\n", - "mu = 0.3\n", - "k_I = 0\n", - "k_R = 0\n", - "SeroPrevFactor =0.2\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.6\n", - "rem_mob = 0.35\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.3\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.433 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.005 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.002# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = (fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = t_sp + 0\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.01,t_sp_temp+increasedays,dailyincrease,form='quadratic')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = np.array([0.0,0.3,0.45,0.6,1.5])/5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "increasedays = 50\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(fiestaspatrias_day-10,fiestaspatrias_day+increasedays*0.50001,fiestaspatrias_day+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = state,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = t_sp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plt.title('VMI Usage')\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(state.sochimi_dates,state.Vr)\n", - "plt.scatter(state.sochimi_dates,state.Vr_tot)\n", - "plt.plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(state.Br_dates,state.Br,label='Muertes Hospitalizados')\n", - "plt.scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "plt.scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "#for i in range(len(simulation)): \n", - "# axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "#axs[1,0].set_title('UTI Usage')\n", - "#axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "#axs[1,0].scatter(state.sochimi_dates,state.UTI)\n", - "#axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[1,0].legend(loc=0)\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(state.sochimi_dates,state.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5-final" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/12_Magallanes_SEIRHVD5.ipynb b/DataFit/12_Magallanes_SEIRHVD5.ipynb deleted file mode 100644 index 458dcfc..0000000 --- a/DataFit/12_Magallanes_SEIRHVD5.ipynb +++ /dev/null @@ -1,1288 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "tstate = '12'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "178362" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Región de Magallanes'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = np.array(state.UTI)\n", - "state.Hr_tot = np.array(state.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.074\n", - "mu = 0.3\n", - "k_I = 0\n", - "k_R = 0\n", - "\n", - "SeroPrevFactor =0.2\n", - "\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "#quarantine = datetime(2020,8,22)\n", - "quarantine = datetime(2020,7,25)\n", - "qday = (quarantine-initdate).days\n", - "# Quarantines \n", - "max_mob = 0.6\n", - "rem_mob = 0.35\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=qday)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.5\n", - "Ias_det = 0.3\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.433 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.005 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.002# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# First increase\n", - "initincrease = datetime(2020,8,8)\n", - "initincreaseday = (initincrease - initdate).days\n", - "endincrease = datetime(2020,8,28)\n", - "increasedays = (endincrease - initincrease).days \n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = t_sp + 0\n", - "chi0 = SeroPrevDynamics(initincreaseday,initincreaseday+increasedays*0.01,initincreaseday+increasedays,dailyincrease,form='quadratic')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = np.array([0.0,0.3,0.45,0.6,0.8])/1.5" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Increase date \n", - "increasedate = datetime(2020,10,25)\n", - "increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 200\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(increaseday-2,increaseday+increasedays*0.50001,increaseday+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 5, 15, 0, 0)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = state,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 3.0s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 3.0s remaining: 4.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 3.0s remaining: 2.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 3.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 3.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = t_sp\n", - "\n", - "\n", - "enddate = initdate + timedelta(days=1.5*365)\n", - "#enddate = datetime(2020,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Región de Magallanes')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(round(renewalFactor[i],2))+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,7,19), linestyle = 'dotted',color = 'grey',label='Paso a fase 3')\n", - "axs[0,0].axvline(x = datetime(2020,8,9), linestyle = 'dashed',color = 'grey',label='Paso a fase 2')\n", - "axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dashdot',color = 'grey',label='Paso a fase 1')\n", - "\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(round(renewalFactor[i],2)),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "axs[0,1].axvline(x = datetime(2020,7,19), linestyle = 'dotted',color = 'grey',label='Paso a fase 3')\n", - "axs[0,1].axvline(x = datetime(2020,8,9), linestyle = 'dashed',color = 'grey',label='Paso a fase 2')\n", - "axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dashdot',color = 'grey',label='Paso a fase 1')\n", - "\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "#for i in range(len(simulation)): \n", - "# axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "#axs[1,0].set_title('UTI Usage')\n", - "#axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "#axs[1,0].scatter(state.sochimi_dates,state.UTI)\n", - "#axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[1,0].legend(loc=0)\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(round(renewalFactor[i],2)),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = datetime(2020,7,19), linestyle = 'dotted',color = 'grey',label='Paso a fase 3')\n", - "axs[1,0].axvline(x = datetime(2020,8,9), linestyle = 'dashed',color = 'grey',label='Paso a fase 2')\n", - "axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dashdot',color = 'grey',label='Paso a fase 1')\n", - "\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(round(renewalFactor[i],2)),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "axs[1,1].scatter(state.sochimi_dates,state.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = datetime(2020,7,19), linestyle = 'dotted',color = 'grey',label='Paso a fase 3')\n", - "axs[1,1].axvline(x = datetime(2020,8,9), linestyle = 'dashed',color = 'grey',label='Paso a fase 2')\n", - "axs[1,1].axvline(x = datetime(2020,8,22), linestyle = 'dashdot',color = 'grey',label='Paso a fase 1')\n", - "\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "axs[1,1].legend(loc=0)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 672, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 672, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": 673, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.1368385493965184,\n", - " 0.1531416186379188,\n", - " 0.16129183185505935,\n", - " 0.1694434429178627,\n", - " 0.18031078315804094]" - ] - }, - "execution_count": 673, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown Data" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "PA = Data['12101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(PA.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/13_Metropolitana_SEIRHVD5.ipynb b/DataFit/13_Metropolitana_SEIRHVD5.ipynb deleted file mode 100644 index 04b0d3b..0000000 --- a/DataFit/13_Metropolitana_SEIRHVD5.ipynb +++ /dev/null @@ -1,1698 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,10,19)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19458310" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Chile'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.sochimi_tr = state.UCI_tr[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.85\n", - "rem_mob = 0.45\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q2.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 7.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 7.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0.15,0.3,0.45,0.6,0.75]" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "navidad = datetime(2020,12,25)\n", - "navidad_day = (datetime(2020,12,25)-initdate).days\n", - "# Increase date \n", - "increasedate = datetime(2020,12,25)\n", - "increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 20\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "#chi = [chisum(chi0,SeroPrevDynamics(increaseday,increaseday+increasedays*0.50001,increaseday+increasedays,i)) for i in dailyincrease]\n", - "chi = [chisum(chi0,SeroPrevDynamics(navidad_day-4,navidad_day+increasedays*0.0001,navidad_day+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i,\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 2.6s remaining: 3.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 3.1s remaining: 2.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 3.5s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 3.5s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,1,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "153" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(state.I_d_r)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "#axs[0,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[0,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[0,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "#\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "#axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[0,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[0,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "#axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('UCI Usage')\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='UCI covid use')\n", - "axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "#axs[1,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].t,simulation[i].I,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "17039" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.23391937010287128,\n", - " 0.27568977162322367,\n", - " 0.3247580649794889,\n", - " 0.3811697591259596,\n", - " 0.44363540047649497]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pandas GUI" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "from pandasgui import show" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([17039, 11938, 11434, 10931, 10723, 10914, 10618, 11200, 11002,\n", - " 11240, 10900, 11330, 11272, 11688, 11674, 12431, 12981, 14909,\n", - " 15332, 16515, 15339, 19074, 19160, 23833, 26131, 30447, 29007,\n", - " 31423, 30625, 30847, 29400, 27598, 25317, 26196, 25374, 26068])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.Ir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "data = {'Ir':state.Ir,'Ir_dates':state.Ir_dates,'Ir_t':state.tr,'Dr':state.Dr,'Dr_dates':state.Br_dates,'Dr_t':state.Br_tr}" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "dataframe = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in data.items() ]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Ir Ir_dates Ir_t Dr Dr_dates Dr_t\n", - "0 17039.0 2020-10-19 0.0 46 2020-10-19 0\n", - "1 11938.0 2020-10-24 5.0 53 2020-10-20 1\n", - "2 11434.0 2020-10-26 7.0 43 2020-10-21 2\n", - "3 10931.0 2020-10-30 11.0 46 2020-10-22 3\n", - "4 10723.0 2020-11-02 14.0 43 2020-10-23 4\n", - ".. ... ... ... .. ... ...\n", - "117 NaN NaT NaN 61 2021-02-13 117\n", - "118 NaN NaT NaN 56 2021-02-14 118\n", - "119 NaN NaT NaN 73 2021-02-15 119\n", - "120 NaN NaT NaN 44 2021-02-16 120\n", - "121 NaN NaT NaN 15 2021-02-17 121\n", - "\n", - "[122 rows x 6 columns]" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'show' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'show' is not defined" - ] - } - ], - "source": [ - "show(dataframe)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/14_LosRios_SEIRHVD5.ipynb b/DataFit/14_LosRios_SEIRHVD5.ipynb deleted file mode 100644 index 2b75ad6..0000000 --- a/DataFit/14_LosRios_SEIRHVD5.ipynb +++ /dev/null @@ -1,1404 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = '14'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "405835" - ] - }, - "execution_count": 257, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Región de Los Rios'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = np.array(state.UTI)\n", - "state.Hr_tot = np.array(state.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 0.108\n", - "mu = 0.3\n", - "k_I = 0\n", - "k_R = 0\n", - "SeroPrevFactor =0.35\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Region de los Rios\n", - "Ellos tuvieron cordón sanitario que comenzó el 12 de julio y el vienes 02 de Octubre entraron en Fase 2" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "SPchange_date = datetime(2020,9,11)\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.4\n", - "rem_mob = 0.35\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.3\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.433 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.005 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.002# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 263, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 264, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = (fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.0\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = t_sp + 0\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.01,t_sp_temp+increasedays,dailyincrease,form='quadratic')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 265, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = np.array([0.0,0.3,0.45,0.6,1.5])/5" - ] - }, - { - "cell_type": "code", - "execution_count": 266, - "metadata": {}, - "outputs": [], - "source": [ - "# Increase date \n", - "increasedate = datetime(2020,10,23)\n", - "increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 50\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 267, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(increaseday,increaseday+increasedays*0.50001,increaseday+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 268, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 268, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 269, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 5, 15, 0, 0)" - ] - }, - "execution_count": 269, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 270, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = state,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 271, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 3.0s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 3.0s remaining: 4.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 3.1s remaining: 2.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 3.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 3.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 282, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=2*365)\n", - " enddate = datetime(2020,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 283, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 284, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 285, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Región de Los Rios')" - ] - }, - "execution_count": 285, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[0,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[0,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "#axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[0,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[0,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "#for i in range(len(simulation)): \n", - "# axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "#axs[1,0].set_title('UTI Usage')\n", - "#axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "#axs[1,0].scatter(state.sochimi_dates,state.UTI)\n", - "#axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[1,0].legend(loc=0)\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "\n", - "axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "axs[1,1].scatter(state.sochimi_dates,state.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "\n", - "axs[1,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[1,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[1,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 253, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 253, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Beta_Rt_UK_Chile.ipynb b/DataFit/Beta_Rt_UK_Chile.ipynb deleted file mode 100644 index 1077d15..0000000 --- a/DataFit/Beta_Rt_UK_Chile.ipynb +++ /dev/null @@ -1,260 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estudio de Beta para nueva y vieja cepa\n", - "Cálculo del beta para la nueva cepa, comparación con el beta actual para aplicación eqyuvalente en el simulador actual\n", - "\n", - "\\begin{align}\n", - "\\beta = \\gamma R_{0} \\\\\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - }, - { - "ename": "IndentationError", - "evalue": "unexpected indent (importdata.py, line 1433)", - "output_type": "error", - "traceback": [ - "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", - " File \u001b[1;32m\"/home/samuel/.local/lib/python3.8/site-packages/IPython/core/interactiveshell.py\"\u001b[0m, line \u001b[1;32m3417\u001b[0m, in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m39\u001b[0;36m, in \u001b[0;35m\u001b[0;36m\u001b[0m\n\u001b[0;31m from importdata import ImportData\u001b[0m\n", - "\u001b[0;36m File \u001b[0;32m\"../src/utils/importdata.py\"\u001b[0;36m, line \u001b[0;32m1433\u001b[0m\n\u001b[0;31m print(help)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from datetime import timedelta\n", - "\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "\n", - "import json\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "\n", - "import timeutils " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Beta UK" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gamma = 1/7\n", - "R_min_UK = 1.4\n", - "R_max_UK = 1.8\n", - "R_mean_UK = (R_max_UK + R_min_UK)/2\n", - "beta_min_UK = gamma*R_min_UK\n", - "beta_max_UK = gamma*R_max_UK\n", - "beta_mean_UK = gamma*R_mean_UK\n", - "print('Beta min UK: '+str(beta_min_UK))\n", - "print('Beta max UK: '+str(beta_max_UK))\n", - "print('Beta mean UK: '+str(beta_mean_UK))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Beta - R_t: Chile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'http://192.168.2.223:5006/getNationalEffectiveReproduction'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auxnacional = requests.get('http://192.168.2.223:5006/getNationalEffectiveReproduction').json()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_e_Cl = auxnacional['mean']\n", - "R_e_dates = [timeutils.timeJStoPy(i) for i in auxnacional['dates']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(R_e_dates,R_e_Cl)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_e_cl_max = np.max(R_e_Cl[-20:-3])\n", - "R_e_cl_mean = np.mean(R_e_Cl[-20:-3])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_e_cl_mean" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_e_cl_max" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_e_Cl[-10:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta_cl_data = gamma*R_e_cl_mean" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta_cl_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta_cl_sim = 0.01035" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta_uk_sim = beta_cl_sim*R_max_UK/R_e_cl_max" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta_uk_sim" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/COVID19 Data Analysis.ipynb b/DataFit/COVID19 Data Analysis.ipynb deleted file mode 100644 index 2e59b82..0000000 --- a/DataFit/COVID19 Data Analysis.ipynb +++ /dev/null @@ -1,1981 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# COVID19 Data Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import requests\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Data\n", - "You must be connected to Dlab VPN for making this script work" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "initdate = datetime(2020,4,15)\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "\n", - "RM = ImportData(tstate = '14', initdate = initdate)\n", - "\n", - "RM.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "405835" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RM.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estudio VMI" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "initdate = datetime(2020,4,15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", - " 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", - " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", - " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", - " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", - " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", - " 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RM.Br_Nonhosp" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Dlab Endpoint Error\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "No information for counties, importing state information\n", - "Done\n" - ] - } - ], - "source": [ - "initdate = datetime(2020,4,15)\n", - "Santiago = ImportData(tstate = '13101', initdate = initdate)\n", - "Santiago.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PuntaArenas.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Analysis\n", - "Tasas de hopspitalizado de cada tipo sobre infectado activo \n", - "tasas entre ventiladores y uti/uci \n", - "y muertos sobre ventilados " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hospitalization Data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "UCI_VMI = [RM.UCI[i] - RM.VMI[i] for i in range(len(RM.UCI))]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(RM.sochimi_dates,RM.UTI,label='UTI')\n", - "plt.plot(RM.sochimi_dates,UCI_VMI,label='UCI - VMI')\n", - "plt.title('UCI-VMI vs UTI')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(RM.sochimi_tr,RM.VMI,label='VMI')\n", - "plt.plot(RM.sochimi_tr,RM.VMI_tot,label='VMI_tot')\n", - "plt.plot(RM.sochimi_tr,RM.VMI_sospechoso,label='VMI_suspects')\n", - "plt.plot(RM.sochimi_tr,RM.VMI_confirmado,label='VMI_confirmed')\n", - "plt.title('VMI Dynamics - RM')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'UCI vs UTI - RM')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(RM.sochimi_tr,RM.UTI,label='UTI', color = 'blue')\n", - "plt.plot(RM.sochimi_tr,RM.UTI_tot,label='UTI_tot',linestyle = 'dashed', color = 'blue')\n", - "plt.plot(RM.sochimi_tr,RM.UCI,label='UCI', color = 'red')\n", - "plt.plot(RM.sochimi_tr,RM.UCI_tot,label='UCI_tot',linestyle = 'dashed', color = 'red')\n", - "plt.legend(loc=0)\n", - "plt.title('UCI vs UTI - RM')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(RM.sochimi_tr,RM.UCI,label='UCI', color = 'blue')\n", - "plt.plot(RM.sochimi_tr,RM.UCI_tot,label='UCI_tot',linestyle = 'dashed', color = 'blue')\n", - "plt.plot(RM.sochimi_tr,RM.VMI,label='VMI', color = 'red')\n", - "plt.plot(RM.sochimi_tr,RM.VMI_tot,label='VMI_tot',linestyle = 'dashed', color = 'red')\n", - "plt.title('VMI vs UCI - RM')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(RM.sochimi_tr,RM.UTI,label='UTI', color = 'blue')\n", - "plt.plot(RM.sochimi_tr,RM.UTI_tot,label='UTI_tot',linestyle = 'dashed', color = 'blue')\n", - "plt.plot(RM.sochimi_tr,RM.VMI,label='VMI', color = 'red')\n", - "plt.plot(RM.sochimi_tr,RM.VMI_tot,label='VMI_tot',linestyle = 'dashed', color = 'red')\n", - "plt.plot(RM.sochimi_tr,RM.UCI,label='UCI', color = 'cyan')\n", - "plt.plot(RM.sochimi_tr,RM.UCI_tot,label='UCI_tot',linestyle = 'dashed', color = 'cyan')\n", - "plt.title('VMI vs UTI vs UCI - RM')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RM.sochimi_dates[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMI_UTI = [RM.VMI[i]/RM.UTI[i] for i in range(len(RM.UTI))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(RM.sochimi_dates,VMI_UTI,label='VMI/UTI')\n", - "plt.plot(RM.sochimi_dates,VMI_UTI,label='VMI/UTI')\n", - "plt.title('VMI UTI UCI rates - RM')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.mean(VMI_UTI)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.std(VMI_UTI)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UCI_VMI = [RM.UCI[i]/RM.VMI[i] for i in range(len(RM.UTI))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Mean ' + str(1/np.mean(UCI_VMI)))\n", - "print('STD ' + str(np.std(UCI_VMI)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Punta Arenas" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,8,14)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "PuntaArenas = ImportData(tstate = '09', initdate = initdate)\n", - "PuntaArenas.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "plt.plot(PuntaArenas.sochimi_dates,PuntaArenas.UTI,label='UTI', color = 'blue')\n", - "plt.plot(PuntaArenas.sochimi_dates,PuntaArenas.UTI_tot,label='UTI_tot',linestyle = 'dashed', color = 'blue')\n", - "plt.plot(PuntaArenas.sochimi_dates,PuntaArenas.VMI,label='VMI', color = 'red')\n", - "plt.plot(PuntaArenas.sochimi_dates,PuntaArenas.VMI_tot,label='VMI_tot',linestyle = 'dashed', color = 'red')\n", - "plt.plot(PuntaArenas.sochimi_dates,PuntaArenas.UCI,label='UCI', color = 'cyan')\n", - "plt.plot(PuntaArenas.sochimi_dates,PuntaArenas.UCI_tot,label='UCI_tot',linestyle = 'dashed', color = 'cyan')\n", - "plt.title('VMI vs UTI vs UCI - Punta Arenas')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 3)\n", - "\n", - "#for i in range(len(simulation)):\n", - "# axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(PuntaArenas.I_ac_r_dates,PuntaArenas.I_ac_r)\n", - "axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "axs[0,1].set_title('Hospitalized Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(PuntaArenas.Br_dates,PuntaArenas.Br,label='Muertes Totales')\n", - "axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "#for i in range(len(simulation)):\n", - "# axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,2].set_title('Daily new Infected')\n", - "axs[0,2].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,2].scatter(PuntaArenas.I_d_r_dates,PuntaArenas.I_d_r)\n", - "axs[0,2].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,2].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,2].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,2].legend(loc=0)\n", - "\n", - "\n", - "axs[1,0].set_title('UTI')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].scatter(PuntaArenas.sochimi_dates,PuntaArenas.UTI,label='UTI usage')\n", - "axs[1,0].scatter(PuntaArenas.sochimi_dates,PuntaArenas.UTI_tot,label='UTI capacity')\n", - "axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "\n", - "axs[1,1].set_title('VMI')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(PuntaArenas.sochimi_dates,PuntaArenas.Vr,label='VMI Usage')\n", - "axs[1,1].scatter(PuntaArenas.sochimi_dates,PuntaArenas.Vr_tot,label='VMI Capacity')\n", - "axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Región de la Araucanía')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "#for i in range(len(simulation)):\n", - "# axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(PuntaArenas.I_ac_r_dates,PuntaArenas.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "axs[0,1].set_title('Hospitalized Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(PuntaArenas.Br_dates,PuntaArenas.Br,label='Muertes Totales')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "#for i in range(len(simulation)):\n", - "# axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[1,0].set_title('Daily new Infected')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].scatter(PuntaArenas.I_d_r_dates,PuntaArenas.I_d_r)\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "axs[1,1].set_title('VMI')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(PuntaArenas.sochimi_dates,PuntaArenas.Vr,label='VMI Usage')\n", - "axs[1,1].scatter(PuntaArenas.sochimi_dates,PuntaArenas.Vr_tot,label='VMI Capacity')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle('Región de la Araucanía', fontsize=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time calibration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t = []\n", - "t_Ir = []\n", - "t_Hr = []\n", - "t_D = []\n", - "for i in range(currentday):\n", - " if i in RM.tr and i in RM.sochimi_tr:\n", - " t.append(i)\n", - " t_Ir.append(np.where(RM.tr==i))\n", - " t_Hr.append(np.where(RM.sochimi_tr==i))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RM.sochimi_tr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RM.tr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(t_Ir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hospitalized by type vs Active Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H_I = [RM.Hr[i]/RM.Ir[i] for i in range(len(t_Hr))]\n", - "V_I = [RM.Vr[i]/RM.Ir[i] for i in range(len(t_Hr))]\n", - "V_H = [RM.Vr[i]/RM.Hr[i] for i in range(len(RM.sochimi_tr))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(t,H_I,label= 'Hospitalized vs Infected')\n", - "plt.plot(t,V_I,label= 'Ventilators vs Infected')\n", - "plt.plot(RM.sochimi_tr,V_H, label = 'Vents/Hospitalized')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deaths: Hospitalized vs Non Hospitalized" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "initdate = datetime(2020,3,25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto57/fallecidos_hospitalizados.csv'\n", - "\n", - "aux = pd.read_csv(endpoint)\n", - "aux = aux.loc[aux['Region'] == 'Metropolitana']\n", - "Dr_hosp = aux.loc[aux['Hospitalizacion'] == 'VERDADERO']['2020-09-07']\n", - "Dr_Nonhosp = aux.loc[aux['Hospitalizacion'] == 'FALSO']['2020-09-07']\n", - "hosp_dates = [datetime.strptime(aux.loc[aux['Hospitalizacion'] == 'VERDADERO']['Fecha'].tolist()[i], '%Y-%m-%d') for i in range(len(Dr_hosp))]\n", - "\n", - "Br_hosp = Dr_hosp.cumsum()\n", - "Br_Nonhosp = Dr_Nonhosp.cumsum()\n", - "\n", - "index = np.where(np.array(hosp_dates) >= initdate)[0][0]\n", - "\n", - "Dr_hosp = Br_hosp[index:]\n", - "Dr_Nonhosp = Br_Nonhosp[index:]\n", - "\n", - "Br_hosp = Br_hosp[index:]\n", - "Br_Nonhosp = Br_Nonhosp[index:]\n", - "\n", - "hosp_dates = hosp_dates[index:]\n", - "hosp_tr = [(hosp_dates[i]-initdate).days for i in range(len(hosp_dates))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "V_F = [Br_Nonhosp.iloc[i]/Br_hosp.iloc[i] for i in range(len(Br_Nonhosp))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(hosp_dates,V_F)\n", - "plt.title('Proporción Muertes de No Hospitalizados / Muertes de Hospitalizados')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# -----------------------------------------------" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " cutlistendpoint = 'http://192.168.2.223:5006/getComunas'\n", - " #'http://192.168.2.220:8080/covid19/getProvincias' \n", - " cutlist = pd.read_json(cutlistendpoint)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cutlist" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cutlist[['state','county','ss','male_pop','female_pop','total_pop']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cutlist.loc[cutlist['state'] == int('13')]['total_pop'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population += int(cutlist.loc[cutlist['county'] == int('13101')]['total_pop'].sum())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19.csv'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux = pd.read_csv(endpoint)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 13101" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux.loc[aux['Codigo comuna']==int(i)].iloc[:,4].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cutlist" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Accumulated Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cut = '13101'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state = '13'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'http://192.168.2.223:5006/getTotalCasesByComuna?comuna='+cut\n", - "endpointState = 'http://192.168.2.223:5006/getTotalCasesAllComunasByState?state='+state\n", - "endPointTotal = 'http://192.168.2.223:5006/getTotalCasesAllComunas'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac = pd.DataFrame(requests.get(endPointTotal).json()['data'])\n", - "I_ac_dates = requests.get(endpointState).json()['dates']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = I_ac.filter(regex='^13',axis=1)\n", - "b = I_ac.filter(regex='^14',axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tstate = ['13101','13102','13103']\n", - "tstate = '13'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " \n", - " endPointTotal = 'http://192.168.2.223:5006/getTotalCasesAllComunas'\n", - " data = pd.DataFrame(requests.get(endPointTotal).json()['data'])\n", - "\n", - "\n", - " if type(tstate) == list:\n", - " counties = [i for i in tstate if len(i)>2 ]\n", - " states = [i for i in tstate if len(i)==2 ] \n", - " aux = []\n", - " for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))\n", - " \n", - " aux.append(data[counties])\n", - " I_ac_r = pd.concat(aux, axis=1).sum(axis=1)\n", - " \n", - " else:\n", - " if len(tstate) == 2:\n", - " I_ac_r = data.filter(regex='^'+tstate,axis=1).sum(axis=1)\n", - " elif len(tstate) > 2:\n", - " I_ac_r = data[tstate] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac_r" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac = requests.get(endpointState).json()['data']\n", - "I_ac_dates = requests.get(endpointState).json()['dates']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac_dates[0][:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[datetime.strptime(i[:10],'%Y-%m-%d') for i in I_ac_dates]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.concat([a,b], axis=1).sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tstate = ['13','14','15101']\n", - "counties = [i for i in tstate if len(i)>2 ]\n", - "states = [i for i in tstate if len(i)==2 ] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.DataFrame(requests.get(endPointTotal).json()['data'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux = []\n", - "for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux = []\n", - "for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))\n", - "\n", - "aux.append(data[counties])\n", - "I_ac = pd.concat(aux, axis=1).sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " counties = [i for i in tstate if len(i)>2 ]\n", - " states = [i for i in tstate if len(i)==2 ] \n", - " aux = []\n", - " for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))\n", - " \n", - " aux.append(data[counties])\n", - " I_ac = pd.concat(aux, axis=1).sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " endPointTotal = 'http://192.168.2.223:5006/getTotalCasesAllComunas'\n", - " data = pd.DataFrame(requests.get(endPointTotal).json()['data'])\n", - "\n", - "\n", - " if type(tstate) == list:\n", - " counties = [i for i in tstate if len(i)>2 ]\n", - " states = [i for i in tstate if len(i)==2 ] \n", - " aux = []\n", - " for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))\n", - " \n", - " aux.append(data[counties])\n", - " I_ac = pd.concat(aux, axis=1).sum(axis=1)\n", - " \n", - " else:\n", - " if len(tstate) == 2:\n", - " I_ac = data.filter(regex='^'+tstate,axis=1)\n", - " elif len(tstate) > 2:\n", - " I_ac = data[tstate] \n", - "\n", - " # Get and filter by dates\n", - " dates = pd.DataFrame(requests.get(endPointTotal).json()['dates'])\n", - " I_ac_r_dates = [datetime.strptime(i[:10],'%Y-%m-%d') for i in dates] \n", - " index = np.where(np.array(I_ac_r_dates) >= initdate)[0][0] \n", - " I_ac_r = I_ac_r[index:]\n", - " I_ac_r_dates = I_ac_r_dates[index:]\n", - " I_ac_r_tr = [(I_ac_r_dates[i]-initdate).days for i in range(len(I_ac_r))] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "initdate = datetime(2020,5,15)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " endPointTotal = 'http://192.168.2.223:5006/getTotalCasesAllComunas'\n", - " data = pd.DataFrame(requests.get(endPointTotal).json()['data'])\n", - "\n", - "\n", - " if type(tstate) == list:\n", - " counties = [i for i in tstate if len(i)>2 ]\n", - " states = [i for i in tstate if len(i)==2 ] \n", - " aux = []\n", - " for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))\n", - " \n", - " aux.append(data[counties])\n", - " I_ac_r = pd.concat(aux, axis=1).sum(axis=1)\n", - " \n", - " else:\n", - " if len(tstate) == 2:\n", - " I_ac_r = data.filter(regex='^'+tstate,axis=1)\n", - " elif len(tstate) > 2:\n", - " I_ac_r = data[tstate] \n", - "\n", - " # Get and filter by dates\n", - " dates = pd.DataFrame(requests.get(endPointTotal).json()['dates'])[0]\n", - " I_ac_r_dates = [datetime.strptime(i[:10],'%Y-%m-%d') for i in dates] \n", - " index = np.where(np.array(I_ac_r_dates) >= initdate)[0][0] \n", - " I_ac_r = I_ac_r[index:]\n", - " I_ac_r_dates = I_ac_r_dates[index:]\n", - " I_ac_r_tr = [(I_ac_r_dates[i]-initdate).days for i in range(len(I_ac_r))] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(requests.get(endPointTotal).json()['dates'])[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "I_ac = pd.DataFrame(I_ac).sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tstate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - " endpointComunas = 'http://192.168.2.223:5006/getComunas'\n", - " endpointRegiones = 'http://192.168.2.223:5006/getStates'\n", - " endpointSS = 'http://192.168.2.223:5006/getHealthServices'\n", - " \n", - " county = pd.read_json(endpointComunas)\n", - " #regiones = pd.read_json(endpointRegiones)\n", - " #ServicioSalud = pd.read_json(endpointSS)\n", - "\n", - " if type(tstate) == list:\n", - " population = 0\n", - " for i in tstate:\n", - " if len(i)==2: \n", - " population += int(county.loc[county['state'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['state'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['state'] == int(tstate)]['female_pop'].sum())\n", - " if len(i)>2:\n", - " population += int(county.loc[county['county'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['county'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['county'] == int(tstate)]['female_pop'].sum()) \n", - " else:\n", - " if len(tstate)==2:\n", - " population = int(county.loc[county['state'] == int(tstate)]['total_pop'].sum())\n", - " population_male = int(county.loc[county['state'] == int(tstate)]['male_pop'].sum())\n", - " population_female = int(county.loc[county['state'] == int(tstate)]['female_pop'].sum())\n", - " if len(tstate)>2:\n", - " population = int(county.loc[county['county'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['county'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['county'] == int(tstate)]['female_pop'].sum()) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " def importPopulation(self=None,endpoint = '',tstate = ''): \n", - " \"\"\"\n", - " Import Population\n", - " This Function imports the selected area population. \n", - "\n", - " input: \n", - " - tstate: [string or string list] CUT por comuna o región\n", - " - endpoint [string](opcional)\n", - " output:\n", - " - population [int] \n", - "\n", - " object variables:\n", - " self.population\n", - " self.population_male\n", - " self.population_female\n", - "\n", - " Example usage as function:\n", - " population = importPopulation(tstate = '13101',initdate=datetime(2020,5,15)) \n", - " \n", - " \"\"\"\n", - " print('Importing Population') \n", - "\n", - " if self:\n", - " tstate = self.tstate\n", - " else:\n", - " if not tstate:\n", - " raise Exception(\"State code missing\")\n", - "\n", - " endpointComunas = 'http://192.168.2.223:5006/getComunas'\n", - " endpointRegiones = 'http://192.168.2.223:5006/getStates'\n", - " endpointSS = 'http://192.168.2.223:5006/getHealthServices'\n", - " \n", - " county = pd.read_json(endpointComunas)\n", - " #regiones = pd.read_json(endpointRegiones)\n", - " #ServicioSalud = pd.read_json(endpointSS)\n", - "\n", - " if type(tstate) == list:\n", - " population = 0\n", - " for i in tstate:\n", - " if len(i)==2: \n", - " population += int(county.loc[county['state'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['state'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['state'] == int(tstate)]['female_pop'].sum())\n", - " if len(i)>2:\n", - " population += int(county.loc[county['county'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['county'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['county'] == int(tstate)]['female_pop'].sum()) \n", - " else:\n", - " if len(tstate)==2:\n", - " population = int(county.loc[county['state'] == int(tstate)]['total_pop'].sum())\n", - " population_male = int(county.loc[county['state'] == int(tstate)]['male_pop'].sum())\n", - " population_female = int(county.loc[county['state'] == int(tstate)]['female_pop'].sum())\n", - " if len(tstate)>2:\n", - " population = int(county.loc[county['county'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['county'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['county'] == int(tstate)]['female_pop'].sum()) \n", - " \n", - " if self:\n", - " self.population = population\n", - " self.population_male = population_male\n", - " self.population_female = population_female\n", - " return\n", - " else: \n", - " return population\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " endpointComunas = 'http://192.168.2.223:5006/getComunas'\n", - " endpointRegiones = 'http://192.168.2.223:5006/getStates'\n", - " endpointSS = 'http://192.168.2.223:5006/getHealthServices'\n", - " \n", - " county = pd.read_json(endpointComunas)\n", - " #regiones = pd.read_json(endpointRegiones)\n", - " #ServicioSalud = pd.read_json(endpointSS)\n", - "\n", - " if type(tstate) == list:\n", - " population = 0\n", - " population_male = 0\n", - " population_female = 0\n", - " for i in tstate:\n", - " if len(i)==2: \n", - " population += int(county.loc[county['state'] == int(i)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['state'] == int(i)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['state'] == int(i)]['female_pop'].sum())\n", - " if len(i)>2:\n", - " population += int(county.loc[county['county'] == int(i)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['county'] == int(i)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['county'] == int(i)]['female_pop'].sum()) \n", - " else:\n", - " if len(tstate)==2:\n", - " population = int(county.loc[county['state'] == int(tstate)]['total_pop'].sum())\n", - " population_male = int(county.loc[county['state'] == int(tstate)]['male_pop'].sum())\n", - " population_female = int(county.loc[county['state'] == int(tstate)]['female_pop'].sum())\n", - " if len(tstate)>2:\n", - " population = int(county.loc[county['county'] == int(tstate)]['total_pop'].sum())\n", - " population_male += int(county.loc[county['county'] == int(tstate)]['male_pop'].sum())\n", - " population_female += int(county.loc[county['county'] == int(tstate)]['female_pop'].sum()) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population_male" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " endpointComunas = 'http://192.168.2.223:5006/getComunas'\n", - " endpointRegiones = 'http://192.168.2.223:5006/getStates'\n", - " endpointSS = 'http://192.168.2.223:5006/getHealthServices'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "county = pd.read_json(endpointComunas)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "county" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Subreporte" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "initdate = datetime(2020,5,15)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "tstate = ['10','12']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'http://192.168.2.223:5006/getActiveCasesUnderreportByState'" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "subreport = pd.DataFrame(requests.get(endpoint).json()['data']['13'])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "subreport = pd.DataFrame(requests.get(endpoint).json()['data'])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10 [0.42098168, 0.41884813, 0.4165822, 0.41417882...\n", - "12 [0.39730588, 0.39680141, 0.39623079, 0.3955902...\n", - "Name: estimate, dtype: object" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subreport[tstate].loc['estimate']" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subreport_dates = subreport[tstate].loc['dates']\n", - "\n", - "len(subreport_dates)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 4, 10, 0, 0)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datetime.strptime(subreport_dates.iloc[0][0][:10],'%Y-%m-%d')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "subreport_dates = subreport[tstate].loc['dates']\n", - "subreport_dates = [[datetime.strptime(subreport_dates.iloc[j][i][:10],'%Y-%m-%d') for i in range(len(subreport_dates.iloc[j]))] for j in range(len(subreport_dates))]\n", - "#subreport_dates.loc[np.array(subreport_dates.iloc[0])>initdate]" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[datetime.datetime(2020, 4, 10, 0, 0),\n", - " datetime.datetime(2020, 4, 11, 0, 0),\n", - " datetime.datetime(2020, 4, 12, 0, 0),\n", - " datetime.datetime(2020, 4, 13, 0, 0),\n", - " datetime.datetime(2020, 4, 14, 0, 0),\n", - " datetime.datetime(2020, 4, 15, 0, 0),\n", - " datetime.datetime(2020, 4, 16, 0, 0),\n", - " datetime.datetime(2020, 4, 17, 0, 0),\n", - " datetime.datetime(2020, 4, 18, 0, 0),\n", - " datetime.datetime(2020, 4, 19, 0, 0),\n", - " datetime.datetime(2020, 4, 20, 0, 0),\n", - " datetime.datetime(2020, 4, 21, 0, 0),\n", - " datetime.datetime(2020, 4, 22, 0, 0),\n", - " datetime.datetime(2020, 4, 23, 0, 0),\n", - " datetime.datetime(2020, 4, 24, 0, 0),\n", - " datetime.datetime(2020, 4, 25, 0, 0),\n", - " datetime.datetime(2020, 4, 26, 0, 0),\n", - " datetime.datetime(2020, 4, 27, 0, 0),\n", - " datetime.datetime(2020, 4, 28, 0, 0),\n", - " datetime.datetime(2020, 4, 29, 0, 0),\n", - " datetime.datetime(2020, 4, 30, 0, 0),\n", - " datetime.datetime(2020, 5, 1, 0, 0),\n", - " datetime.datetime(2020, 5, 2, 0, 0),\n", - " datetime.datetime(2020, 5, 3, 0, 0),\n", - " datetime.datetime(2020, 5, 4, 0, 0),\n", - " datetime.datetime(2020, 5, 5, 0, 0),\n", - " datetime.datetime(2020, 5, 6, 0, 0),\n", - " datetime.datetime(2020, 5, 7, 0, 0),\n", - " datetime.datetime(2020, 5, 8, 0, 0),\n", - " datetime.datetime(2020, 5, 9, 0, 0),\n", - " datetime.datetime(2020, 5, 10, 0, 0),\n", - " datetime.datetime(2020, 5, 11, 0, 0),\n", - " datetime.datetime(2020, 5, 12, 0, 0),\n", - " datetime.datetime(2020, 5, 13, 0, 0),\n", - " datetime.datetime(2020, 5, 14, 0, 0),\n", - " datetime.datetime(2020, 5, 15, 0, 0),\n", - " datetime.datetime(2020, 5, 16, 0, 0),\n", - " datetime.datetime(2020, 5, 17, 0, 0),\n", - " datetime.datetime(2020, 5, 18, 0, 0),\n", - " datetime.datetime(2020, 5, 19, 0, 0),\n", - " datetime.datetime(2020, 5, 20, 0, 0),\n", - " datetime.datetime(2020, 5, 21, 0, 0),\n", - " datetime.datetime(2020, 5, 22, 0, 0),\n", - " datetime.datetime(2020, 5, 23, 0, 0),\n", - " datetime.datetime(2020, 5, 24, 0, 0),\n", - " datetime.datetime(2020, 5, 25, 0, 0),\n", - " datetime.datetime(2020, 5, 26, 0, 0),\n", - " datetime.datetime(2020, 5, 27, 0, 0),\n", - " datetime.datetime(2020, 5, 28, 0, 0),\n", - " datetime.datetime(2020, 5, 29, 0, 0),\n", - " datetime.datetime(2020, 5, 30, 0, 0),\n", - " datetime.datetime(2020, 5, 31, 0, 0),\n", - " datetime.datetime(2020, 6, 1, 0, 0),\n", - " datetime.datetime(2020, 6, 2, 0, 0),\n", - " datetime.datetime(2020, 6, 3, 0, 0),\n", - " datetime.datetime(2020, 6, 4, 0, 0),\n", - " datetime.datetime(2020, 6, 5, 0, 0),\n", - 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" datetime.datetime(2020, 6, 28, 0, 0),\n", - " datetime.datetime(2020, 6, 29, 0, 0),\n", - " datetime.datetime(2020, 6, 30, 0, 0),\n", - " datetime.datetime(2020, 7, 1, 0, 0),\n", - " datetime.datetime(2020, 7, 2, 0, 0),\n", - " datetime.datetime(2020, 7, 3, 0, 0),\n", - " datetime.datetime(2020, 7, 4, 0, 0),\n", - " datetime.datetime(2020, 7, 5, 0, 0),\n", - " datetime.datetime(2020, 7, 6, 0, 0),\n", - " datetime.datetime(2020, 7, 7, 0, 0),\n", - " datetime.datetime(2020, 7, 8, 0, 0),\n", - " datetime.datetime(2020, 7, 9, 0, 0),\n", - " datetime.datetime(2020, 7, 10, 0, 0),\n", - " datetime.datetime(2020, 7, 11, 0, 0),\n", - " datetime.datetime(2020, 7, 12, 0, 0),\n", - " datetime.datetime(2020, 7, 13, 0, 0),\n", - " datetime.datetime(2020, 7, 14, 0, 0),\n", - " datetime.datetime(2020, 7, 15, 0, 0),\n", - " datetime.datetime(2020, 7, 16, 0, 0),\n", - " datetime.datetime(2020, 7, 17, 0, 0),\n", - " datetime.datetime(2020, 7, 18, 0, 0),\n", - " datetime.datetime(2020, 7, 19, 0, 0),\n", - " datetime.datetime(2020, 7, 20, 0, 0),\n", - " datetime.datetime(2020, 7, 21, 0, 0),\n", - " datetime.datetime(2020, 7, 22, 0, 0),\n", - " datetime.datetime(2020, 7, 23, 0, 0),\n", - " datetime.datetime(2020, 7, 24, 0, 0),\n", - " datetime.datetime(2020, 7, 25, 0, 0),\n", - " datetime.datetime(2020, 7, 26, 0, 0),\n", - " datetime.datetime(2020, 7, 27, 0, 0),\n", - " datetime.datetime(2020, 7, 28, 0, 0),\n", - " datetime.datetime(2020, 7, 29, 0, 0),\n", - " datetime.datetime(2020, 7, 30, 0, 0),\n", - " datetime.datetime(2020, 7, 31, 0, 0),\n", - " datetime.datetime(2020, 8, 1, 0, 0),\n", - " datetime.datetime(2020, 8, 2, 0, 0),\n", - " datetime.datetime(2020, 8, 3, 0, 0),\n", - " datetime.datetime(2020, 8, 4, 0, 0),\n", - " datetime.datetime(2020, 8, 5, 0, 0),\n", - " datetime.datetime(2020, 8, 6, 0, 0)],\n", - " [datetime.datetime(2020, 4, 7, 0, 0),\n", - " datetime.datetime(2020, 4, 8, 0, 0),\n", - " datetime.datetime(2020, 4, 9, 0, 0),\n", - " datetime.datetime(2020, 4, 10, 0, 0),\n", - " datetime.datetime(2020, 4, 11, 0, 0),\n", - " datetime.datetime(2020, 4, 12, 0, 0),\n", - " datetime.datetime(2020, 4, 13, 0, 0),\n", - " datetime.datetime(2020, 4, 14, 0, 0),\n", - " datetime.datetime(2020, 4, 15, 0, 0),\n", - " datetime.datetime(2020, 4, 16, 0, 0),\n", - " datetime.datetime(2020, 4, 17, 0, 0),\n", - " datetime.datetime(2020, 4, 18, 0, 0),\n", - " datetime.datetime(2020, 4, 19, 0, 0),\n", - " datetime.datetime(2020, 4, 20, 0, 0),\n", - " datetime.datetime(2020, 4, 21, 0, 0),\n", - " datetime.datetime(2020, 4, 22, 0, 0),\n", - " datetime.datetime(2020, 4, 23, 0, 0),\n", - " datetime.datetime(2020, 4, 24, 0, 0),\n", - " datetime.datetime(2020, 4, 25, 0, 0),\n", - " datetime.datetime(2020, 4, 26, 0, 0),\n", - " datetime.datetime(2020, 4, 27, 0, 0),\n", - " datetime.datetime(2020, 4, 28, 0, 0),\n", - " datetime.datetime(2020, 4, 29, 0, 0),\n", - " datetime.datetime(2020, 4, 30, 0, 0),\n", - " datetime.datetime(2020, 5, 1, 0, 0),\n", - " datetime.datetime(2020, 5, 2, 0, 0),\n", - " datetime.datetime(2020, 5, 3, 0, 0),\n", - " datetime.datetime(2020, 5, 4, 0, 0),\n", - " datetime.datetime(2020, 5, 5, 0, 0),\n", - " datetime.datetime(2020, 5, 6, 0, 0),\n", - " datetime.datetime(2020, 5, 7, 0, 0),\n", - " datetime.datetime(2020, 5, 8, 0, 0),\n", - " datetime.datetime(2020, 5, 9, 0, 0),\n", - " datetime.datetime(2020, 5, 10, 0, 0),\n", - " datetime.datetime(2020, 5, 11, 0, 0),\n", - " datetime.datetime(2020, 5, 12, 0, 0),\n", - " datetime.datetime(2020, 5, 13, 0, 0),\n", - " datetime.datetime(2020, 5, 14, 0, 0),\n", - " datetime.datetime(2020, 5, 15, 0, 0),\n", - " datetime.datetime(2020, 5, 16, 0, 0),\n", - " datetime.datetime(2020, 5, 17, 0, 0),\n", - " datetime.datetime(2020, 5, 18, 0, 0),\n", - " datetime.datetime(2020, 5, 19, 0, 0),\n", - " datetime.datetime(2020, 5, 20, 0, 0),\n", - " datetime.datetime(2020, 5, 21, 0, 0),\n", - " datetime.datetime(2020, 5, 22, 0, 0),\n", - " datetime.datetime(2020, 5, 23, 0, 0),\n", - " datetime.datetime(2020, 5, 24, 0, 0),\n", - " datetime.datetime(2020, 5, 25, 0, 0),\n", - " datetime.datetime(2020, 5, 26, 0, 0),\n", - " datetime.datetime(2020, 5, 27, 0, 0),\n", - " datetime.datetime(2020, 5, 28, 0, 0),\n", - " datetime.datetime(2020, 5, 29, 0, 0),\n", - " datetime.datetime(2020, 5, 30, 0, 0),\n", - " datetime.datetime(2020, 5, 31, 0, 0),\n", - " datetime.datetime(2020, 6, 1, 0, 0),\n", - " datetime.datetime(2020, 6, 2, 0, 0),\n", - " datetime.datetime(2020, 6, 3, 0, 0),\n", - " datetime.datetime(2020, 6, 4, 0, 0),\n", - " datetime.datetime(2020, 6, 5, 0, 0),\n", - " datetime.datetime(2020, 6, 6, 0, 0),\n", - " datetime.datetime(2020, 6, 7, 0, 0),\n", - " datetime.datetime(2020, 6, 8, 0, 0),\n", - " datetime.datetime(2020, 6, 9, 0, 0),\n", - " datetime.datetime(2020, 6, 10, 0, 0),\n", - " datetime.datetime(2020, 6, 11, 0, 0),\n", - " datetime.datetime(2020, 6, 12, 0, 0),\n", - " datetime.datetime(2020, 6, 13, 0, 0),\n", - " datetime.datetime(2020, 6, 14, 0, 0),\n", - " datetime.datetime(2020, 6, 15, 0, 0),\n", - " datetime.datetime(2020, 6, 16, 0, 0),\n", - " datetime.datetime(2020, 6, 17, 0, 0),\n", - " datetime.datetime(2020, 6, 18, 0, 0),\n", - " datetime.datetime(2020, 6, 19, 0, 0),\n", - " datetime.datetime(2020, 6, 20, 0, 0),\n", - " datetime.datetime(2020, 6, 21, 0, 0),\n", - " datetime.datetime(2020, 6, 22, 0, 0),\n", - " datetime.datetime(2020, 6, 23, 0, 0),\n", - " datetime.datetime(2020, 6, 24, 0, 0),\n", - " datetime.datetime(2020, 6, 25, 0, 0),\n", - " datetime.datetime(2020, 6, 26, 0, 0),\n", - " datetime.datetime(2020, 6, 27, 0, 0),\n", - " datetime.datetime(2020, 6, 28, 0, 0),\n", - " datetime.datetime(2020, 6, 29, 0, 0),\n", - " datetime.datetime(2020, 6, 30, 0, 0),\n", - " datetime.datetime(2020, 7, 1, 0, 0),\n", - " datetime.datetime(2020, 7, 2, 0, 0),\n", - " datetime.datetime(2020, 7, 3, 0, 0),\n", - " datetime.datetime(2020, 7, 4, 0, 0),\n", - " datetime.datetime(2020, 7, 5, 0, 0),\n", - " datetime.datetime(2020, 7, 6, 0, 0),\n", - " datetime.datetime(2020, 7, 7, 0, 0),\n", - " datetime.datetime(2020, 7, 8, 0, 0),\n", - " datetime.datetime(2020, 7, 9, 0, 0),\n", - " datetime.datetime(2020, 7, 10, 0, 0),\n", - " datetime.datetime(2020, 7, 11, 0, 0),\n", - " datetime.datetime(2020, 7, 12, 0, 0),\n", - " datetime.datetime(2020, 7, 13, 0, 0),\n", - " datetime.datetime(2020, 7, 14, 0, 0),\n", - " datetime.datetime(2020, 7, 15, 0, 0),\n", - " datetime.datetime(2020, 7, 16, 0, 0),\n", - " datetime.datetime(2020, 7, 17, 0, 0),\n", - " datetime.datetime(2020, 7, 18, 0, 0),\n", - " datetime.datetime(2020, 7, 19, 0, 0),\n", - " datetime.datetime(2020, 7, 20, 0, 0),\n", - " datetime.datetime(2020, 7, 21, 0, 0),\n", - " datetime.datetime(2020, 7, 22, 0, 0),\n", - " datetime.datetime(2020, 7, 23, 0, 0),\n", - " datetime.datetime(2020, 7, 24, 0, 0),\n", - " datetime.datetime(2020, 7, 25, 0, 0),\n", - " datetime.datetime(2020, 7, 26, 0, 0),\n", - " datetime.datetime(2020, 7, 27, 0, 0),\n", - " datetime.datetime(2020, 7, 28, 0, 0),\n", - " datetime.datetime(2020, 7, 29, 0, 0),\n", - " datetime.datetime(2020, 7, 30, 0, 0),\n", - " datetime.datetime(2020, 7, 31, 0, 0),\n", - " datetime.datetime(2020, 8, 1, 0, 0),\n", - " datetime.datetime(2020, 8, 2, 0, 0),\n", - " datetime.datetime(2020, 8, 3, 0, 0),\n", - " datetime.datetime(2020, 8, 4, 0, 0),\n", - " datetime.datetime(2020, 8, 5, 0, 0),\n", - " datetime.datetime(2020, 8, 6, 0, 0)]]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subreport_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Active Infected" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'http://192.168.2.223:5006/getActiveCasesAllComunas'" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.DataFrame(requests.get(endpoint).json()['data'])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - " if type(tstate) == list:\n", - " counties = [i for i in tstate if len(i)>2 ]\n", - " states = [i for i in tstate if len(i)==2 ] \n", - " aux = []\n", - " for i in states:\n", - " aux.append(data.filter(regex='^'+i,axis=1))\n", - " \n", - " aux.append(data[counties])\n", - " Ir = np.array(pd.concat(aux, axis=1).sum(axis=1))\n", - " \n", - " else:\n", - " if len(tstate) == 2:\n", - " Ir = np.array(data.filter(regex='^'+tstate,axis=1).sum(axis=1))\n", - " elif len(tstate) > 2:\n", - " Ir = np.array(data[tstate])\n", - " dates = list(requests.get(endpoint).json()['dates'])" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 339, 268, 251, 253, 201, 178, 144, 166, 202, 230, 216,\n", - " 197, 202, 198, 206, 210, 221, 195, 189, 293, 378, 477,\n", - " 477, 478, 589, 621, 654, 707, 630, 687, 850, 1001, 1153,\n", - " 1302, 1414, 1356, 1279, 1288, 1525, 1571, 1767, 1797, 2261, 1998,\n", - " 2283, 2133, 2382, 2411, 2836])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Ir" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['2020-04-13T04:00:00.000Z',\n", - " '2020-04-15T04:00:00.000Z',\n", - " '2020-04-17T04:00:00.000Z',\n", - " '2020-04-20T04:00:00.000Z',\n", - " '2020-04-24T04:00:00.000Z',\n", - " '2020-04-27T04:00:00.000Z',\n", - " '2020-05-01T04:00:00.000Z',\n", - " '2020-05-04T04:00:00.000Z',\n", - " '2020-05-08T04:00:00.000Z',\n", - " '2020-05-11T04:00:00.000Z',\n", - " '2020-05-15T04:00:00.000Z',\n", - " '2020-05-18T04:00:00.000Z',\n", - " '2020-05-22T04:00:00.000Z',\n", - " '2020-05-25T04:00:00.000Z',\n", - " '2020-05-29T04:00:00.000Z',\n", - " '2020-06-01T04:00:00.000Z',\n", - " '2020-06-05T04:00:00.000Z',\n", - " '2020-06-08T04:00:00.000Z',\n", - " '2020-06-12T04:00:00.000Z',\n", - " '2020-06-15T04:00:00.000Z',\n", - " '2020-06-19T04:00:00.000Z',\n", - " '2020-06-23T04:00:00.000Z',\n", - " '2020-06-28T04:00:00.000Z',\n", - " '2020-07-01T04:00:00.000Z',\n", - " '2020-07-05T04:00:00.000Z',\n", - " '2020-07-10T04:00:00.000Z',\n", - " '2020-07-13T04:00:00.000Z',\n", - " '2020-07-17T04:00:00.000Z',\n", - " '2020-07-20T04:00:00.000Z',\n", - " '2020-07-24T04:00:00.000Z',\n", - " '2020-07-27T04:00:00.000Z',\n", - " '2020-07-31T04:00:00.000Z',\n", - " '2020-08-03T04:00:00.000Z',\n", - " '2020-08-07T04:00:00.000Z',\n", - " '2020-08-10T04:00:00.000Z',\n", - " '2020-08-14T04:00:00.000Z',\n", - " '2020-08-17T04:00:00.000Z',\n", - " '2020-08-21T04:00:00.000Z',\n", - " '2020-08-24T04:00:00.000Z',\n", - " '2020-08-28T04:00:00.000Z',\n", - " '2020-08-31T04:00:00.000Z',\n", - " '2020-09-04T04:00:00.000Z',\n", - " '2020-09-07T03:00:00.000Z',\n", - " '2020-09-11T03:00:00.000Z',\n", - " '2020-09-14T03:00:00.000Z',\n", - " '2020-09-18T03:00:00.000Z',\n", - " '2020-09-21T03:00:00.000Z',\n", - " '2020-09-25T03:00:00.000Z',\n", - " '2020-09-28T03:00:00.000Z']" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subreport" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python38264bit48ae65e862f64697a29185f9fa581b02" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_26_Mayo.ipynb b/DataFit/Chile_26_Mayo.ipynb deleted file mode 100644 index e4ada66..0000000 --- a/DataFit/Chile_26_Mayo.ipynb +++ /dev/null @@ -1,1455 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD6 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events\n", - "\n", - "import json\n", - "from pandas.core.base import DataError\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "import pandas as pd\n", - "import matplotlib\n", - "\n", - "from datetime import date\n", - "\n", - "import copy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2021,3,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def dataretriever(user=None,password=None):\n", - " # Esta funcion devolvera una funcion de request \n", - " def request(endpoint):\n", - " if user:\n", - " aux = requests.get('https://api.cv19gm.org/'+endpoint, auth=HTTPBasicAuth(user, password))\n", - " if aux.status_code == 401:\n", - " raise Exception('Wrong Credentials')\n", - " else:\n", - " print('Logged in successfully')\n", - " else:\n", - " aux = requests.get('http://192.168.2.223:5006/'+endpoint)\n", - " return aux\n", - " return request\n", - "def timeJStoPy(t):\n", - " return datetime.strptime(t[:10],'%Y-%m-%d')\n", - "def timetxttoDatetime(t):\n", - " return datetime.strptime(t,'%Y-%m-%d')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from all regions\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data from Minciencia\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from all regions')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()\n", - "state.name = 'Chile'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "capacidadUCIcovid = 4600\n", - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = capacidadUCIcovid - np.array(state.UCI_use_noncovid)#[4000]*len(state.UCI_use_covid)#state.UCI_capacity\n", - "state.UCI_non_covid = state.UCI_use_noncovid\n", - "state.sochimi_tr = state.UCI_tr#[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1228.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.UCI_non_covid[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4435.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.UCI_capacity[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3026.0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.UCI_use_covid[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2021, 5, 25, 0, 0)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.UCI_dates[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Infectados Diarios y Acumulados" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint ='https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/TotalesPorRegion.csv'\n", - "producto3 = pd.read_csv(endpoint)\n", - "I_ac_confirmado = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos acumulados')]\n", - "I_antigeno_ac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos confirmados por antigeno')]\n", - "I_probables_ac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos probables acumulados')]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "confirmado_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos acumulados')].iloc[:,2:].sum()\n", - "antigeno_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos confirmados por antigeno')].iloc[:,2:].sum()\n", - "probables_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos probables acumulados')].iloc[:,2:].sum()\n", - "\n", - "I_ac_nac = confirmado_nac+antigeno_nac+probables_nac\n", - "I_act_nac_roll = I_ac_nac.diff(periods=11).rolling(7).mean().dropna()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "I_act_nac_roll.plot(label='confirmados+antigenos+probables')\n", - "confirmado_nac.diff(periods=11).rolling(7).mean().dropna().plot(label='confirmados')\n", - "plt.title('Activos')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Recovered\n", - "R = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos confirmados recuperados')].iloc[0][3:]\n", - "R.index = pd.to_datetime(R.index, format='%Y-%m-%d')\n", - "R = R.loc[R.index>=initdate]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "initday = (initdate-pd.to_datetime(I_ac_confirmado.iloc[0].index[2], format='%Y-%m-%d')).days\n", - "#I_ac = I_ac_confirmado.iloc[0][initday:] + I_antigeno_ac.iloc[0][initday:] + I_probables_ac.iloc[0][initday:]\n", - "I_ac = I_ac_confirmado.iloc[0][3:] + I_antigeno_ac.iloc[0][3:] + I_probables_ac.iloc[0][3:]\n", - "I_ac.index = pd.to_datetime(I_ac.index, format='%Y-%m-%d')\n", - "\n", - "I_d = I_ac.diff(periods=1)\n", - "I_act = I_ac.diff(periods=11)\n", - "I_d_roll = I_d.rolling(7).mean()\n", - "I_act_roll = I_act.rolling(7).mean()\n", - "I_d = I_d.loc[I_d.index>=initdate]\n", - "I_act = I_act.loc[I_act.index>=initdate]\n", - "I_ac = I_ac.loc[I_ac.index>=initdate]\n", - "\n", - "\n", - "I_d_roll = I_d_roll.loc[I_d_roll.index>=initdate]\n", - "I_act_roll = I_act_roll.loc[I_act_roll.index>=initdate]\n", - "\n", - "state.I_d_r = I_d_roll\n", - "state.I_d_r_dates = I_d_roll.index\n", - "state.I_d_r_tr = [(I_d_roll.index[i]-initdate).days for i in range(len(I_d_roll))]\n", - "\n", - "state.Ir = I_act_roll\n", - "state.Ir_dates = I_act_roll.index\n", - "state.tr = [(I_act_roll.index[i]-initdate).days for i in range(len(I_act_roll))]\n", - "\n", - "state.I_ac_r = I_ac\n", - "state.I_ac_r_dates = I_ac.index\n", - "state.I_ac_r_tr = [(I_ac.index[i]-initdate).days for i in range(len(I_ac))]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "state.R = R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importante:\n", - "valores al 26 de Abril\n", - "Casos activos calculados con probables y antigenos: 38657\n", - "Casos activos confirmados publicados: 15810\n", - "Casos activos calculado solo con los casos acumulados: 28170\n", - "\n", - "Activos_confirmados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos activos confirmados')]\n", - "Activos_desdeacumulados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')].iloc[0][initday:].diff(periods=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "Ias_det = 0.3#0.1\n", - "Imi_det = 1#0.4\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.3#75 #list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2021,4,2),datetime(2021,5,3),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = list(np.array([0.127,0.11])*0.92)\n", - "\n", - "escenarios = [60,80,90,100,110,120]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "\n", - "title = 'Proyección Situación Epidemiológica Nacional'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.05842000000000001" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new[0](0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b,c):\n", - " def aux(t):\n", - " return a(t)+b(t)+c(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "chi = aux" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = -0.1\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,4,2)-initdate).days\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 9\n", - "renewalFactor = -0.33\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,4,11)-initdate).days\n", - "chi1 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = 0.3\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,5,3)-initdate).days\n", - "chi2 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "chi = chisum(chi0,chi1,chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [chi(t) for t in np.arange(0,tsim,0.1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(chiplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.30 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.565 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.025 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.11 # Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 12.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 11.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 11.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 7.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 7.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.7 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 11.0\n", - "pV_D = 0.3 # Transition from Ventilators to Death\n", - "tV_D = 11.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo medio Infeccioso" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias+pE_Imi+pE_Ise+pE_Icr" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.765" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias*tIas_R + pE_Imi*tImi_R + pE_Ise*tIse_Hse + pE_Icr*tIcr_V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "simulation[0].integr_sci(0,1000,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 1.0s remaining: 2.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 1.4s remaining: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 1.4s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 2.6s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 2.6s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,12,31)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2023, 12, 9, 0, 0)" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation[0].dates[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 10\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title('Infectados Activos Detectados',fontsize=fontsize_title)\n", - "\n", - "axs[0,0].scatter(state.Ir_dates,state.Ir,color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Fallecidos Acumulados (PCR+)',fontsize=fontsize_title)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='DP50',color='grey',zorder=10)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title('Nuevos Infectados Diarios Detectados',fontsize=fontsize_title)\n", - "\n", - "\n", - "if False:#sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - "\n", - "#axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='DP5',color='black',zorder=5)\n", - "axs[1,0].scatter(state.I_d_r_dates,I_d_roll,label='DP3 med_mov 7',color='grey',zorder=5)\n", - " \n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,capacidadUCIcovid-np.array(state.UCI_use_noncovid),label='Limite uso COVID',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='Mov: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title('Uso UCI Covid',fontsize=fontsize_title)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='DP58',color='grey',zorder=10)\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "axs[1,1].axhline(y = capacidadUCIcovid, linestyle = 'dashed',linewidth=datelinewidth,color='grey',label='UCI totales')\n", - "\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " \n", - "axs[0,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[0,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "# x axis: Date range\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "\n", - "# x axis: Date interval\n", - "interval = 30\n", - "#axs[0,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[0,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "\n", - "# x axis: Date format\n", - "axs[0,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[0,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "\n", - "# Y axis: Format\n", - "axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Y axis: Range\n", - "#axs[0,0].set_ylim(0,5e4)\n", - "axs[0,1].set_ylim(0,100000)\n", - "#axs[1,0].set_ylim(0,10000)\n", - "#axs[1,1].set_ylim(0,2.5e3)\n", - "#axs[0,0].set_yscale('log')\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "#axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "initdate_table = datetime(2021,5,1)\n", - "enddate_table = datetime(2021,12,31)\n", - "\n", - "init_index = np.where(np.array(dates[0]) == np.array(initdate_table))[0][0]\n", - "end_index = np.where(np.array(dates[0]) == np.array(enddate_table))[0][0] + 1 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.around(np.array([1.1,2.5,8.9])).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_d_dict = [{'Infectados_diarios_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_act_dict = [{'Infectados_activos_mov_'+str(escenarios[i]):np.around(simulation[i].I_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "D_acc_dict = [{'Fallecidos_acumulados_mov_'+str(escenarios[i]):np.around(simulation[i].B[init_index:end_index]).astype(int)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_d_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_act_table = {'fechas':dates[0][init_index:end_index]}\n", - "D_acc_table = {'fechas':dates[0][init_index:end_index]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i}\n", - "for i in I_d_dict: \n", - " I_d_table = {**I_d_table, **i} \n", - "for i in I_act_dict: \n", - " I_act_table = {**I_act_table, **i} \n", - "for i in D_acc_dict:\n", - " D_acc_table = {**D_acc_table, **i}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)\n", - "I_d_df = pd.DataFrame(I_d_table)\n", - "I_act_df = pd.DataFrame(I_act_table)\n", - "D_acc_df = pd.DataFrame(D_acc_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "D_acc_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_may-dic.csv')\n", - "I_d_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_diarios_may-dic.csv')\n", - "I_act_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_activos_may-dic.csv')\n", - "D_acc_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/D_accu_may-dic.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']= initdate)[0][0]\n", - "I_ac = I_ac[index_ac:]\n", - "I_ac_dates = dates_I_d[index_ac:]\n", - "I_ac_tr = [(i-initdate).days for i in I_ac_dates]\n", - "\n", - "index = np.where(np.array(dates_I_d) >= initdate-timedelta(days=7))[0][0]\n", - "dates_I_d = dates_I_d[index:]\n", - "I_d = I_d[index:]\n", - "weekdays = [date.weekday(dates_I_d[i]) for i in range(len(dates_I_d))]\n", - "I = pd.DataFrame({'dates':dates_I_d,'I':I_d,'weekdays':weekdays})\n", - "I_week = I[(I['weekdays']>2) | (I['weekdays']==0)]\n", - "I_week_ra = I_week['I'].rolling(5).mean()[I_week['dates']>=initdate]\n", - "I_week_ra_dates = I_week['dates'][I_week['dates']>=initdate]\n", - "I_week_ra_tr = [(i-initdate).days for i in I_week_ra_dates]\n", - "index_date = np.where(np.array(dates_I_d) >= initdate)[0][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters Fiteo UCI" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.35#list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2020,12,21),datetime(2021,1,8),datetime(2021,2,25),datetime(2021,3,20),datetime(2021,4,11),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.0099,0.0126,0.0209,0.03,0.041]\n", - "\n", - "escenarios = [0.01,0.03,0.035,0.04]\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "\n", - "title = 'Ajuste por uso UCI'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters Fiteo EPI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.34#6#list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.population*0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2020,12,26),datetime(2021,1,3),datetime(2021,1,28),datetime(2021,2,20),datetime(2021,2,24),datetime(2021,3,1),datetime(2021,3,7),datetime(2021,3,13),datetime(2021,3,23),datetime(2021,4,5),datetime(2021,4,13),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.011,0.015,0.021,0.017,0.021,0.024,0.027,0.03,0.0325,0.0348,0.03745]\n", - "\n", - "escenarios = [0.03,0.035,0.04,0.045,0.05]\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "#alpha_var = escenarios\n", - "#alpha_new = [Events.Events(values=mobility +[i],days=mob_days) for i in escenarios]\n", - "\n", - "title = 'Ajuste por infectados diarios informe EPI'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters Fiteo Informe Diario" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state_InfDiario = copy.deepcopy(state)\n", - "state_InfDiario.I_d_r = list(I_week_ra)\n", - "state_InfDiario.I_d_r_dates = list(I_week_ra_dates)\n", - "state_InfDiario.I_d_r_tr = I_week_ra_tr\n", - "\n", - "state_InfDiario.I_ac_r = I_ac\n", - "state_InfDiario.I_ac_r_dates = I_ac_dates\n", - "state_InfDiario.I_ac_r_tr = I_ac_tr\n", - "\n", - "state = state_InfDiario" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.28#6#list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.population*0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2020,12,15),datetime(2020,12,31),datetime(2021,1,7),datetime(2021,1,28),datetime(2021,2,24),datetime(2021,3,1),datetime(2021,3,7),datetime(2021,3,13),datetime(2021,3,18),datetime(2021,3,24),datetime(2021,4,15),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.012,0.015,0.021,0.026,0.0225,0.024,0.027,0.03,0.0325,0.037,0.039]\n", - "\n", - "escenarios = [0.03,0.035,0.04,0.045,0.05]\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "#alpha_var = escenarios\n", - "#alpha_new = [Events.Events(values=mobility +[i],days=mob_days) for i in escenarios]\n", - "title = 'Ajuste por infectados diarios informe diario'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "\n", - "chi = aux" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.so" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.35" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mu" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 3.0s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 3.1s remaining: 6.3s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 3.2s remaining: 3.2s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 3.2s remaining: 1.6s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 3.7s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 3.7s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,7,1)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 12\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title('Infectados Acumulados',fontsize=fontsize_title)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].set_ylim(0,3.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[0,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " \n", - "\n", - "#axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "#axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "#axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "if sim_InfEPI:\n", - " axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Informe EPI',zorder=10)\n", - "\n", - "if sim_InfDiario:\n", - " # cambiar\n", - " axs[0,0].scatter(I_ac_dates,I_ac,color='grey',label='Informe Diario',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc='upper left')\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Fallecidos Acumulados (PCR+)',fontsize=fontsize_title)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].set_ylim(0,60000)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[0,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc='upper left')\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=fontsize_title)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "if sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - " \n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[1,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,4500-np.array(state.UCI_use_noncovid),label='Limite uso COVID',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='Mov: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title('Uso UCI',fontsize=fontsize_title)\n", - "axs[1,1].set_xlim(datetime(2021,3,15),datetime(2021,6,1))\n", - "axs[1,1].set_ylim(0,4e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "axs[1,1].axvline(x = datetime(2021,4,25), linestyle = 'dotted',color = 'grey')\n", - "axs[1,1].axvline(x = datetime(2021,4,30), linestyle = 'dotted',color = 'grey')\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(alpha_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop\n" - ] - } - ], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'Movilidad: '+str(escenarios[i])+'%'\n", - "dates[i],simulation[i].V\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i} " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux = vmi_df.iloc[109:309].to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].V)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
fechasId_mov_20Id_mov_40Id_mov_60Id_mov_80Id_mov_90Id_mov_100
1652021-05-101927.03972.06173.08562.09835.011164.0
1662021-05-111925.03967.06166.08554.09827.011157.0
1672021-05-121923.03962.06158.08546.09819.011150.0
1682021-05-131921.03957.06151.08538.09811.011142.0
1692021-05-141919.03953.06145.08530.09803.011133.0
1702021-05-151917.03949.06138.08521.09794.011124.0
1712021-05-161916.03945.06131.08513.09784.011114.0
\n", - "" - ], - "text/plain": [ - " fechas Id_mov_20 Id_mov_40 Id_mov_60 Id_mov_80 Id_mov_90 \\\n", - "165 2021-05-10 1927.0 3972.0 6173.0 8562.0 9835.0 \n", - "166 2021-05-11 1925.0 3967.0 6166.0 8554.0 9827.0 \n", - "167 2021-05-12 1923.0 3962.0 6158.0 8546.0 9819.0 \n", - "168 2021-05-13 1921.0 3957.0 6151.0 8538.0 9811.0 \n", - "169 2021-05-14 1919.0 3953.0 6145.0 8530.0 9803.0 \n", - "170 2021-05-15 1917.0 3949.0 6138.0 8521.0 9794.0 \n", - "171 2021-05-16 1916.0 3945.0 6131.0 8513.0 9784.0 \n", - "\n", - " Id_mov_100 \n", - "165 11164.0 \n", - "166 11157.0 \n", - "167 11150.0 \n", - "168 11142.0 \n", - "169 11133.0 \n", - "170 11124.0 \n", - "171 11114.0 " - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Id_10may" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "Id_average = [np.mean(Id_10may[i]) for i in Id_10may.keys()[1:]]" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1921., 3958., 6152., 8538., 9810., 11141.])" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.around(Id_average)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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fechasId_mov_20Id_mov_40Id_mov_60Id_mov_80Id_mov_90Id_mov_100
1652021-05-106532.013465.020926.029025.033338.037844.0
1662021-05-116524.013448.020900.028997.033313.037822.0
1672021-05-126517.013431.020876.028970.033286.037797.0
1682021-05-136511.013415.020852.028942.033258.037770.0
1692021-05-146505.013400.020829.028914.033229.037740.0
1702021-05-156499.013386.020806.028885.033199.037709.0
1712021-05-166493.013372.020783.028856.033168.037674.0
\n", - "
" - ], - "text/plain": [ - " fechas Id_mov_20 Id_mov_40 Id_mov_60 Id_mov_80 Id_mov_90 \\\n", - "165 2021-05-10 6532.0 13465.0 20926.0 29025.0 33338.0 \n", - "166 2021-05-11 6524.0 13448.0 20900.0 28997.0 33313.0 \n", - "167 2021-05-12 6517.0 13431.0 20876.0 28970.0 33286.0 \n", - "168 2021-05-13 6511.0 13415.0 20852.0 28942.0 33258.0 \n", - "169 2021-05-14 6505.0 13400.0 20829.0 28914.0 33229.0 \n", - "170 2021-05-15 6499.0 13386.0 20806.0 28885.0 33199.0 \n", - "171 2021-05-16 6493.0 13372.0 20783.0 28856.0 33168.0 \n", - "\n", - " Id_mov_100 \n", - "165 37844.0 \n", - "166 37822.0 \n", - "167 37797.0 \n", - "168 37770.0 \n", - "169 37740.0 \n", - "170 37709.0 \n", - "171 37674.0 " - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Id_10may" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_Abril-26-dp3_Infected.ipynb b/DataFit/Chile_Abril-26-dp3_Infected.ipynb deleted file mode 100644 index 2b0cd02..0000000 --- a/DataFit/Chile_Abril-26-dp3_Infected.ipynb +++ /dev/null @@ -1,1309 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD6 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events\n", - "\n", - "import json\n", - "from pandas.core.base import DataError\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "import pandas as pd\n", - "import matplotlib\n", - "\n", - "from datetime import date\n", - "\n", - "import copy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2021,3,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def dataretriever(user=None,password=None):\n", - " # Esta funcion devolvera una funcion de request \n", - " def request(endpoint):\n", - " if user:\n", - " aux = requests.get('https://api.cv19gm.org/'+endpoint, auth=HTTPBasicAuth(user, password))\n", - " if aux.status_code == 401:\n", - " raise Exception('Wrong Credentials')\n", - " else:\n", - " print('Logged in successfully')\n", - " else:\n", - " aux = requests.get('http://192.168.2.223:5006/'+endpoint)\n", - " return aux\n", - " return request\n", - "def timeJStoPy(t):\n", - " return datetime.strptime(t[:10],'%Y-%m-%d')\n", - "def timetxttoDatetime(t):\n", - " return datetime.strptime(t,'%Y-%m-%d')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from all regions\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from all regions')\n", - "#tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "tstate = '13'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()\n", - "state.name = 'Chile'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "capacidadUCIcovid = 2600\n", - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = capacidadUCIcovid - np.array(state.UCI_use_noncovid)#[4000]*len(state.UCI_use_covid)#state.UCI_capacity\n", - "state.UCI_non_covid = state.UCI_use_noncovid\n", - "state.sochimi_tr = state.UCI_tr#[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Infectados Diarios y Acumulados" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint ='https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/TotalesPorRegion.csv'\n", - "producto3 = pd.read_csv(endpoint)\n", - "I_ac_confirmado = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')]\n", - "I_antigeno_ac = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados por antigeno')]\n", - "I_probables_ac = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos probables acumulados')]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "confirmado_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos acumulados')].iloc[:,2:].sum()\n", - "antigeno_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos confirmados por antigeno')].iloc[:,2:].sum()\n", - "probables_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos probables acumulados')].iloc[:,2:].sum()\n", - "\n", - "I_ac_nac = confirmado_nac+antigeno_nac+probables_nac\n", - "I_act_nac_roll = I_ac_nac.diff(periods=11).rolling(7).mean().dropna()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "I_act_nac_roll.plot(label='confirmados+antigenos+probables')\n", - "confirmado_nac.diff(periods=11).rolling(7).mean().dropna().plot(label='confirmados')\n", - "plt.title('Activos')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "R = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados recuperados')].iloc[0][3:]\n", - "R.index = pd.to_datetime(R.index, format='%Y-%m-%d')\n", - "R = R.loc[R.index>=initdate]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "initday = (initdate-pd.to_datetime(I_ac_confirmado.iloc[0].index[2], format='%Y-%m-%d')).days\n", - "#I_ac = I_ac_confirmado.iloc[0][initday:] + I_antigeno_ac.iloc[0][initday:] + I_probables_ac.iloc[0][initday:]\n", - "I_ac = I_ac_confirmado.iloc[0][3:] + I_antigeno_ac.iloc[0][3:] + I_probables_ac.iloc[0][3:]\n", - "I_ac.index = pd.to_datetime(I_ac.index, format='%Y-%m-%d')\n", - "\n", - "I_d = I_ac.diff(periods=1)\n", - "I_act = I_ac.diff(periods=11)\n", - "I_d_roll = I_d.rolling(7).mean()\n", - "I_act_roll = I_act.rolling(7).mean()\n", - "I_d = I_d.loc[I_d.index>=initdate]\n", - "I_act = I_act.loc[I_act.index>=initdate]\n", - "I_ac = I_ac.loc[I_ac.index>=initdate]\n", - "\n", - "\n", - "I_d_roll = I_d_roll.loc[I_d_roll.index>=initdate]\n", - "I_act_roll = I_act_roll.loc[I_act_roll.index>=initdate]\n", - "\n", - "state.I_d_r = I_d_roll\n", - "state.I_d_r_dates = I_d_roll.index\n", - "state.I_d_r_tr = [(I_d_roll.index[i]-initdate).days for i in range(len(I_d_roll))]\n", - "\n", - "state.Ir = I_act_roll\n", - "state.Ir_dates = I_act_roll.index\n", - "state.tr = [(I_act_roll.index[i]-initdate).days for i in range(len(I_act_roll))]\n", - "\n", - "state.I_ac_r = I_ac\n", - "state.I_ac_r_dates = I_ac.index\n", - "state.I_ac_r_tr = [(I_ac.index[i]-initdate).days for i in range(len(I_ac))]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "state.R = R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importante:\n", - "valores al 26 de Abril\n", - "Casos activos calculados con probables y antigenos: 38657\n", - "Casos activos confirmados publicados: 15810\n", - "Casos activos calculado solo con los casos acumulados: 28170\n", - "\n", - "Activos_confirmados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos activos confirmados')]\n", - "Activos_desdeacumulados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')].iloc[0][initday:].diff(periods=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1#0.4\n", - "Ias_det = 1#0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.315#5 #list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2021,3,27),datetime(2021,4,16),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.019,0.0215]\n", - "\n", - "escenarios = [20,40,60,74]#,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "\n", - "title = 'Ajuste por uso UCI'" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0095" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new[0](0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "chi = aux" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.30 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.05 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.10 # Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 12.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 11.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 11.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 11.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 11.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.7 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 13.0\n", - "pV_D = 0.3 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo medio Infeccioso" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias+pE_Imi+pE_Ise+pE_Icr" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.85" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias*tIas_R + pE_Imi*tImi_R + pE_Ise*tIse_Hse + pE_Icr*tIcr_V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "simulation[0].integr_sci(0,1000,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 4 | elapsed: 1.4s remaining: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 4 | elapsed: 1.5s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 4 | elapsed: 1.5s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,7,1)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2023, 12, 9, 0, 0)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation[0].dates[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 10\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title('Infectados Activos',fontsize=fontsize_title)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].set_ylim(0,5e4)\n", - "\n", - "\n", - "axs[0,0].scatter(state.Ir_dates,state.Ir,color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Fallecidos Acumulados (PCR+)',fontsize=fontsize_title)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].set_ylim(0,30000)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='DP50',color='grey',zorder=10)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=fontsize_title)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "if False:#sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - "\n", - "#axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='DP5',color='black',zorder=5)\n", - "axs[1,0].scatter(state.I_d_r_dates,I_d_roll,label='DP3 med_mov 7',color='grey',zorder=5)\n", - " \n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,capacidadUCIcovid-np.array(state.UCI_use_noncovid),label='Limite uso COVID',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='Mov: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title('Uso UCI',fontsize=fontsize_title)\n", - "axs[1,1].set_xlim(datetime(2021,3,15),enddate)\n", - "axs[1,1].set_ylim(0,2.5e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='DP58',color='grey',zorder=10)\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " \n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# x axis: Date format\n", - "axs[0,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m-%y'))\n", - "axs[0,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m-%y'))\n", - "axs[1,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m-%y'))\n", - "axs[1,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m-%y'))\n", - "\n", - "# Y axis\n", - "axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "#axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop = []\n", - "for i in range(len(simulation)):\n", - " pop.append([simulation[i].S[t] + simulation[i].E[t] + simulation[i].Ias[t] + simulation[i].Imi[t] + simulation[i].Ise[t] + simulation[i].Icr[t] + simulation[i].D[t] + simulation[i].B[t] + simulation[i].R[t] + simulation[i].Hse[t] + simulation[i].Hout[t] + simulation[i].V[t] for t in range(len(simulation[i].S))])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(pop)):\n", - " plt.plot(pop)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation[0].eta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = -1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation[i].t,simulation[i].S,label='S')\n", - "plt.plot(simulation[i].t,simulation[i].E,label='E')\n", - "plt.plot(simulation[i].t,simulation[i].I,label='I')\n", - "plt.plot(simulation[i].t,simulation[i].R,label='R')\n", - "#plt.plot(simulation[i].t,simulation[i].B,label='B')\n", - "#plt.plot(simulation[i].t,simulation[i].H,label='H')\n", - "#plt.plot(simulation[i].t,simulation[i].V,label='V')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'Movilidad: '+str(escenarios[i])+'%'\n", - "dates[i],simulation[i].V\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i} " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux = vmi_df.iloc[109:309].to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].V)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "I_act_nac_roll.plot(label='confirmados+antigenos+probables')\n", - "confirmado_nac.diff(periods=11).rolling(7).mean().dropna().plot(label='confirmados')\n", - "plt.title('Activos')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint ='https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/TotalesPorRegion.csv'\n", - "producto3 = pd.read_csv(endpoint)\n", - "I_ac_confirmado = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')]\n", - "I_antigeno_ac = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados por antigeno')]\n", - "I_probables_ac = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos probables acumulados')]" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [], - "source": [ - "initday = (initdate-pd.to_datetime(I_ac_confirmado.iloc[0].index[2], format='%Y-%m-%d')).days\n", - "#I_ac = I_ac_confirmado.iloc[0][initday:] + I_antigeno_ac.iloc[0][initday:] + I_probables_ac.iloc[0][initday:]\n", - "I_ac = I_ac_confirmado.iloc[0][3:] + I_antigeno_ac.iloc[0][3:] + I_probables_ac.iloc[0][3:]\n", - "I_ac.index = pd.to_datetime(I_ac.index, format='%Y-%m-%d')\n", - "I_d = I_ac.diff(periods=1)\n", - "I_act = I_ac.diff(periods=11)\n", - "I_d_roll = I_d.rolling(7).mean()\n", - "I_act_roll = I_act.rolling(7).mean()\n", - "I_d = I_d.loc[I_d.index>=initdate]\n", - "I_act = I_act.loc[I_act.index>=initdate]\n", - "I_ac = I_ac.loc[I_ac.index>=initdate]\n", - "\n", - "I_d_roll = I_d_roll.loc[I_d_roll.index>=initdate]\n", - "I_act_roll = I_act_roll.loc[I_act_roll.index>=initdate]\n", - "\n", - "state.I_d_r = I_d_roll\n", - "state.I_d_r_dates = I_d_roll.index\n", - "state.I_d_r_tr = [(I_d_roll.index[i]-initdate).days for i in range(len(I_d_roll))]\n", - "\n", - "state.Ir = I_act_roll\n", - "state.Ir_dates = I_act_roll.index\n", - "state.tr = [(I_act_roll.index[i]-initdate).days for i in range(len(I_act_roll))]\n", - "\n", - "state.I_ac_r = I_ac\n", - "state.I_ac_r_dates = I_ac.index\n", - "state.I_ac_r_tr = [(I_ac.index[i]-initdate).days for i in range(len(I_ac))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importante:\n", - "valores al 26 de Abril\n", - "Casos activos calculados con probables y antigenos: 38657\n", - "Casos activos confirmados publicados: 15810\n", - "Casos activos calculado solo con los casos acumulados: 28170\n", - "\n", - "Activos_confirmados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos activos confirmados')]\n", - "Activos_desdeacumulados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')].iloc[0][initday:].diff(periods=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1#0.4\n", - "Ias_det = 1#0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.325#5 #list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2021,4,18),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.018]\n", - "\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "\n", - "title = 'Ajuste por uso UCI'" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.009" - ] - }, - "execution_count": 206, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new[0](0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "\n", - "chi = aux" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.30 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.05 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.10 # Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 12.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 11.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 11.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 11.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 11.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.7 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 14.0\n", - "pV_D = 0.3 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo medio Infeccioso" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias+pE_Imi+pE_Ise+pE_Icr" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.85" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias*tIas_R + pE_Imi*tImi_R + pE_Ise*tIse_Hse + pE_Icr*tIcr_V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 1.2s remaining: 2.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 1.4s remaining: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 2.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 3.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 3.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,7,1)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 12\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "#for i in range(len(simulation)):\n", - "# if type(mu) == list:\n", - "# axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "# if type(alpha_new) == list:\n", - "# axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "#axs[0,0].set_title('Infectados Acumulados',fontsize=fontsize_title)\n", - "#axs[0,0].set_xlim(initdate,enddate)\n", - "#axs[0,0].set_ylim(0,3.5e6)\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title('Infectados Activos',fontsize=fontsize_title)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].set_ylim(0,5e4)\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[0,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " \n", - "\n", - "#axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "#axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "#axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "#if sim_InfEPI:\n", - "# axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Informe EPI',zorder=10)\n", - "\n", - "#if sim_InfDiario:\n", - " # cambiar\n", - "# axs[0,0].scatter(I_ac_dates,I_ac,color='grey',label='Informe Diario',zorder=10)\n", - "\n", - "axs[0,0].scatter(state.Ir_dates,state.Ir,color='grey',label='Data',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc='upper left')\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Fallecidos Acumulados (PCR+)',fontsize=fontsize_title)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].set_ylim(0,30000)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[0,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc='upper left')\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=fontsize_title)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "if False:#sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - "\n", - "#axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='DP5',color='black',zorder=5)\n", - "axs[1,0].scatter(state.I_d_r_dates,I_d_roll,label='DP5 roll_av 7',color='red',zorder=5)\n", - " \n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[1,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,capacidadUCIcovid-np.array(state.UCI_use_noncovid),label='Limite uso COVID',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='Mov: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title('Uso UCI',fontsize=fontsize_title)\n", - "axs[1,1].set_xlim(datetime(2021,3,15),datetime(2021,6,1))\n", - "axs[1,1].set_ylim(0,2.5e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "axs[1,1].axvline(x = datetime(2021,4,25), linestyle = 'dotted',color = 'grey')\n", - "axs[1,1].axvline(x = datetime(2021,4,30), linestyle = 'dotted',color = 'grey')\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "#axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop\n" - ] - } - ], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop\n" - ] - } - ], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "list indices must be integers or slices, not datetime.datetime", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'Movilidad: '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mescenarios\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'%'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mV_cap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'dashed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'black'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not datetime.datetime" - ] - } - ], - "source": [ - "'Movilidad: '+str(escenarios[i])+'%'\n", - "dates[i],simulation[i].V\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i} " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aux = vmi_df.iloc[109:309].to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].V)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']= initdate)[0][0]\n", - "I_ac = I_ac[index_ac:]\n", - "I_ac_dates = dates_I_d[index_ac:]\n", - "I_ac_tr = [(i-initdate).days for i in I_ac_dates]\n", - "\n", - "index = np.where(np.array(dates_I_d) >= initdate-timedelta(days=7))[0][0]\n", - "dates_I_d = dates_I_d[index:]\n", - "I_d = I_d[index:]\n", - "weekdays = [date.weekday(dates_I_d[i]) for i in range(len(dates_I_d))]\n", - "I = pd.DataFrame({'dates':dates_I_d,'I':I_d,'weekdays':weekdays})\n", - "I_week = I[(I['weekdays']>2) | (I['weekdays']==0)]\n", - "I_week_ra = I_week['I'].rolling(5).mean()[I_week['dates']>=initdate]\n", - "I_week_ra_dates = I_week['dates'][I_week['dates']>=initdate]\n", - "I_week_ra_tr = [(i-initdate).days for i in I_week_ra_dates]\n", - "index_date = np.where(np.array(dates_I_d) >= initdate)[0][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters Fiteo UCI" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.35#list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3891662.0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.population*0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2020,12,21),datetime(2021,1,8),datetime(2021,2,25),datetime(2021,3,19),datetime(2021,4,11),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.0099,0.0126,0.0209,0.03,0.04]\n", - "\n", - "escenarios = [0.01,0.03,0.035,0.04]\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters Fiteo EPI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.34#6#list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.population*0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2020,12,26),datetime(2021,1,3),datetime(2021,1,28),datetime(2021,2,20),datetime(2021,2,24),datetime(2021,3,1),datetime(2021,3,7),datetime(2021,3,13),datetime(2021,3,24),datetime(2021,4,4),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.011,0.015,0.021,0.017,0.021,0.024,0.027,0.03,0.0325,0.035]\n", - "\n", - "escenarios = [0.03,0.035,0.04,0.045,0.05]\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "#alpha_var = escenarios\n", - "#alpha_new = [Events.Events(values=mobility +[i],days=mob_days) for i in escenarios]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters Fiteo Informe Diario" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import copy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state_InfDiario = copy.deepcopy(state)\n", - "state_InfDiario.I_d_r = list(I_week_ra)\n", - "state_InfDiario.I_d_r_dates = list(I_week_ra_dates)\n", - "state_InfDiario.I_d_r_tr = I_week_ra_tr\n", - "\n", - "state_InfDiario.I_ac_r = I_ac\n", - "state_InfDiario.I_ac_r_dates = I_ac_dates\n", - "state_InfDiario.I_ac_r_tr = I_ac_tr\n", - "\n", - "state = state_InfDiario" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.28#6#list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.6#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.population*0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2020,12,15),datetime(2020,12,31),datetime(2021,1,7),datetime(2021,1,28),datetime(2021,2,24),datetime(2021,3,1),datetime(2021,3,7),datetime(2021,3,13),datetime(2021,3,18),datetime(2021,3,24),datetime(2021,4,7),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.012,0.015,0.021,0.026,0.0225,0.024,0.027,0.03,0.0325,0.037,0.0393]\n", - "\n", - "escenarios = [0.03,0.035,0.04,0.045,0.05]\n", - "escenarios = [20,40,60,80,90,100]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "#alpha_var = escenarios\n", - "#alpha_new = [Events.Events(values=mobility +[i],days=mob_days) for i in escenarios]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "\n", - "chi = aux" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.35" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mu" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 2.7s remaining: 5.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 2.8s remaining: 2.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 2.8s remaining: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 3.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 3.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,12,31)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 12\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title('Infectados Acumulados',fontsize=fontsize_title)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].set_ylim(0,3.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[0,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " \n", - "\n", - "#axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "#axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "#axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "if sim_InfEPI:\n", - " axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Informe EPI',zorder=10)\n", - "\n", - "if sim_InfDiario:\n", - " # cambiar\n", - " axs[0,0].scatter(I_ac_dates,I_ac,color='grey',label='Informe Diario',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc='upper left')\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Fallecidos Acumulados (PCR+)',fontsize=fontsize_title)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].set_ylim(0,60000)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[0,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc='upper left')\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=fontsize_title)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "if sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - " \n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[1,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title('Uso UCI',fontsize=fontsize_title)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "#axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "#axs[1,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "axs[1,1].plot(state.UCI_dates,4000-np.array(state.UCI_use_noncovid),label='Limite uso COVID',color='black',zorder=10)\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='upper right',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='upper right',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='upper right',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='upper right',fontsize=fontsize_legend)\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(alpha_var)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Arima" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from statsmodels.tsa.arima_model import ARIMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = pd.DataFrame({'data':4000 - np.array(state.UCI_use_noncovid)})\n", - "train = dataset[:80]\n", - "test = dataset[80:]\n", - "model_arima = ARIMA(dataset, order=(0,1,1))\n", - "model_fit = model_arima.fit(disp=0)\n", - "print(model_fit.summary())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fc, se, conf = model_fit.forecast(45,alpha=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fc_series = pd.Series(fc, index=test.index)\n", - "lower_series = pd.Series(conf[:,0], index=test.index)\n", - "upper_series = pd.Series(conf[:,1], index=test.index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(12,5), dpi=100)\n", - "plt.plot(train, label='training')\n", - "plt.plot(test, label='actual')\n", - "plt.plot(fc_series, label='forecast')\n", - "plt.fill_between(lower_series.index, lower_series, upper_series, \n", - " color='k', alpha=.15)\n", - "plt.title('Forecast vs Actuals')\n", - "plt.legend(loc='upper left', fontsize=8)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(dataset)\n", - "plt.ylim(0,4000)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(12,5), dpi=100)\n", - "plt.plot(train, label='training')\n", - "plt.plot(test, label='actual')\n", - "plt.plot(fc_series, label='forecast')\n", - "plt.fill_between(lower_series.index, lower_series, upper_series, \n", - " color='k', alpha=.15)\n", - "plt.title('Forecast vs Actuals')\n", - "plt.legend(loc='upper left', fontsize=8)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_fit.predict()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_fit." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_Abril_v1.ipynb b/DataFit/Chile_Abril_v1.ipynb deleted file mode 100644 index f9dce5f..0000000 --- a/DataFit/Chile_Abril_v1.ipynb +++ /dev/null @@ -1,1802 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events\n", - "\n", - "import json\n", - "from pandas.core.base import DataError\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "import pandas as pd\n", - "import matplotlib\n", - "\n", - "from datetime import date" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,12,3)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def dataretriever(user=None,password=None):\n", - " # Esta funcion devolvera una funcion de request \n", - " def request(endpoint):\n", - " if user:\n", - " aux = requests.get('https://api.cv19gm.org/'+endpoint, auth=HTTPBasicAuth(user, password))\n", - " if aux.status_code == 401:\n", - " raise Exception('Wrong Credentials')\n", - " else:\n", - " print('Logged in successfully')\n", - " else:\n", - " aux = requests.get('http://192.168.2.223:5006/'+endpoint)\n", - " return aux\n", - " return request\n", - "def timeJStoPy(t):\n", - " return datetime.strptime(t[:10],'%Y-%m-%d')\n", - "def timetxttoDatetime(t):\n", - " return datetime.strptime(t,'%Y-%m-%d')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "request = dataretriever()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from all regions\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from all regions')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()\n", - "state.name = 'Chile'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.UCI_non_covid = state.UCI_use_noncovid\n", - "state.sochimi_tr = state.UCI_tr#[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Infectados Diarios" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto5/TotalesNacionales.csv'\n", - "casosnuevos = pd.read_csv(endpoint)\n", - "I_d = casosnuevos[casosnuevos['Fecha']=='Casos nuevos totales'].transpose()\n", - "dates_I_d = [timetxttoDatetime(I_d.axes[0][i]) for i in range(1,len(I_d.axes[0]))]\n", - "I_d = np.array(I_d.iloc[1:][6])\n", - "I_ac = np.cumsum(I_d)\n", - "\n", - "index_ac = np.where(np.array(dates_I_d) >= initdate)[0][0]\n", - "I_ac = I_ac[index_ac:]\n", - "I_ac_dates = dates_I_d[index_ac:]\n", - "I_ac_tr = [(i-initdate).days for i in I_ac_dates]\n", - "\n", - "index = np.where(np.array(dates_I_d) >= initdate-timedelta(days=7))[0][0]\n", - "dates_I_d = dates_I_d[index:]\n", - "I_d = I_d[index:]\n", - "weekdays = [date.weekday(dates_I_d[i]) for i in range(len(dates_I_d))]\n", - "I = pd.DataFrame({'dates':dates_I_d,'I':I_d,'weekdays':weekdays})\n", - "I_week = I[(I['weekdays']>2) | (I['weekdays']==0)]\n", - "I_week_ra = I_week['I'].rolling(5).mean()[I_week['dates']>=initdate]\n", - "I_week_ra_dates = I_week['dates'][I_week['dates']>=initdate]\n", - "I_week_ra_tr = [(i-initdate).days for i in I_week_ra_dates]\n", - "index_date = np.where(np.array(dates_I_d) >= initdate)[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 12, 3, 0, 0)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "initdate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.2#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "mitdadvacaciones = datetime(2021,2,15)\n", - "mitadvacaciones_day = (mitdadvacaciones - initdate).days\n", - "cuarentena = datetime(2021,4,11)\n", - "cuarentena_day = (cuarentena - initdate).days\n", - "\n", - "# Quarantines \n", - "first_mob = 0.45\n", - "second_mob = 0.57\n", - "third_mob = 0.59 #0.612\n", - "fourth_mob = 0.59 #0.612\n", - "\n", - "days=[[0,newyear_day+3],[newyear_day+3,mitadvacaciones_day],[mitadvacaciones_day,cuarentena_day],[cuarentena_day,tsim]]\n", - "alpha_new = Events.Events(values=[first_mob,second_mob,third_mob,fourth_mob],days=days)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot Quarantines\n", - "if False:\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in [alpha_new]]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []\n", - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "#chi = [chi0]\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi0)\n", - "\n", - "\n", - "# Navidad\n", - "increasedays = 2\n", - "renewalFactor = 0.65\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "\n", - "chi1 = SeroPrevDynamics(navidad_day,navidad_day+increasedays*0.8,navidad_day+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "#chi = Events.functionAddition([chi0,chi1])\n", - "chi.append(chi1)\n", - "\n", - "\n", - "# Año nuevo\n", - "delay = 0\n", - "increasedays = 7\n", - "renewalFactor_ny = 0.25\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor_ny/increasedays\n", - "\n", - "chi2 = SeroPrevDynamics(newyear_day+delay,newyear_day+delay+increasedays*0.05,newyear_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi2)\n", - "\n", - "chi = Events.functionAddition(chi)\n", - "\n", - "#### Contraction during holidays\n", - "delay = 0\n", - "hd_contraction = datetime(2021,1,19) #+ timedelta(days=30)\n", - "hd_contraction_day = (hd_contraction-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 10\n", - "decrease = -0.55\n", - "# Daily amount of people\n", - "dailyincrease = state.population*SeroPrevFactor*decrease/increasedays\n", - "\n", - "chi4 = SeroPrevDynamics(hd_contraction_day,hd_contraction_day+delay+increasedays*0.0001,hd_contraction_day+delay+increasedays,dailyincrease) \n", - "chi = [chisum(chi,chi4)]\n", - "\n", - "\n", - "#### Second Expansion during holidays\n", - "\n", - "delay = 0\n", - "hd_expansion = datetime(2021,2,13) #+ timedelta(days=30)\n", - "hd_expansion_day = (hd_expansion-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 33\n", - "increase = 1.4\n", - "# Daily amount of people\n", - "dailyincrease = state.population*SeroPrevFactor*increase/increasedays\n", - "\n", - "chi5 = SeroPrevDynamics(hd_expansion_day,hd_expansion_day+delay+increasedays*0.0001,hd_expansion_day+delay+increasedays,dailyincrease) \n", - "chi = [chisum(chi[0],chi5)]\n", - "\n", - "\n", - "#### Contraction due to lockdown\n", - "delay = 0\n", - "hd_expansion = datetime(2021,4,11) #+ timedelta(days=30)\n", - "hd_expansion_day = (hd_expansion-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 15\n", - "increase = -0.5\n", - "# Daily amount of people\n", - "dailyincrease = state.population*SeroPrevFactor*increase/increasedays\n", - "\n", - "chi6 = SeroPrevDynamics(hd_expansion_day,hd_expansion_day+delay+increasedays*0.0001,hd_expansion_day+delay+increasedays,dailyincrease) \n", - "chi = [chisum(chi[0],chi6)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "if True:\n", - " chiplot = [chi[0](t) for t in np.arange(0,tsim,0.1)]\n", - " plt.title('Tasa Ingreso de Susceptibles',fontsize=20)\n", - " \n", - " plt.plot(np.arange(0,tsim,0.1),chiplot)#,label='RenewalFactor: '+str(renewalFactor[i]))\n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad')\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevp')\n", - " plt.axvline(x = today_day, linestyle = 'dotted',color = 'green',label='hoy')\n", - "\n", - " plt.xlim(0,150)\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0,fontsize=15)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#alpha[0]\n", - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new,k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new,k_I=k_I,k_R = k_R, chi = chi[i], \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(chi))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.7s\n", - "[Parallel(n_jobs=12)]: Done 1 out of 1 | elapsed: 1.8s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Informe Diario\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "beta_InfDiario = 0.022\n", - "mu_InfDiario = 0.31#298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.2#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "mitdadvacaciones = datetime(2021,2,24)\n", - "mitadvacaciones_day = (mitdadvacaciones - initdate).days\n", - "cuarentena = datetime(2021,4,11)\n", - "cuarentena_day = (cuarentena - initdate).days\n", - "\n", - "# Quarantines \n", - "# inicio\n", - "first_mob = 0.46#0.45\n", - "# Año nuevo\n", - "second_mob = 0.57\n", - "# Expansion mitad febrero\n", - "third_mob = 0.6#612 #0.612\n", - "# Cuarentena\n", - "fourth_mob = third_mob #0.612\n", - "\n", - "days=[[0,newyear_day+3],[newyear_day+3,mitadvacaciones_day],[mitadvacaciones_day,cuarentena_day],[cuarentena_day,tsim]]\n", - "alpha_InfDiario = Events.Events(values=[first_mob,second_mob,third_mob,fourth_mob],days=days)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot Quarantines\n", - "if False:\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in [alpha_InfDiario]]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []\n", - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 1.0 #0.7\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "#chi = [chi0]\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi0)\n", - "\n", - "\n", - "# Navidad\n", - "delay = 3\n", - "increasedays = 2\n", - "renewalFactor = 0.65 #0.65\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "\n", - "chi1 = SeroPrevDynamics(navidad_day+delay,navidad_day+delay+increasedays*0.8,navidad_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "#chi = Events.functionAddition([chi0,chi1])\n", - "chi.append(chi1)\n", - "\n", - "\n", - "# Año nuevo\n", - "delay = 3\n", - "increasedays = 7\n", - "renewalFactor_ny = 0.25\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor_ny/increasedays\n", - "\n", - "chi2 = SeroPrevDynamics(newyear_day+delay,newyear_day+delay+increasedays*0.05,newyear_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi2)\n", - "\n", - "chi = Events.functionAddition(chi)\n", - "\n", - "#### Contraction during holidays\n", - "delay = 5\n", - "hd_contraction = datetime(2021,1,19) #+ timedelta(days=30)\n", - "hd_contraction_day = (hd_contraction-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 10\n", - "decrease = -0.50\n", - "# Daily amount of people\n", - "dailyincrease = state.population*SeroPrevFactor*decrease/increasedays\n", - "\n", - "chi4 = SeroPrevDynamics(hd_contraction_day+delay,hd_contraction_day+delay+increasedays*0.0001,hd_contraction_day+delay+increasedays,dailyincrease) \n", - "chi = [chisum(chi,chi4)]\n", - "\n", - "\n", - "#### Second Expansion during holidays\n", - "\n", - "delay = 7\n", - "hd_expansion = datetime(2021,2,13) #+ timedelta(days=30)\n", - "hd_expansion_day = (hd_expansion-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 7\n", - "increase = 1#1#0.7#0.7#1.4\n", - "# Daily amount of people\n", - "dailyincrease = state.population*SeroPrevFactor*increase/increasedays\n", - "\n", - "chi5 = SeroPrevDynamics(hd_expansion_day+delay,hd_expansion_day+delay+increasedays*0.0001,hd_expansion_day+delay+increasedays,dailyincrease) \n", - "chi = [chisum(chi[0],chi5)]\n", - "\n", - "\n", - "#### Contraction due to lockdown\n", - "delay = 0\n", - "hd_expansion = datetime(2021,4,11) #+ timedelta(days=30)\n", - "hd_expansion_day = (hd_expansion-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 15\n", - "increase = 0#-0.5\n", - "# Daily amount of people\n", - "dailyincrease = state.population*SeroPrevFactor*increase/increasedays\n", - "\n", - "chi6 = SeroPrevDynamics(hd_expansion_day,hd_expansion_day+delay+increasedays*0.0001,hd_expansion_day+delay+increasedays,dailyincrease) \n", - "chi = [chisum(chi[0],chi6)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " chiplot = [chi[0](t) for t in np.arange(0,tsim,0.1)]\n", - " plt.title('Tasa Ingreso de Susceptibles',fontsize=20)\n", - " \n", - " plt.plot(np.arange(0,tsim,0.1),chiplot)#,label='RenewalFactor: '+str(renewalFactor[i]))\n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad')\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevp')\n", - " plt.axvline(x = today_day, linestyle = 'dotted',color = 'green',label='hoy')\n", - "\n", - " plt.xlim(0,150)\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0,fontsize=15)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulation constructor" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "import copy" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "state_InfDiario = copy.deepcopy(state)\n", - "state_InfDiario.I_d_r = list(I_week_ra)\n", - "state_InfDiario.I_d_r_dates = list(I_week_ra_dates)\n", - "state_InfDiario.I_d_r_tr = I_week_ra_tr\n", - "\n", - "state_InfDiario.I_ac_r = I_ac\n", - "state_InfDiario.I_ac_r_dates = I_ac_dates\n", - "state_InfDiario.I_ac_r_tr = I_ac_tr\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation_InfDiario = [SEIRHVD(tsim,beta_InfDiario,mu_InfDiario,alpha_InfDiario,k_I=k_I,k_R = k_R, chi = chi[i], \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state_InfDiario) for i in range(len(chi))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation_InfDiario)):\n", - " simulation_InfDiario[i].pE_Ias=pE_Ias\n", - " simulation_InfDiario[i].tE_Ias=tE_Ias\n", - " simulation_InfDiario[i].pE_Imi=pE_Imi\n", - " simulation_InfDiario[i].tE_Imi=tE_Imi\n", - " simulation_InfDiario[i].pE_Icr=pE_Icr\n", - " simulation_InfDiario[i].tE_Icr=tE_Icr\n", - " simulation_InfDiario[i].pE_Ise=pE_Ise\n", - " simulation_InfDiario[i].tE_Ise=tE_Ise\n", - " simulation_InfDiario[i].pIas_R=pIas_R\n", - " simulation_InfDiario[i].tIas_R =tIas_R\n", - " simulation_InfDiario[i].pImi_R=pImi_R\n", - " simulation_InfDiario[i].tImi_R =tImi_R\n", - " simulation_InfDiario[i].pIse_Hse=pIse_Hse\n", - " simulation_InfDiario[i].tIse_Hse=tIse_Hse\n", - " simulation_InfDiario[i].pIse_D=pIse_D\n", - " simulation_InfDiario[i].tIse_D=tIse_D\n", - " simulation_InfDiario[i].pIcr_V=pIcr_V\n", - " simulation_InfDiario[i].tIcr_V=tIcr_V\n", - " simulation_InfDiario[i].pIcr_D=pIcr_D\n", - " simulation_InfDiario[i].tIcr_D=tIcr_D\n", - " simulation_InfDiario[i].pHse_R=pHse_R\n", - " simulation_InfDiario[i].tHse_R =tHse_R\n", - " simulation_InfDiario[i].pHse_V=pHse_V\n", - " simulation_InfDiario[i].tHse_V=tHse_V\n", - " simulation_InfDiario[i].pHse_D=pHse_D\n", - " simulation_InfDiario[i].tHse_D=tHse_D\n", - " simulation_InfDiario[i].pV_Hout=pV_Hout\n", - " simulation_InfDiario[i].tV_Hout =tV_Hout\n", - " simulation_InfDiario[i].pV_D=pV_D\n", - " simulation_InfDiario[i].tV_D =tV_D\n", - " simulation_InfDiario[i].pHout_R=pHout_R\n", - " simulation_InfDiario[i].tHout_R=tHout_R\n", - " simulation_InfDiario[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.5s\n", - "[Parallel(n_jobs=12)]: Done 1 out of 1 | elapsed: 1.5s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims_InfDiario = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation_InfDiario,i,tsim) for i in range(len(simulation_InfDiario)))\n", - "simulation_InfDiario = sims_InfDiario\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,9,30)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "pĺotlinewidth = 4\n", - "datelinewidth = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "#4k\n", - "fontsize_title = 22\n", - "fontsize_suptitle = 25\n", - "fontsize_label = 18\n", - "fontsize_legend = 14" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "#1080\n", - "fontsize_title = 15\n", - "fontsize_suptitle = 16\n", - "fontsize_label = 12\n", - "fontsize_legend = 8\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "sim_InfDiario = True\n", - "sim_InfEPI = True" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " if sim_InfEPI:\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if sim_InfDiario:\n", - " axs[0,0].plot(dates[i],simulation_InfDiario[i].I_ac_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - "axs[0,0].set_title('Infectados acumulados',fontsize=fontsize_title)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "#axs[0,0].set_ylim(0,1.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "\n", - "#axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "#axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "#axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "if sim_InfEPI:\n", - " axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Informe EPI',zorder=10)\n", - "\n", - "if sim_InfDiario:\n", - " # cambiar\n", - " axs[0,0].scatter(I_ac_dates,I_ac,color='grey',label='Informe Diario',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if sim_InfEPI:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_InfDiario:\n", - " axs[0,1].plot(dates[i],simulation_InfDiario[i].B,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Muertes Acumuladas',fontsize=fontsize_title)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if sim_InfEPI:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_InfDiario:\n", - " axs[1,0].plot(dates[i],simulation_InfDiario[i].I_d_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=fontsize_title)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "if sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - " \n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if sim_InfEPI:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if sim_InfDiario:\n", - " axs[1,1].plot(dates[i],simulation_InfDiario[i].V,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,1].set_title('Uso UCI',fontsize=fontsize_title)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "#axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap,label='UCI Capacity', linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2021,2,15), linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc=0,fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc=1,fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc=1,fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc=1,fontsize=fontsize_legend)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(dates[0],simulation_InfDiario[0].I)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1775003.3898305085" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation_InfDiario[0].I_ac[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.14792398194423123" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation_InfDiario[0].prevalence_total[(datetime(2021,3,9)-initdate).days]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop\n" - ] - } - ], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stop\n" - ] - } - ], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "plt.scatter(state.I_d_r_dates,np.log(state.I_d_r),label='Informe EPI',color='black',zorder=5)\n", - "plt.scatter(I_week_ra_dates,np.log(I_week_ra),label='Informe diario (RA5D)',color='red',zorder=5) \n", - "plt.legend(loc=0)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulación control" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "tsim = 10000\n", - "mu = 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "def alphacontrol(t):\n", - " return 1" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation_control = SEIRHVD(tsim,0.13,mu,alphacontrol,k_I=k_I,k_R = k_R, chi = 0, \n", - " expinfection=expinfection,initdate = initdate,\n", - " RealIC=state)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "beta = [0.01,0.05,0.1,0.15]\n", - "sims = []\n", - "for i in beta:\n", - " sims.append(SEIRHVD(tsim,i,mu,alphacontrol,Htot=10000,Vtot=10000,\n", - " H0=0,V0=0,B0=0,D0=0,R0=0,I0=100,I_d0=10,I_ac0=100,SeroPrevFactor=1,expinfection=0,\n", - " population=1000000))\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "linestyle = ['dashed','dotted','solid','dashdot']" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "for i in sims:\n", - " i.integr_sci(0,tsim,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(sims)):\n", - " #plt.plot(sims[i].t,sims[i].S,label='S beta: '+str(beta[i]),linestyle=linestyle[i])\n", - " #plt.plot(sims[i].t,sims[i].I,label='I beta: '+str(beta[i]),linestyle=linestyle[i])\n", - " plt.plot(sims[i].t,sims[i].R,label='R beta: '+str(beta[i]),linestyle=linestyle[i])\n", - " plt.plot(sims[i].t,sims[i].B,label='B beta: '+str(beta[i]),linestyle=linestyle[i])\n", - " #plt.plot(sims[i].t,sims[i].I_ac,label='I_ac beta: '+str(beta[i]),linestyle=linestyle[i])\n", - " #plt.plot(simulation_control.t,simulation_control.I,label='I '+I)\n", - "#plt.plot(simulation_control.t,simulation_control.I_ac,label='I_ac')\n", - "#plt.plot(simulation_control.t,simulation_control.R,label='R')\n", - "#plt.plot(simulation_control.t,simulation_control.B,label='B')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "invalid index to scalar variable.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimulation_control\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable." - ] - } - ], - "source": [ - "simulation_control.S[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_control.S[0] - simulation_control.B[-1] - simulation_control.R[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta*rem_mob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Susceptibles')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].S,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].S,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - " \n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Infectados Activos')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('SeroPrevalence')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I_ac/simulation[i].population,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I_ac/simulation_uk[i].population,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_Enero_CepaUK.ipynb b/DataFit/Chile_Enero_CepaUK.ipynb deleted file mode 100644 index 6d9dd1b..0000000 --- a/DataFit/Chile_Enero_CepaUK.ipynb +++ /dev/null @@ -1,1604 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,10,19)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19458310" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Chile'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.sochimi_tr = state.UCI_tr[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.85\n", - "rem_mob = 0.45\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q2.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 7.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 7.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0.15,0.3,0.45,0.6,0.75]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "navidad = datetime(2020,12,25)\n", - "navidad_day = (datetime(2020,12,25)-initdate).days\n", - "# Increase date \n", - "increasedate = datetime(2020,12,25)\n", - "increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 20\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "#chi = [chisum(chi0,SeroPrevDynamics(increaseday,increaseday+increasedays*0.50001,increaseday+increasedays,i)) for i in dailyincrease]\n", - "chi = [chisum(chi0,SeroPrevDynamics(navidad_day-4,navidad_day+increasedays*0.0001,navidad_day+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i,\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.7s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 3.0s remaining: 4.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 3.8s remaining: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 4.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 4.3s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sim cepa UK" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta_uk = 0.016079461886830973\n", - "\n", - "# Quarantines \n", - "max_mob = rem_mob*beta_uk/(rem_mob*beta)\n", - "rem_mob = 0.45\n", - "\n", - "Q_UK = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=navidad_day)\n", - "\n", - "alpha_UK = Q_UK.alpha" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6991070385578684" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_mob" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.553571196795263" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beta_uk/(beta*rem_mob)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6991070385578684" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_mob" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_uk = [SEIRHVD(tsim,beta,mu,alpha_UK,k_I=k_I,k_R = k_R, chi = i,\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in chi]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation_uk)):\n", - " simulation_uk[i].pE_Ias=pE_Ias\n", - " simulation_uk[i].tE_Ias=tE_Ias\n", - " simulation_uk[i].pE_Imi=pE_Imi\n", - " simulation_uk[i].tE_Imi=tE_Imi\n", - " simulation_uk[i].pE_Icr=pE_Icr\n", - " simulation_uk[i].tE_Icr=tE_Icr\n", - " simulation_uk[i].pE_Ise=pE_Ise\n", - " simulation_uk[i].tE_Ise=tE_Ise\n", - " simulation_uk[i].pIas_R=pIas_R\n", - " simulation_uk[i].tIas_R =tIas_R\n", - " simulation_uk[i].pImi_R=pImi_R\n", - " simulation_uk[i].tImi_R =tImi_R\n", - " simulation_uk[i].pIse_Hse=pIse_Hse\n", - " simulation_uk[i].tIse_Hse=tIse_Hse\n", - " simulation_uk[i].pIse_D=pIse_D\n", - " simulation_uk[i].tIse_D=tIse_D\n", - " simulation_uk[i].pIcr_V=pIcr_V\n", - " simulation_uk[i].tIcr_V=tIcr_V\n", - " simulation_uk[i].pIcr_D=pIcr_D\n", - " simulation_uk[i].tIcr_D=tIcr_D\n", - " simulation_uk[i].pHse_R=pHse_R\n", - " simulation_uk[i].tHse_R =tHse_R\n", - " simulation_uk[i].pHse_V=pHse_V\n", - " simulation_uk[i].tHse_V=tHse_V\n", - " simulation_uk[i].pHse_D=pHse_D\n", - " simulation_uk[i].tHse_D=tHse_D\n", - " simulation_uk[i].pV_Hout=pV_Hout\n", - " simulation_uk[i].tV_Hout =tV_Hout\n", - " simulation_uk[i].pV_D=pV_D\n", - " simulation_uk[i].tV_D =tV_D\n", - " simulation_uk[i].pHout_R=pHout_R\n", - " simulation_uk[i].tHout_R=tHout_R\n", - " simulation_uk[i].setnewparams()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 3.8s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 4.3s remaining: 6.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 4.3s remaining: 2.9s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 4.4s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 4.4s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation_uk,i,tsim) for i in range(len(simulation_uk)))\n", - "simulation_uk = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,1,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "83" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(state.I_d_r)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "sim_UK= True" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if sim_UK:\n", - " axs[0,0].plot(dates[i],simulation_uk[i].I_ac_det,color=colors[i],linestyle='dashed')\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].set_ylim(0,1.5e6)\n", - "\n", - "\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "#axs[0,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[0,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[0,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "#\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " if sim_UK:\n", - " axs[0,1].plot(dates[i],simulation_uk[i].B,color=colors[i],linestyle='dashed')\n", - " \n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "#axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[0,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[0,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "#axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " if sim_UK:\n", - " axs[1,0].plot(dates[i],simulation_uk[i].I_d_det,color=colors[i],linestyle='dashed') \n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,0].set_ylim(0,1e4)\n", - "\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if sim_UK:\n", - " axs[1,1].plot(dates[i],simulation_uk[i].V,color=colors[i],linestyle='dashed') \n", - "axs[1,1].set_title('UCI Usage')\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='UCI covid use')\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad')\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'cyan',label='Año Nuevo')\n", - "\n", - "#axs[1,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " plt.plot(simulation[i].dates,simulation[i].I_d_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "plt.scatter(state.Ir_dates,state.Ir)\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_Enero_v2.ipynb b/DataFit/Chile_Enero_v2.ipynb deleted file mode 100644 index 093ef5b..0000000 --- a/DataFit/Chile_Enero_v2.ipynb +++ /dev/null @@ -1,1864 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,12,1)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19458310" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Chile'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.sochimi_tr = state.UCI_tr[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.2#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK = Quarantine(rem_mob,max_mob_UK,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK2 = Events.functionAddition([Q1.alpha,Quarantine(0,max_mob_UK-max_mob,qp=0,iqt=0,fqt=today_day).alpha])\n", - "\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "Q3 = Events.Events([0.45,0.55], days=[[0,navidad_day],[navidad_day,tsim]])\n", - "\n", - "alpha = [Q1.alpha,Q_UK2]\n", - "\n", - "days=[[0,newyear_day+3],[newyear_day+3,today_day],[today_day,tsim]]\n", - "#days = [[0,10],[10,20],[20,30]]\n", - "alpha_new = Events.Events(values=[0.45,0.57,0.05],days=days)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "\n", - "redrate = [0.2,0.5,1.0]\n", - "alpha_new = [Events.Events(values=[rem_mob,max_mob,max_mob*i],days=days) for i in redrate]\n", - "\n", - "alpha_new_uk = [Events.Events(values=[rem_mob,max_mob,max_mob_UK*i],days=days) for i in redrate]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "t = np.array(np.arange(0,120,0.1))\n", - "alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - "alphaplot_UK= [[j(i) for i in t] for j in alpha_new_uk]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - } - ], - "source": [ - "for f in alphaplot:\n", - " plt.plot(t,f)\n", - "for f in alphaplot_UK:\n", - " plt.plot(t,f,linestyle='dashed') \n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0)\n", - "plt.title('Mobility-beta',fontsize=20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "#chi = [chi0]\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi0)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Navidad\n", - "increasedays = 2\n", - "renewalFactor = 0.65\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "\n", - "chi1 = SeroPrevDynamics(navidad_day,navidad_day+increasedays*0.8,navidad_day+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "#chi = Events.functionAddition([chi0,chi1])\n", - "chi.append(chi1)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Año nuevo\n", - "delay = 0\n", - "increasedays = 15\n", - "renewalFactor_ny = 0.5\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor_ny/increasedays\n", - "\n", - "chi2 = SeroPrevDynamics(newyear_day+delay,newyear_day+delay+increasedays*0.05,newyear_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "chi = Events.functionAddition(chi)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [-0.5,0,1.35]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "delay = 0\n", - "today = datetime.today() #+ timedelta(days=30)\n", - "today_day = (today-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 15\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]\n", - "\n", - "chi3 = [SeroPrevDynamics(today_day-2.5,today_day-2.5+delay+increasedays*0.0001,today_day-2.5+delay+increasedays,i) for i in dailyincrease]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi,i) for i in chi3]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Tasa Ingreso de Susceptibles',fontsize=20)\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad')\n", - "plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevp')\n", - "plt.axvline(x = today_day, linestyle = 'dotted',color = 'green',label='hoy')\n", - "\n", - "plt.xlim(0,75)\n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0,fontsize=15)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "#alpha[0]\n", - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(alpha_new)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.3s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.9s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.9s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation UK\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.45" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new_uk[0](10)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_uk = [SEIRHVD(tsim,beta,mu,alpha_new_uk[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation_uk)):\n", - " simulation_uk[i].pE_Ias=pE_Ias\n", - " simulation_uk[i].tE_Ias=tE_Ias\n", - " simulation_uk[i].pE_Imi=pE_Imi\n", - " simulation_uk[i].tE_Imi=tE_Imi\n", - " simulation_uk[i].pE_Icr=pE_Icr\n", - " simulation_uk[i].tE_Icr=tE_Icr\n", - " simulation_uk[i].pE_Ise=pE_Ise\n", - " simulation_uk[i].tE_Ise=tE_Ise\n", - " simulation_uk[i].pIas_R=pIas_R\n", - " simulation_uk[i].tIas_R =tIas_R\n", - " simulation_uk[i].pImi_R=pImi_R\n", - " simulation_uk[i].tImi_R =tImi_R\n", - " simulation_uk[i].pIse_Hse=pIse_Hse\n", - " simulation_uk[i].tIse_Hse=tIse_Hse\n", - " simulation_uk[i].pIse_D=pIse_D\n", - " simulation_uk[i].tIse_D=tIse_D\n", - " simulation_uk[i].pIcr_V=pIcr_V\n", - " simulation_uk[i].tIcr_V=tIcr_V\n", - " simulation_uk[i].pIcr_D=pIcr_D\n", - " simulation_uk[i].tIcr_D=tIcr_D\n", - " simulation_uk[i].pHse_R=pHse_R\n", - " simulation_uk[i].tHse_R =tHse_R\n", - " simulation_uk[i].pHse_V=pHse_V\n", - " simulation_uk[i].tHse_V=tHse_V\n", - " simulation_uk[i].pHse_D=pHse_D\n", - " simulation_uk[i].tHse_D=tHse_D\n", - " simulation_uk[i].pV_Hout=pV_Hout\n", - " simulation_uk[i].tV_Hout =tV_Hout\n", - " simulation_uk[i].pV_D=pV_D\n", - " simulation_uk[i].tV_D =tV_D\n", - " simulation_uk[i].pHout_R=pHout_R\n", - " simulation_uk[i].tHout_R=tHout_R\n", - " simulation_uk[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.7s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.7s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims_uk = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation_uk,i,tsim) for i in range(len(simulation_uk)))\n", - "simulation_uk = sims_uk\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "sim_UK = True" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "pĺotlinewidth = 4\n", - "datelinewidth = 3" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if sim_UK:\n", - " axs[0,0].plot(dates[i],simulation_uk[i].I_ac_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - "axs[0,0].set_title('Infectados acumulados',fontsize=22)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "#axs[0,0].set_ylim(0,1.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Datos',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[0,1].plot(dates[i],simulation_uk[i].B,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Muertes Acumuladas',fontsize=22)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[1,0].plot(dates[i],simulation_uk[i].I_d_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=22)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Datos',color='black',zorder=10)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if sim_UK:\n", - " axs[1,1].plot(dates[i],simulation_uk[i].V,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,1].set_title('Uso UCI',fontsize=22)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "#axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap,label='UCI Capacity', linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=18)\n", - "axs[0,1].tick_params(labelsize=18)\n", - "axs[1,0].tick_params(labelsize=18)\n", - "axs[1,1].tick_params(labelsize=18)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc=0,fontsize=14)\n", - "axs[0,1].legend(loc=1,fontsize=14)\n", - "axs[1,0].legend(loc=1,fontsize=14)\n", - "axs[1,1].legend(loc=1,fontsize=14)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=25)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta*rem_mob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Susceptibles')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].S,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].S,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - " \n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Infectados Activos')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('SeroPrevalence')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I_ac/simulation[i].population,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I_ac/simulation_uk[i].population,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_Enero_v3.ipynb b/DataFit/Chile_Enero_v3.ipynb deleted file mode 100644 index b9e630e..0000000 --- a/DataFit/Chile_Enero_v3.ipynb +++ /dev/null @@ -1,1860 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,12,1)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19458310" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Chile'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.sochimi_tr = state.UCI_tr[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.2#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK = Quarantine(rem_mob,max_mob_UK,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK2 = Events.functionAddition([Q1.alpha,Quarantine(0,max_mob_UK-3,qp=0,iqt=0,fqt=today_day).alpha])\n", - "\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "Q3 = Events.Events([0.45,0.55], days=[[0,navidad_day],[navidad_day,tsim]])\n", - "\n", - "alpha = [Q1.alpha,Q_UK2]\n", - "\n", - "days=[[0,newyear_day+3],[newyear_day+3,today_day],[today_day,tsim]]\n", - "#days = [[0,10],[10,20],[20,30]]\n", - "alpha_new = Events.Events(values=[0.45,0.57,0.05],days=days)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "\n", - "redrate = [0.2,0.5,1.0]\n", - "alpha_new = [Events.Events(values=[rem_mob,max_mob,max_mob*i],days=days) for i in redrate]\n", - "\n", - "alpha_new_uk = [Events.Events(values=[rem_mob,max_mob,max_mob_UK*i],days=days) for i in redrate]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "t = np.array(np.arange(0,120,0.1))\n", - "alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - "alphaplot_UK= [[j(i) for i in t] for j in alpha_new_uk]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - } - ], - "source": [ - "for f in alphaplot:\n", - " plt.plot(t,f)\n", - "for f in alphaplot_UK:\n", - " plt.plot(t,f,linestyle='dashed') \n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0)\n", - "plt.title('Mobility-beta',fontsize=20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "#chi = [chi0]\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Navidad\n", - "increasedays = 2\n", - "renewalFactor = 0.65\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "\n", - "chi1 = SeroPrevDynamics(navidad_day,navidad_day+increasedays*0.8,navidad_day+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "#chi = Events.functionAddition([chi0,chi1])\n", - "chi.append(chi1)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Año nuevo\n", - "delay = 0\n", - "increasedays = 7\n", - "renewalFactor_ny = 0.25\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor_ny/increasedays\n", - "\n", - "chi2 = SeroPrevDynamics(newyear_day+delay,newyear_day+delay+increasedays*0.05,newyear_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "chi = Events.functionAddition(chi)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [-0.5,0,1.35]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "delay = 0\n", - "today = datetime.today() #+ timedelta(days=30)\n", - "today_day = (today-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 15\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]\n", - "\n", - "chi3 = [SeroPrevDynamics(today_day-2.5,today_day-2.5+delay+increasedays*0.0001,today_day-2.5+delay+increasedays,i) for i in dailyincrease]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi,i) for i in chi3]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Tasa Ingreso de Susceptibles',fontsize=20)\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad')\n", - "plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevp')\n", - "plt.axvline(x = today_day, linestyle = 'dotted',color = 'green',label='hoy')\n", - "\n", - "plt.xlim(0,75)\n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0,fontsize=15)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "#alpha[0]\n", - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(alpha_new)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.9s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.9s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation UK\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.45" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new_uk[0](10)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_uk = [SEIRHVD(tsim,beta,mu,alpha_new_uk[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation_uk)):\n", - " simulation_uk[i].pE_Ias=pE_Ias\n", - " simulation_uk[i].tE_Ias=tE_Ias\n", - " simulation_uk[i].pE_Imi=pE_Imi\n", - " simulation_uk[i].tE_Imi=tE_Imi\n", - " simulation_uk[i].pE_Icr=pE_Icr\n", - " simulation_uk[i].tE_Icr=tE_Icr\n", - " simulation_uk[i].pE_Ise=pE_Ise\n", - " simulation_uk[i].tE_Ise=tE_Ise\n", - " simulation_uk[i].pIas_R=pIas_R\n", - " simulation_uk[i].tIas_R =tIas_R\n", - " simulation_uk[i].pImi_R=pImi_R\n", - " simulation_uk[i].tImi_R =tImi_R\n", - " simulation_uk[i].pIse_Hse=pIse_Hse\n", - " simulation_uk[i].tIse_Hse=tIse_Hse\n", - " simulation_uk[i].pIse_D=pIse_D\n", - " simulation_uk[i].tIse_D=tIse_D\n", - " simulation_uk[i].pIcr_V=pIcr_V\n", - " simulation_uk[i].tIcr_V=tIcr_V\n", - " simulation_uk[i].pIcr_D=pIcr_D\n", - " simulation_uk[i].tIcr_D=tIcr_D\n", - " simulation_uk[i].pHse_R=pHse_R\n", - " simulation_uk[i].tHse_R =tHse_R\n", - " simulation_uk[i].pHse_V=pHse_V\n", - " simulation_uk[i].tHse_V=tHse_V\n", - " simulation_uk[i].pHse_D=pHse_D\n", - " simulation_uk[i].tHse_D=tHse_D\n", - " simulation_uk[i].pV_Hout=pV_Hout\n", - " simulation_uk[i].tV_Hout =tV_Hout\n", - " simulation_uk[i].pV_D=pV_D\n", - " simulation_uk[i].tV_D =tV_D\n", - " simulation_uk[i].pHout_R=pHout_R\n", - " simulation_uk[i].tHout_R=tHout_R\n", - " simulation_uk[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 2.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 2.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims_uk = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation_uk,i,tsim) for i in range(len(simulation_uk)))\n", - "simulation_uk = sims_uk\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "sim_UK = True" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "pĺotlinewidth = 4\n", - "datelinewidth = 3" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if sim_UK:\n", - " axs[0,0].plot(dates[i],simulation_uk[i].I_ac_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - "axs[0,0].set_title('Infectados acumulados',fontsize=22)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "#axs[0,0].set_ylim(0,1.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Datos',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[0,1].plot(dates[i],simulation_uk[i].B,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Muertes Acumuladas',fontsize=22)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[1,0].plot(dates[i],simulation_uk[i].I_d_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=22)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Datos',color='black',zorder=10)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if sim_UK:\n", - " axs[1,1].plot(dates[i],simulation_uk[i].V,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,1].set_title('Uso UCI',fontsize=22)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "#axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap,label='UCI Capacity', linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=18)\n", - "axs[0,1].tick_params(labelsize=18)\n", - "axs[1,0].tick_params(labelsize=18)\n", - "axs[1,1].tick_params(labelsize=18)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc=0,fontsize=14)\n", - "axs[0,1].legend(loc=1,fontsize=14)\n", - "axs[1,0].legend(loc=1,fontsize=14)\n", - "axs[1,1].legend(loc=1,fontsize=14)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=25)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta*rem_mob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Susceptibles')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].S,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].S,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - " \n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Infectados Activos')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "#%matplotlib tk\n", - "plt.title('SeroPrevalence')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I_ac/simulation[i].population,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I_ac/simulation_uk[i].population,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Chile_Marzo_v1.ipynb b/DataFit/Chile_Marzo_v1.ipynb deleted file mode 100644 index 4c21c7f..0000000 --- a/DataFit/Chile_Marzo_v1.ipynb +++ /dev/null @@ -1,1794 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,12,1)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19458310" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Chile'\n", - "state.population" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.sochimi_tr = state.UCI_tr[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.2#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK = Quarantine(rem_mob,max_mob_UK,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK2 = Events.functionAddition([Q1.alpha,Quarantine(0,max_mob_UK-3,qp=0,iqt=0,fqt=today_day).alpha])\n", - "\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "Q3 = Events.Events([0.45,0.55], days=[[0,navidad_day],[navidad_day,tsim]])\n", - "\n", - "alpha = [Q1.alpha,Q_UK2]\n", - "\n", - "days=[[0,newyear_day+3],[newyear_day+3,today_day],[today_day,tsim]]\n", - "#days = [[0,10],[10,20],[20,30]]\n", - "alpha_new = Events.Events(values=[0.45,0.57,0.05],days=days)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "\n", - "redrate = [0.2,0.5,1.0]\n", - "alpha_new = [Events.Events(values=[rem_mob,max_mob,max_mob*i],days=days) for i in redrate]\n", - "\n", - "alpha_new_uk = [Events.Events(values=[rem_mob,max_mob,max_mob_UK*i],days=days) for i in redrate]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "t = np.array(np.arange(0,120,0.1))\n", - "alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - "alphaplot_UK= [[j(i) for i in t] for j in alpha_new_uk]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - } - ], - "source": [ - "plt.figure()\n", - "for f in alphaplot:\n", - " plt.plot(t,f)\n", - "for f in alphaplot_UK:\n", - " plt.plot(t,f,linestyle='dashed') \n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0)\n", - "plt.title('Mobility-beta',fontsize=20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "#chi = [chi0]\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Navidad\n", - "increasedays = 2\n", - "renewalFactor = 0.65\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "\n", - "chi1 = SeroPrevDynamics(navidad_day,navidad_day+increasedays*0.8,navidad_day+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "#chi = Events.functionAddition([chi0,chi1])\n", - "chi.append(chi1)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Año nuevo\n", - "delay = 0\n", - "increasedays = 7\n", - "renewalFactor_ny = 0.25\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor_ny/increasedays\n", - "\n", - "chi2 = SeroPrevDynamics(newyear_day+delay,newyear_day+delay+increasedays*0.05,newyear_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "chi = Events.functionAddition(chi)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [-0.5,0,1.35]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "delay = 0\n", - "today = datetime.today() #+ timedelta(days=30)\n", - "today_day = (today-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 15\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]\n", - "\n", - "chi3 = [SeroPrevDynamics(today_day-2.5,today_day-2.5+delay+increasedays*0.0001,today_day-2.5+delay+increasedays,i) for i in dailyincrease]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi,i) for i in chi3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Tasa Ingreso de Susceptibles',fontsize=20)\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad')\n", - "plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevp')\n", - "plt.axvline(x = today_day, linestyle = 'dotted',color = 'green',label='hoy')\n", - "\n", - "plt.xlim(0,75)\n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0,fontsize=15)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#alpha[0]\n", - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(alpha_new)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation UK\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "alpha_new_uk[0](10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_uk = [SEIRHVD(tsim,beta,mu,alpha_new_uk[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation_uk)):\n", - " simulation_uk[i].pE_Ias=pE_Ias\n", - " simulation_uk[i].tE_Ias=tE_Ias\n", - " simulation_uk[i].pE_Imi=pE_Imi\n", - " simulation_uk[i].tE_Imi=tE_Imi\n", - " simulation_uk[i].pE_Icr=pE_Icr\n", - " simulation_uk[i].tE_Icr=tE_Icr\n", - " simulation_uk[i].pE_Ise=pE_Ise\n", - " simulation_uk[i].tE_Ise=tE_Ise\n", - " simulation_uk[i].pIas_R=pIas_R\n", - " simulation_uk[i].tIas_R =tIas_R\n", - " simulation_uk[i].pImi_R=pImi_R\n", - " simulation_uk[i].tImi_R =tImi_R\n", - " simulation_uk[i].pIse_Hse=pIse_Hse\n", - " simulation_uk[i].tIse_Hse=tIse_Hse\n", - " simulation_uk[i].pIse_D=pIse_D\n", - " simulation_uk[i].tIse_D=tIse_D\n", - " simulation_uk[i].pIcr_V=pIcr_V\n", - " simulation_uk[i].tIcr_V=tIcr_V\n", - " simulation_uk[i].pIcr_D=pIcr_D\n", - " simulation_uk[i].tIcr_D=tIcr_D\n", - " simulation_uk[i].pHse_R=pHse_R\n", - " simulation_uk[i].tHse_R =tHse_R\n", - " simulation_uk[i].pHse_V=pHse_V\n", - " simulation_uk[i].tHse_V=tHse_V\n", - " simulation_uk[i].pHse_D=pHse_D\n", - " simulation_uk[i].tHse_D=tHse_D\n", - " simulation_uk[i].pV_Hout=pV_Hout\n", - " simulation_uk[i].tV_Hout =tV_Hout\n", - " simulation_uk[i].pV_D=pV_D\n", - " simulation_uk[i].tV_D =tV_D\n", - " simulation_uk[i].pHout_R=pHout_R\n", - " simulation_uk[i].tHout_R=tHout_R\n", - " simulation_uk[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims_uk = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation_uk,i,tsim) for i in range(len(simulation_uk)))\n", - "simulation_uk = sims_uk\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sim_UK = True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pĺotlinewidth = 4\n", - "datelinewidth = 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if sim_UK:\n", - " axs[0,0].plot(dates[i],simulation_uk[i].I_ac_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - "axs[0,0].set_title('Infectados acumulados',fontsize=22)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "#axs[0,0].set_ylim(0,1.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Datos',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[0,1].plot(dates[i],simulation_uk[i].B,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Muertes Acumuladas',fontsize=22)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[1,0].plot(dates[i],simulation_uk[i].I_d_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=22)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Datos',color='black',zorder=10)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if sim_UK:\n", - " axs[1,1].plot(dates[i],simulation_uk[i].V,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,1].set_title('Uso UCI',fontsize=22)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "#axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap,label='UCI Capacity', linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=18)\n", - "axs[0,1].tick_params(labelsize=18)\n", - "axs[1,0].tick_params(labelsize=18)\n", - "axs[1,1].tick_params(labelsize=18)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc=0,fontsize=14)\n", - "axs[0,1].legend(loc=1,fontsize=14)\n", - "axs[1,0].legend(loc=1,fontsize=14)\n", - "axs[1,1].legend(loc=1,fontsize=14)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=25)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta*rem_mob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Susceptibles')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].S,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].S,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - " \n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Infectados Activos')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('SeroPrevalence')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I_ac/simulation[i].population,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I_ac/simulation_uk[i].population,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/DataFit_study.ipynb b/DataFit/DataFit_study.ipynb deleted file mode 100644 index 1b91f83..0000000 --- a/DataFit/DataFit_study.ipynb +++ /dev/null @@ -1,1907 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,8,1)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from Region de Los Rios\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from Region de Los Rios')\n", - "tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "#tstate = '13101'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19458310" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.name = 'Chile'\n", - "state.population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Study" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(state.tr, state.Ir)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(state.tr, np.log(state.Ir))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = state.UCI_capacity\n", - "state.sochimi_tr = state.UCI_tr[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "\n", - "beta = 0.023\n", - "mu = 0.298\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 0.2#1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK = Quarantine(rem_mob,max_mob_UK,qp=0,iqt=0,fqt=newyear_day+3)\n", - "\n", - "Q_UK2 = Events.functionAddition([Q1.alpha,Quarantine(0,max_mob_UK-3,qp=0,iqt=0,fqt=today_day).alpha])\n", - "\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "Q3 = Events.Events([0.45,0.55], days=[[0,navidad_day],[navidad_day,tsim]])\n", - "\n", - "alpha = [Q1.alpha,Q_UK2]\n", - "\n", - "days=[[0,newyear_day+3],[newyear_day+3,today_day],[today_day,tsim]]\n", - "#days = [[0,10],[10,20],[20,30]]\n", - "alpha_new = Events.Events(values=[0.45,0.57,0.05],days=days)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.57\n", - "rem_mob = 0.45\n", - "beta_uk_cl_ratio = 1.55\n", - "max_mob_UK = max_mob*beta_uk_cl_ratio\n", - "\n", - "\n", - "redrate = [0.2,0.5,1.0]\n", - "alpha_new = [Events.Events(values=[rem_mob,max_mob,max_mob*i],days=days) for i in redrate]\n", - "\n", - "alpha_new_uk = [Events.Events(values=[rem_mob,max_mob,max_mob_UK*i],days=days) for i in redrate]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "t = np.array(np.arange(0,120,0.1))\n", - "alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - "alphaplot_UK= [[j(i) for i in t] for j in alpha_new_uk]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - } - ], - "source": [ - "for f in alphaplot:\n", - " plt.plot(t,f)\n", - "for f in alphaplot_UK:\n", - " plt.plot(t,f,linestyle='dashed') \n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0)\n", - "plt.title('Mobility-beta',fontsize=20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 0.4\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 62#(fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.6\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (plebiscito-initdate).days -3\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "#chi = [chi0]\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Navidad\n", - "increasedays = 2\n", - "renewalFactor = 0.65\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "\n", - "chi1 = SeroPrevDynamics(navidad_day,navidad_day+increasedays*0.8,navidad_day+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "#chi = Events.functionAddition([chi0,chi1])\n", - "chi.append(chi1)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Año nuevo\n", - "delay = 0\n", - "increasedays = 7\n", - "renewalFactor_ny = 0.25\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor_ny/increasedays\n", - "\n", - "chi2 = SeroPrevDynamics(newyear_day+delay,newyear_day+delay+increasedays*0.05,newyear_day+delay+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "\n", - "#renewalFactor = [renewalFactor]\n", - "chi.append(chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "chi = Events.functionAddition(chi)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [-0.5,0,1.35]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "delay = 0\n", - "today = datetime.today() #+ timedelta(days=30)\n", - "today_day = (today-initdate).days\n", - "# Increase date \n", - "#increasedate = today\n", - "#increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 15\n", - "# Daily amount of people\n", - "dailyincrease = [state.population*SeroPrevFactor*i/increasedays for i in renewalFactor]\n", - "\n", - "chi3 = [SeroPrevDynamics(today_day-2.5,today_day-2.5+delay+increasedays*0.0001,today_day-2.5+delay+increasedays,i) for i in dailyincrease]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi,i) for i in chi3]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Tasa Ingreso de Susceptibles',fontsize=20)\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad')\n", - "plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevp')\n", - "plt.axvline(x = today_day, linestyle = 'dotted',color = 'green',label='hoy')\n", - "\n", - "plt.xlim(0,75)\n", - "plt.tick_params(labelsize=18)\n", - "plt.legend(loc=0,fontsize=15)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "#alpha[0]\n", - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(alpha_new)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.9s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 1.9s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation UK\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.45" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new_uk[0](10)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "simulation_uk = [SEIRHVD(tsim,beta,mu,alpha_new_uk[i],k_I=k_I,k_R = k_R, chi = chi[i],\n", - " Htot=10000,Vtot=int(state.UCI_capacity[0]),H0=1000,V0=state.UCI_use_covid[0],B0=state.Br[0],D0=state.Dr[0]\n", - " ,R0=state.I_ac_r[0]-state.I_ac_r[0]*0.1,I0=state.Ir[0],I_d0=state.I_d_r[0],I_ac0=state.I_ac_r[0],\n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,population = state.population) for i in range(len(chi))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation_uk)):\n", - " simulation_uk[i].pE_Ias=pE_Ias\n", - " simulation_uk[i].tE_Ias=tE_Ias\n", - " simulation_uk[i].pE_Imi=pE_Imi\n", - " simulation_uk[i].tE_Imi=tE_Imi\n", - " simulation_uk[i].pE_Icr=pE_Icr\n", - " simulation_uk[i].tE_Icr=tE_Icr\n", - " simulation_uk[i].pE_Ise=pE_Ise\n", - " simulation_uk[i].tE_Ise=tE_Ise\n", - " simulation_uk[i].pIas_R=pIas_R\n", - " simulation_uk[i].tIas_R =tIas_R\n", - " simulation_uk[i].pImi_R=pImi_R\n", - " simulation_uk[i].tImi_R =tImi_R\n", - " simulation_uk[i].pIse_Hse=pIse_Hse\n", - " simulation_uk[i].tIse_Hse=tIse_Hse\n", - " simulation_uk[i].pIse_D=pIse_D\n", - " simulation_uk[i].tIse_D=tIse_D\n", - " simulation_uk[i].pIcr_V=pIcr_V\n", - " simulation_uk[i].tIcr_V=tIcr_V\n", - " simulation_uk[i].pIcr_D=pIcr_D\n", - " simulation_uk[i].tIcr_D=tIcr_D\n", - " simulation_uk[i].pHse_R=pHse_R\n", - " simulation_uk[i].tHse_R =tHse_R\n", - " simulation_uk[i].pHse_V=pHse_V\n", - " simulation_uk[i].tHse_V=tHse_V\n", - " simulation_uk[i].pHse_D=pHse_D\n", - " simulation_uk[i].tHse_D=tHse_D\n", - " simulation_uk[i].pV_Hout=pV_Hout\n", - " simulation_uk[i].tV_Hout =tV_Hout\n", - " simulation_uk[i].pV_D=pV_D\n", - " simulation_uk[i].tV_D =tV_D\n", - " simulation_uk[i].pHout_R=pHout_R\n", - " simulation_uk[i].tHout_R=tHout_R\n", - " simulation_uk[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 2.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 3 | elapsed: 2.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims_uk = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation_uk,i,tsim) for i in range(len(simulation_uk)))\n", - "simulation_uk = sims_uk\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "sim_UK = True" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "pĺotlinewidth = 4\n", - "datelinewidth = 3" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,linewidth=pĺotlinewidth,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if sim_UK:\n", - " axs[0,0].plot(dates[i],simulation_uk[i].I_ac_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - "axs[0,0].set_title('Infectados acumulados',fontsize=22)\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "#axs[0,0].set_ylim(0,1.5e6)\n", - "\n", - "\n", - "\n", - "axs[0,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r,color='black',label='Datos',zorder=10)\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[0,1].plot(dates[i],simulation_uk[i].B,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Muertes Acumuladas',fontsize=22)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "#axs[0,1].set_ylim(0,1e4)\n", - "\n", - "axs[0,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[0,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Datos',color='black',zorder=10)\n", - "\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if sim_UK:\n", - " axs[1,0].plot(dates[i],simulation_uk[i].I_d_det,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,0].set_title('Nuevos Infectados Diarios',fontsize=22)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "#axs[1,0].set_ylim(0,1e4)\n", - "\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Datos',color='black',zorder=10)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,0].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "#axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "#axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "#axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if sim_UK:\n", - " axs[1,1].plot(dates[i],simulation_uk[i].V,color=colors[i],linestyle='dashed',linewidth=pĺotlinewidth) \n", - "axs[1,1].set_title('Uso UCI',fontsize=22)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "#axs[1,1].set_ylim(0,3e3)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='Datos',color='black',zorder=10)\n", - "#axs[1,1].plot(state.UCI_dates,state.UCI_capacity,label='UCI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap,label='UCI Capacity', linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "#axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,25), linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2020,12,31), linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - "axs[1,1].axvline(x = datetime(2021,1,15), linestyle = 'dotted',color = 'green',label='15 Enero',linewidth=datelinewidth)\n", - "\n", - "\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# Date format\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%d-%m-%Y')\n", - "\n", - "# Y axis\n", - "#axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=18)\n", - "axs[0,1].tick_params(labelsize=18)\n", - "axs[1,0].tick_params(labelsize=18)\n", - "axs[1,1].tick_params(labelsize=18)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc=0,fontsize=14)\n", - "axs[0,1].legend(loc=1,fontsize=14)\n", - "axs[1,0].legend(loc=1,fontsize=14)\n", - "axs[1,1].legend(loc=1,fontsize=14)\n", - "\n", - "\n", - "fig.suptitle(state.name,fontsize=25)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('stop')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta*rem_mob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Susceptibles')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].S,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].S,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - " \n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%matplotlib tk\n", - "plt.title('Infectados Activos')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.axvline(x=newyear,color='blue',label='año nuevo')\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - } - ], - "source": [ - "#%matplotlib tk\n", - "plt.title('SeroPrevalence')\n", - "for i in range(len(simulation)):\n", - " plt.plot(simulation[i].dates,simulation[i].I_ac/simulation[i].population,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " #plt.plot(simulation[i].dates,simulation[i].chi,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "#plt.scatter(state.Ir_dates,state.Ir)\n", - "for i in range(len(simulation_uk)):\n", - " plt.plot(simulation_uk[i].dates,simulation_uk[i].I_ac/simulation_uk[i].population,linestyle='dashed',label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "\n", - "plt.gca().xaxis.set_major_formatter( mdates.DateFormatter('%Y-%m-%d'))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Ir[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/Delta_14-07.ipynb b/DataFit/Delta_14-07.ipynb deleted file mode 100644 index f265438..0000000 --- a/DataFit/Delta_14-07.ipynb +++ /dev/null @@ -1,1444 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lo ideal sería tener una proyección de nivel país suponiendo el número de personas susceptibles que informamos en nuestro reporte de hace un par de semanas.\n", - "Recuerda que Delta tiene un R_0 de entre 6 y 8\n", - "Suponiendo además que tenemos 10 personas infectadas con Delta al día 12 de julio\n", - "usando tres escenarios: movilidad del 100% durante el resto del año, reducción de movilidad al 65% remanente desde el 28 de agosto y hasta el final de la simulación, y movilidad remanente del 20% al 28 de agosto y hasta el final.\n", - "\n", - "5 millones de susceptibles" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samuel/.local/lib/python3.8/site-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (4.0.0) doesn't match a supported version!\n", - " warnings.warn(\"urllib3 ({}) or chardet ({}) doesn't match a supported \"\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD6 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData\n", - "import Events\n", - "\n", - "import json\n", - "from pandas.core.base import DataError\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "import pandas as pd\n", - "import matplotlib\n", - "\n", - "from datetime import date\n", - "\n", - "import copy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2021,7,1)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,7,25)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days\n", - "\n", - "navidad = datetime(2020,12,25)\n", - "navidad_day = (navidad-initdate).days\n", - "\n", - "newyear = datetime(2020,12,31)\n", - "newyear_day = (newyear-initdate).days\n", - "\n", - "today = datetime.today()\n", - "today_day = (today-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. state region is represented by cut = 14." - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "def dataretriever(user=None,password=None):\n", - " # Esta funcion devolvera una funcion de request \n", - " def request(endpoint):\n", - " if user:\n", - " aux = requests.get('https://api.cv19gm.org/'+endpoint, auth=HTTPBasicAuth(user, password))\n", - " if aux.status_code == 401:\n", - " raise Exception('Wrong Credentials')\n", - " else:\n", - " print('Logged in successfully')\n", - " else:\n", - " aux = requests.get('http://192.168.2.223:5006/'+endpoint)\n", - " return aux\n", - " return request\n", - "def timeJStoPy(t):\n", - " return datetime.strptime(t[:10],'%Y-%m-%d')\n", - "def timetxttoDatetime(t):\n", - " return datetime.strptime(t,'%Y-%m-%d')" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing data from all regions\n", - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data from Minciencia\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n", - "Importing Daily Infected\n" - ] - } - ], - "source": [ - "print('Importing data from all regions')\n", - "#tstate = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16']\n", - "tstate = '13'\n", - "# Import Data\n", - "state = ImportData(tstate=tstate,initdate = initdate)\n", - "state.importdata()\n", - "\n", - "state.importDailyInfectedNacional()\n", - "state.name = 'Chile'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "capacidadUCIcovid = 2600\n", - "state.Hr = 1000\n", - "state.Hr_tot = [10000,10000]\n", - "state.Vr = state.UCI_use_covid\n", - "state.Vr_tot = capacidadUCIcovid - np.array(state.UCI_use_noncovid)#[4000]*len(state.UCI_use_covid)#state.UCI_capacity\n", - "state.UCI_non_covid = state.UCI_use_noncovid\n", - "state.sochimi_tr = state.UCI_tr#[-2:]\n", - "state.sochimi_dates = state.UCI_dates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Infectados Diarios y Acumulados" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint ='https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto3/TotalesPorRegion.csv'\n", - "producto3 = pd.read_csv(endpoint)\n", - "I_ac_confirmado = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')]\n", - "I_antigeno_ac = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados por antigeno')]\n", - "I_probables_ac = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos probables acumulados')]" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "confirmado_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos acumulados')].iloc[:,2:].sum()\n", - "antigeno_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos confirmados por antigeno')].iloc[:,2:].sum()\n", - "probables_nac = producto3[(producto3['Region']=='Total')&(producto3['Categoria']=='Casos probables acumulados')].iloc[:,2:].sum()\n", - "\n", - "I_ac_nac = confirmado_nac+antigeno_nac+probables_nac\n", - "I_act_nac_roll = I_ac_nac.diff(periods=11).rolling(7).mean().dropna()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "I_act_nac_roll.plot(label='confirmados+antigenos+probables')\n", - "confirmado_nac.diff(periods=11).rolling(7).mean().dropna().plot(label='confirmados')\n", - "plt.title('Activos')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "R = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados recuperados')].iloc[0][3:]\n", - "R.index = pd.to_datetime(R.index, format='%Y-%m-%d')\n", - "R = R.loc[R.index>=initdate]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "initday = (initdate-pd.to_datetime(I_ac_confirmado.iloc[0].index[2], format='%Y-%m-%d')).days\n", - "#I_ac = I_ac_confirmado.iloc[0][initday:] + I_antigeno_ac.iloc[0][initday:] + I_probables_ac.iloc[0][initday:]\n", - "I_ac = I_ac_confirmado.iloc[0][3:] + I_antigeno_ac.iloc[0][3:] + I_probables_ac.iloc[0][3:]\n", - "I_ac.index = pd.to_datetime(I_ac.index, format='%Y-%m-%d')\n", - "\n", - "I_d = I_ac.diff(periods=1)\n", - "I_act = I_ac.diff(periods=11)\n", - "I_d_roll = I_d.rolling(7).mean()\n", - "I_act_roll = I_act.rolling(7).mean()\n", - "I_d = I_d.loc[I_d.index>=initdate]\n", - "I_act = I_act.loc[I_act.index>=initdate]\n", - "I_ac = I_ac.loc[I_ac.index>=initdate]\n", - "\n", - "\n", - "I_d_roll = I_d_roll.loc[I_d_roll.index>=initdate]\n", - "I_act_roll = I_act_roll.loc[I_act_roll.index>=initdate]\n", - "\n", - "state.I_d_r = I_d_roll\n", - "state.I_d_r_dates = I_d_roll.index\n", - "state.I_d_r_tr = [(I_d_roll.index[i]-initdate).days for i in range(len(I_d_roll))]\n", - "\n", - "state.Ir = I_act_roll\n", - "state.Ir_dates = I_act_roll.index\n", - "state.tr = [(I_act_roll.index[i]-initdate).days for i in range(len(I_act_roll))]\n", - "\n", - "state.I_ac_r = I_ac\n", - "state.I_ac_r_dates = I_ac.index\n", - "state.I_ac_r_tr = [(I_ac.index[i]-initdate).days for i in range(len(I_ac))]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "state.R = R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importante:\n", - "valores al 26 de Abril\n", - "Casos activos calculados con probables y antigenos: 38657\n", - "Casos activos confirmados publicados: 15810\n", - "Casos activos calculado solo con los casos acumulados: 28170\n", - "\n", - "Activos_confirmados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos activos confirmados')]\n", - "Activos_desdeacumulados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')].iloc[0][initday:].diff(periods=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "Ias_det = 0.3#0.1\n", - "Imi_det = 1#0.4\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.375#5 #list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.127]\n", - "\n", - "escenarios = [50,60,70,80]\n", - "alpha_var = escenarios\n", - "#alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "alpha_new = [lambda t:0.127]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b,c):\n", - " def aux(t):\n", - " return a(t)+b(t)+c(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "chi = aux" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = 0\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,7,21)-initdate).days\n", - "chi = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [], - "source": [ - "#chi = chisum(chi0,chi1,chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "#chiplot = [chi(t) for t in np.arange(0,tsim,0.1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(chiplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.30 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.04 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.11 # Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 12.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 11.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 11.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 7.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 7.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.7 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 11.0\n", - "pV_D = 0.3 # Transition from Ventilators to Death\n", - "tV_D = 11.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo medio Infeccioso" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias+pE_Imi+pE_Ise+pE_Icr" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.69" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias*tIas_R + pE_Imi*tImi_R + pE_Ise*tIse_Hse + pE_Icr*tIcr_V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "simulation[0].integr_sci(0,1000,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.4s\n", - "[Parallel(n_jobs=12)]: Done 1 out of 1 | elapsed: 2.4s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,8,1)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2024, 3, 26, 0, 0)" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation[0].dates[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 10\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [], - "source": [ - "# Español\n", - "title = 'Proyección Situación Epidemiológica RM - May 26th'\n", - "plottitle = [['Infectados Activos Detectados','Fallecidos Acumulados (PCR+)'],\n", - " ['Nuevos Infectados Diarios Detectados','Uso UCI Covid']]\n", - "mob = 'Movilidad'" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "# English\n", - "title = 'Projection of epidemiological situation in the Metropolitan Region - May 26th'\n", - "plottitle = [['Active detected infected','Accumulated deaths (PCR+)'],\n", - " ['New daily detected infected','ICU covid use']]\n", - "mob = 'Mobility'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "rango = -1\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label=mob+': '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title(plottitle[0][0],fontsize=fontsize_title)\n", - "\n", - "axs[0,0].scatter(state.Ir_dates[:rango],state.Ir[:rango],color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label=mob+': '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title(plottitle[0][1],fontsize=fontsize_title)\n", - "\n", - "axs[0,1].scatter(state.Br_dates[:rango],state.Br[:rango],label='DP50',color='grey',zorder=10)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label=mob+': '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title(plottitle[1][0],fontsize=fontsize_title)\n", - "\n", - "\n", - "if False:#sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - "\n", - "#axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='DP5',color='black',zorder=5)\n", - "axs[1,0].scatter(state.I_d_r_dates[:rango],I_d_roll[:rango],label='DP3 med_mov 7',color='grey',zorder=5)\n", - " \n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,capacidadUCIcovid-np.array(state.UCI_use_noncovid),label='COVID ICU use limit',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label=mob+': '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title(plottitle[1][1],fontsize=fontsize_title)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates[:rango],state.UCI_use_covid[:rango],label='DP58',color='grey',zorder=10)\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " \n", - "axs[0,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[0,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "# x axis: Date range\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "\n", - "# x axis: Date interval\n", - "interval = 30\n", - "#axs[0,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[0,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "\n", - "# x axis: Date format\n", - "axs[0,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[0,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "\n", - "# Y axis: Format\n", - "axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Y axis: Range\n", - "#axs[0,0].set_ylim(0,5e4)\n", - "axs[0,1].set_ylim(0,50000)\n", - "#axs[1,0].set_ylim(0,10000)\n", - "#axs[1,1].set_ylim(0,2.5e3)\n", - "#axs[0,0].set_yscale('log')\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "#axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Activos" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(1, 1)\n", - "\n", - "axs.scatter(state.Ir_dates,state.Ir,color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs.plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs.plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='Simulation',color=colors[i])\n", - "axs.set_title('Infectados Activos Detectados',fontsize=fontsize_title)\n", - "\n", - "\n", - "# Grid\n", - "axs.grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# x axis: Date range\n", - "axs.set_xlim(initdate,enddate)\n", - "\n", - "\n", - "# x axis: Date interval\n", - "interval = 30\n", - "\n", - "# x axis: Date format\n", - "axs.get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "\n", - "# Y axis: Format\n", - "axs.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Y axis: Range\n", - "axs.set_ylim(0,70000)\n", - "\n", - "# Font-size\n", - "axs.tick_params(labelsize=fontsize_label)\n", - "# Legend\n", - "axs.legend(loc='lower right',fontsize=fontsize_legend)\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "daterange = [initdate_table+timedelta(days=7*i) for i in range(int((enddate-initdate_table).days/7)+1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "int((enddate-initdate).days/7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "initdate_table = datetime(2021,5,1)\n", - "enddate_table = datetime(2021,12,31)\n", - "\n", - "init_index = np.where(np.array(dates[0]) == np.array(initdate_table))[0][0]\n", - "end_index = np.where(np.array(dates[0]) == np.array(enddate_table))[0][0] + 1 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.around(np.array([1.1,2.5,8.9])).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_d_dict = [{'Infectados_diarios_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_act_dict = [{'Infectados_activos_mov_'+str(escenarios[i]):np.around(simulation[i].I_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "D_acc_dict = [{'Fallecidos_acumulados_mov_'+str(escenarios[i]):np.around(simulation[i].B[init_index:end_index]).astype(int)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_d_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_act_table = {'fechas':dates[0][init_index:end_index]}\n", - "D_acc_table = {'fechas':dates[0][init_index:end_index]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i}\n", - "for i in I_d_dict: \n", - " I_d_table = {**I_d_table, **i} \n", - "for i in I_act_dict: \n", - " I_act_table = {**I_act_table, **i} \n", - "for i in D_acc_dict:\n", - " D_acc_table = {**D_acc_table, **i}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)\n", - "I_d_df = pd.DataFrame(I_d_table)\n", - "I_act_df = pd.DataFrame(I_act_table)\n", - "D_acc_df = pd.DataFrame(D_acc_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "D_acc_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_may-dic_0206.csv')\n", - "I_d_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_diarios_may-dic_0206.csv')\n", - "I_act_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_activos_may-dic_0206.csv')\n", - "D_acc_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/D_accu_may-dic_0206.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "I_act_nac_roll.plot(label='confirmados+antigenos+probables')\n", - "confirmado_nac.diff(periods=11).rolling(7).mean().dropna().plot(label='confirmados')\n", - "plt.title('Activos')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "R = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados recuperados')].iloc[0][3:]\n", - "R.index = pd.to_datetime(R.index, format='%Y-%m-%d')\n", - "R = R.loc[R.index>=initdate]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "initday = (initdate-pd.to_datetime(I_ac_confirmado.iloc[0].index[2], format='%Y-%m-%d')).days\n", - "#I_ac = I_ac_confirmado.iloc[0][initday:] + I_antigeno_ac.iloc[0][initday:] + I_probables_ac.iloc[0][initday:]\n", - "I_ac = I_ac_confirmado.iloc[0][3:] + I_antigeno_ac.iloc[0][3:] + I_probables_ac.iloc[0][3:]\n", - "I_ac.index = pd.to_datetime(I_ac.index, format='%Y-%m-%d')\n", - "\n", - "I_d = I_ac.diff(periods=1)\n", - "I_act = I_ac.diff(periods=11)\n", - "I_d_roll = I_d.rolling(7).mean()\n", - "I_act_roll = I_act.rolling(7).mean()\n", - "I_d = I_d.loc[I_d.index>=initdate]\n", - "I_act = I_act.loc[I_act.index>=initdate]\n", - "I_ac = I_ac.loc[I_ac.index>=initdate]\n", - "\n", - "\n", - "I_d_roll = I_d_roll.loc[I_d_roll.index>=initdate]\n", - "I_act_roll = I_act_roll.loc[I_act_roll.index>=initdate]\n", - "\n", - "state.I_d_r = I_d_roll\n", - "state.I_d_r_dates = I_d_roll.index\n", - "state.I_d_r_tr = [(I_d_roll.index[i]-initdate).days for i in range(len(I_d_roll))]\n", - "\n", - "state.Ir = I_act_roll\n", - "state.Ir_dates = I_act_roll.index\n", - "state.tr = [(I_act_roll.index[i]-initdate).days for i in range(len(I_act_roll))]\n", - "\n", - "state.I_ac_r = I_ac\n", - "state.I_ac_r_dates = I_ac.index\n", - "state.I_ac_r_tr = [(I_ac.index[i]-initdate).days for i in range(len(I_ac))]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "state.R = R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importante:\n", - "valores al 26 de Abril\n", - "Casos activos calculados con probables y antigenos: 38657\n", - "Casos activos confirmados publicados: 15810\n", - "Casos activos calculado solo con los casos acumulados: 28170\n", - "\n", - "Activos_confirmados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos activos confirmados')]\n", - "Activos_desdeacumulados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')].iloc[0][initday:].diff(periods=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Ias_det = 0.3#0.1\n", - "Imi_det = 1#0.4\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.375#5 #list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2021,4,2),datetime(2021,5,6),datetime(2021,6,9),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.127,0.11,0.1265]\n", - "\n", - "escenarios = [50,60,70,80]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[0, 18], [18, 52], [52, 86], [86, 1000]]" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mob_days" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0635" - ] - }, - "execution_count": 179, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new[0](0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b,c):\n", - " def aux(t):\n", - " return a(t)+b(t)+c(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "chi = aux" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = -0.1\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,4,2)-initdate).days\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 9\n", - "renewalFactor = -0.33\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,4,11)-initdate).days\n", - "chi1 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = 0.3\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,5,3)-initdate).days\n", - "chi2 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [], - "source": [ - "chi = chisum(chi0,chi1,chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [chi(t) for t in np.arange(0,tsim,0.1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(chiplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.30 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.04 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.11 # Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 12.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 11.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 11.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 7.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 7.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.7 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 11.0\n", - "pV_D = 0.3 # Transition from Ventilators to Death\n", - "tV_D = 11.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo medio Infeccioso" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 191, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias+pE_Imi+pE_Ise+pE_Icr" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.69" - ] - }, - "execution_count": 192, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias*tIas_R + pE_Imi*tImi_R + pE_Ise*tIse_Hse + pE_Icr*tIcr_V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "simulation[0].integr_sci(0,1000,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.6s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 4 | elapsed: 1.9s remaining: 1.9s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 4 | elapsed: 3.5s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 4 | elapsed: 3.5s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,8,1)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2023, 12, 9, 0, 0)" - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation[0].dates[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 10\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": {}, - "outputs": [], - "source": [ - "# Español\n", - "title = 'Proyección Situación Epidemiológica RM - May 26th'\n", - "plottitle = [['Infectados Activos Detectados','Fallecidos Acumulados (PCR+)'],\n", - " ['Nuevos Infectados Diarios Detectados','Uso UCI Covid']]\n", - "mob = 'Movilidad'" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [], - "source": [ - "# English\n", - "title = 'Projection of epidemiological situation in the Metropolitan Region - May 26th'\n", - "plottitle = [['Active detected infected','Accumulated deaths (PCR+)'],\n", - " ['New daily detected infected','ICU covid use']]\n", - "mob = 'Mobility'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "rango = -1\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label=mob+': '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title(plottitle[0][0],fontsize=fontsize_title)\n", - "\n", - "axs[0,0].scatter(state.Ir_dates[:rango],state.Ir[:rango],color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label=mob+': '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title(plottitle[0][1],fontsize=fontsize_title)\n", - "\n", - "axs[0,1].scatter(state.Br_dates[:rango],state.Br[:rango],label='DP50',color='grey',zorder=10)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label=mob+': '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title(plottitle[1][0],fontsize=fontsize_title)\n", - "\n", - "\n", - "if False:#sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - "\n", - "#axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='DP5',color='black',zorder=5)\n", - "axs[1,0].scatter(state.I_d_r_dates[:rango],I_d_roll[:rango],label='DP3 med_mov 7',color='grey',zorder=5)\n", - " \n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,capacidadUCIcovid-np.array(state.UCI_use_noncovid),label='COVID ICU use limit',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label=mob+': '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title(plottitle[1][1],fontsize=fontsize_title)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates[:rango],state.UCI_use_covid[:rango],label='DP58',color='grey',zorder=10)\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " \n", - "axs[0,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[0,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "# x axis: Date range\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "\n", - "# x axis: Date interval\n", - "interval = 30\n", - "#axs[0,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[0,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "\n", - "# x axis: Date format\n", - "axs[0,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[0,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "\n", - "# Y axis: Format\n", - "axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Y axis: Range\n", - "#axs[0,0].set_ylim(0,5e4)\n", - "axs[0,1].set_ylim(0,50000)\n", - "#axs[1,0].set_ylim(0,10000)\n", - "#axs[1,1].set_ylim(0,2.5e3)\n", - "#axs[0,0].set_yscale('log')\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='upper left',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "#axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Activos" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(1, 1)\n", - "\n", - "axs.scatter(state.Ir_dates,state.Ir,color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs.plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs.plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='Simulation',color=colors[i])\n", - "axs.set_title('Infectados Activos Detectados',fontsize=fontsize_title)\n", - "\n", - "\n", - "# Grid\n", - "axs.grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "\n", - "# x axis: Date range\n", - "axs.set_xlim(initdate,enddate)\n", - "\n", - "\n", - "# x axis: Date interval\n", - "interval = 30\n", - "\n", - "# x axis: Date format\n", - "axs.get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "\n", - "# Y axis: Format\n", - "axs.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Y axis: Range\n", - "axs.set_ylim(0,70000)\n", - "\n", - "# Font-size\n", - "axs.tick_params(labelsize=fontsize_label)\n", - "# Legend\n", - "axs.legend(loc='lower right',fontsize=fontsize_legend)\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "daterange = [initdate_table+timedelta(days=7*i) for i in range(int((enddate-initdate_table).days/7)+1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "int((enddate-initdate).days/7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "initdate_table = datetime(2021,5,1)\n", - "enddate_table = datetime(2021,12,31)\n", - "\n", - "init_index = np.where(np.array(dates[0]) == np.array(initdate_table))[0][0]\n", - "end_index = np.where(np.array(dates[0]) == np.array(enddate_table))[0][0] + 1 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.around(np.array([1.1,2.5,8.9])).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_d_dict = [{'Infectados_diarios_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_act_dict = [{'Infectados_activos_mov_'+str(escenarios[i]):np.around(simulation[i].I_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "D_acc_dict = [{'Fallecidos_acumulados_mov_'+str(escenarios[i]):np.around(simulation[i].B[init_index:end_index]).astype(int)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_d_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_act_table = {'fechas':dates[0][init_index:end_index]}\n", - "D_acc_table = {'fechas':dates[0][init_index:end_index]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i}\n", - "for i in I_d_dict: \n", - " I_d_table = {**I_d_table, **i} \n", - "for i in I_act_dict: \n", - " I_act_table = {**I_act_table, **i} \n", - "for i in D_acc_dict:\n", - " D_acc_table = {**D_acc_table, **i}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)\n", - "I_d_df = pd.DataFrame(I_d_table)\n", - "I_act_df = pd.DataFrame(I_act_table)\n", - "D_acc_df = pd.DataFrame(D_acc_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "D_acc_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_may-dic_0206.csv')\n", - "I_d_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_diarios_may-dic_0206.csv')\n", - "I_act_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_activos_may-dic_0206.csv')\n", - "D_acc_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/D_accu_may-dic_0206.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "I_act_nac_roll.plot(label='confirmados+antigenos+probables')\n", - "confirmado_nac.diff(periods=11).rolling(7).mean().dropna().plot(label='confirmados')\n", - "plt.title('Activos')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "R = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos confirmados recuperados')].iloc[0][3:]\n", - "R.index = pd.to_datetime(R.index, format='%Y-%m-%d')\n", - "R = R.loc[R.index>=initdate]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "initday = (initdate-pd.to_datetime(I_ac_confirmado.iloc[0].index[2], format='%Y-%m-%d')).days\n", - "#I_ac = I_ac_confirmado.iloc[0][initday:] + I_antigeno_ac.iloc[0][initday:] + I_probables_ac.iloc[0][initday:]\n", - "I_ac = I_ac_confirmado.iloc[0][3:] + I_antigeno_ac.iloc[0][3:] + I_probables_ac.iloc[0][3:]\n", - "I_ac.index = pd.to_datetime(I_ac.index, format='%Y-%m-%d')\n", - "\n", - "I_d = I_ac.diff(periods=1)\n", - "I_act = I_ac.diff(periods=11)\n", - "I_d_roll = I_d.rolling(7).mean()\n", - "I_act_roll = I_act.rolling(7).mean()\n", - "I_d = I_d.loc[I_d.index>=initdate]\n", - "I_act = I_act.loc[I_act.index>=initdate]\n", - "I_ac = I_ac.loc[I_ac.index>=initdate]\n", - "\n", - "\n", - "I_d_roll = I_d_roll.loc[I_d_roll.index>=initdate]\n", - "I_act_roll = I_act_roll.loc[I_act_roll.index>=initdate]\n", - "\n", - "state.I_d_r = I_d_roll\n", - "state.I_d_r_dates = I_d_roll.index\n", - "state.I_d_r_tr = [(I_d_roll.index[i]-initdate).days for i in range(len(I_d_roll))]\n", - "\n", - "state.Ir = I_act_roll\n", - "state.Ir_dates = I_act_roll.index\n", - "state.tr = [(I_act_roll.index[i]-initdate).days for i in range(len(I_act_roll))]\n", - "\n", - "state.I_ac_r = I_ac\n", - "state.I_ac_r_dates = I_ac.index\n", - "state.I_ac_r_tr = [(I_ac.index[i]-initdate).days for i in range(len(I_ac))]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "state.R = R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importante:\n", - "valores al 26 de Abril\n", - "Casos activos calculados con probables y antigenos: 38657\n", - "Casos activos confirmados publicados: 15810\n", - "Casos activos calculado solo con los casos acumulados: 28170\n", - "\n", - "Activos_confirmados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos activos confirmados')]\n", - "Activos_desdeacumulados = producto3[(producto3['Region']=='Metropolitana')&(producto3['Categoria']=='Casos acumulados')].iloc[0][initday:].diff(periods=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Subreport" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "Ias_det = 0.3#0.1\n", - "Imi_det = 1#0.4\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "beta = 1\n", - "mu = 0.375#5 #list(np.arange(0.2,0.4,0.01))#0.4\n", - "k_I = 0\n", - "k_R = 0#10\n", - "SeroPrevFactor = 1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "mob_dates = [initdate,datetime(2021,4,2),datetime(2021,5,3),initdate+timedelta(days=tsim)]\n", - "mob_days = []\n", - "for i in range(len(mob_dates)-1):\n", - " d1 = (mob_dates[i]-initdate).days\n", - " d2 = (mob_dates[i+1]-initdate).days\n", - " mob_days.append([d1,d2])\n", - "\n", - "mobility = [0.127,0.11]\n", - "\n", - "escenarios = [60,80,90,100,110,120]\n", - "alpha_var = escenarios\n", - "alpha_new = [Events.Events(values=mobility +[i*mobility[-1]/100],days=mob_days) for i in escenarios]\n", - "\n", - "title = 'Proyección Situación Epidemiológica RM'" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0635" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alpha_new[0](0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Movilidad" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "if False:\n", - " datelinewidth = 1\n", - " t = np.array(np.arange(0,240,0.1))\n", - " alphaplot= [[j(i) for i in t] for j in alpha_new]\n", - " for f in alphaplot:\n", - " plt.plot(t,f)\n", - " #for f in alphaplot_UK:\n", - " #plt.plot(t,f,linestyle='dashed') \n", - " plt.axvline(x = navidad_day, linestyle = 'dotted',color = 'red',label='Navidad',linewidth=datelinewidth)\n", - " plt.axvline(x = newyear_day, linestyle = 'dotted',color = 'blue',label='Año Nuevo',linewidth=datelinewidth)\n", - " #plt.axvline(x = mitadvacaciones_day, linestyle = 'dotted',color = 'green',label='15 Febrero',linewidth=datelinewidth)\n", - "\n", - " plt.tick_params(labelsize=18)\n", - " plt.legend(loc=0)\n", - " plt.title('Mobility-beta',fontsize=20)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b,c):\n", - " def aux(t):\n", - " return a(t)+b(t)+c(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "def aux(t):\n", - " return 0\n", - "chi = aux" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "chi = []" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = -0.1\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,4,2)-initdate).days\n", - "chi0 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 9\n", - "renewalFactor = -0.33\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,4,11)-initdate).days\n", - "chi1 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = 10\n", - "renewalFactor = 0.3\n", - "dailyincrease = state.population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = (datetime(2021,5,3)-initdate).days\n", - "chi2 = SeroPrevDynamics(t_sp_temp,t_sp_temp+increasedays*0.8,t_sp_temp+increasedays,dailyincrease,form='line')" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "chi = chisum(chi0,chi1,chi2)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "chiplot = [chi(t) for t in np.arange(0,tsim,0.1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(chiplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Susceptible Dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# State transition parameters:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#state parameters: \n", - "pE_Ias = 0.43 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.53 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.0065 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.0335# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 5.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 5.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.30 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 3.0\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected\n", - "tE_Imi = 3.0\n", - "pE_Icr = 0.04 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.11 # Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 12.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 11.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 11.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 11.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 11.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.7 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 12.0\n", - "pV_D = 0.3 # Transition from Ventilators to Death\n", - "tV_D = 11.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo medio Infeccioso" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias+pE_Imi+pE_Ise+pE_Icr" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.85" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pE_Ias*tIas_R + pE_Imi*tImi_R + pE_Ise*tIse_Hse + pE_Icr*tIcr_V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha_new[i],k_I=k_I,k_R = k_R, chi = chi, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate,\n", - " Imi_det = Imi_det,Ias_det = Ias_det,RealIC=state) for i in range(len(alpha_new))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "simulation[0].integr_sci(0,1000,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.7s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 2.7s remaining: 5.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 2.7s remaining: 2.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 3.7s remaining: 1.8s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 4.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 4.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "#%matplotlib inline\n", - "%matplotlib tk\n", - "# Big Screen\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #19.20,10.80#8,6\n", - "# Small Screen\n", - "#plt.rcParams[\"figure.figsize\"] = 19.2, 10.80 #19.20,10.80#8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = 50\n", - "\n", - "enddate = initdate + timedelta(days=365)\n", - "enddate = datetime(2021,6,30)\n", - "\n", - "\n", - "dates = [[initdate+timedelta(days=int(i)) for i in simulation[j].t] for j in range(len(simulation))]\n", - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2023, 12, 9, 0, 0)" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation[0].dates[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pantalla" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "pantalla = '1080'\n", - "if pantalla == '4k':\n", - " fontsize_title = 22\n", - " fontsize_suptitle = 25\n", - " fontsize_label = 18\n", - " fontsize_legend = 14\n", - "\n", - "#1080\n", - "elif pantalla == '1080':\n", - " fontsize_title = 15\n", - " fontsize_suptitle = 16\n", - " fontsize_label = 10\n", - " fontsize_legend = 12\n", - "\n", - "pĺotlinewidth = 2\n", - "datelinewidth = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0]\n", - "sim_InfDiario = False\n", - "sim_InfEPI = True\n", - "\n", - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(simulation)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='mu: '+str(mu[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[0,0].plot(dates[i],simulation[i].I_det,linewidth=pĺotlinewidth,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i])\n", - "axs[0,0].set_title('Infectados Activos Detectados',fontsize=fontsize_title)\n", - "\n", - "axs[0,0].scatter(state.Ir_dates,state.Ir,color='grey',label='DP3 med_mov 7',zorder=10)\n", - "\n", - "\n", - "# Accumulated deaths\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - " \n", - "axs[0,1].set_title('Fallecidos Acumulados (PCR+)',fontsize=fontsize_title)\n", - "\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='DP50',color='grey',zorder=10)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " if type(alpha_new) == list:\n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='Movilidad: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,0].set_title('Nuevos Infectados Diarios Detectados',fontsize=fontsize_title)\n", - "\n", - "\n", - "if False:#sim_InfEPI:\n", - " axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='Informe EPI',color='black',zorder=5)\n", - "if sim_InfDiario:\n", - " axs[1,0].scatter(I_week_ra_dates,I_week_ra,label='Informe diario (RA5D)',color='red',zorder=5) \n", - " axs[1,0].plot(dates_I_d,I_d,label='Informe diario',color='blue',zorder=10,linestyle='dashed')\n", - "\n", - "#axs[1,0].scatter(state.I_d_r_dates,state.I_d_r,label='DP5',color='black',zorder=5)\n", - "axs[1,0].scatter(state.I_d_r_dates,I_d_roll,label='DP3 med_mov 7',color='grey',zorder=5)\n", - " \n", - "\n", - "axs[1,0].legend(loc='upper left')\n", - "\n", - "axs[1,1].plot(state.UCI_dates,capacidadUCIcovid-np.array(state.UCI_use_noncovid),label='Limite uso COVID',color='black',zorder=10)\n", - "for i in range(len(simulation)):\n", - " if type(mu) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='mu: '+str(mu[i]),color=colors[i],linewidth=pĺotlinewidth)\n", - " #axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - " if type(alpha_new) == list:\n", - " axs[1,1].plot(dates[i],simulation[i].V,label='Mov: '+str(escenarios[i])+'%',color=colors[i],linewidth=pĺotlinewidth)\n", - "axs[1,1].set_title('Uso UCI Covid',fontsize=fontsize_title)\n", - "\n", - "axs[1,1].scatter(state.UCI_dates,state.UCI_use_covid,label='DP58',color='grey',zorder=10)\n", - "axs[1,1].plot(simulation[0].dates,simulation[0].V_cap, linestyle = 'dashed', color = 'black')\n", - "\n", - "\n", - "# Ephemerides\n", - "for i in mob_dates:\n", - " axs[0,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[0,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,0].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " axs[1,1].axvline(x = i, linestyle = 'dashed',linewidth=datelinewidth)\n", - " \n", - "axs[0,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[0,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[0,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,0].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,0].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "axs[1,1].axvline(x = datetime(2021,4,11), linestyle = 'dashed',linewidth=datelinewidth,color='red')\n", - "axs[1,1].axvline(x = datetime(2021,5,3), linestyle = 'dashed',linewidth=datelinewidth,color='grey')\n", - "\n", - "# Format and shit\n", - "\n", - "# Grid\n", - "axs[0,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[0,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,0].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "axs[1,1].grid(color='grey', linestyle='dotted', linewidth=0.5)\n", - "\n", - "# x axis: Date range\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "\n", - "# x axis: Date interval\n", - "interval = 30\n", - "#axs[0,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[0,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,0].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "#axs[1,1].xaxis.set_major_locator(mdates.DayLocator(interval=interval))\n", - "\n", - "# x axis: Date format\n", - "axs[0,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[0,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,0].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "axs[1,1].get_xaxis().set_major_formatter(mdates.DateFormatter('%d-%m'))\n", - "\n", - "# Y axis: Format\n", - "axs[0,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[0,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,0].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "axs[1,1].get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n", - "\n", - "# Y axis: Range\n", - "#axs[0,0].set_ylim(0,5e4)\n", - "#axs[0,1].set_ylim(0,30000)\n", - "#axs[1,0].set_ylim(0,10000)\n", - "#axs[1,1].set_ylim(0,2.5e3)\n", - "#axs[0,0].set_yscale('log')\n", - "\n", - "# Font-size\n", - "axs[0,0].tick_params(labelsize=fontsize_label)\n", - "axs[0,1].tick_params(labelsize=fontsize_label)\n", - "axs[1,0].tick_params(labelsize=fontsize_label)\n", - "axs[1,1].tick_params(labelsize=fontsize_label)\n", - "\n", - "# Legend\n", - "axs[0,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[0,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,0].legend(loc='lower right',fontsize=fontsize_legend)\n", - "axs[1,1].legend(loc='lower right',fontsize=fontsize_legend)\n", - "#axs[1,1].legend(bbox_to_anchor=(0.33, 0.52),fontsize=fontsize_legend)\n", - "\n", - "\n", - "fig.suptitle(title,fontsize=fontsize_suptitle)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3731.1428571428573" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.I_d_r[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "daterange = [initdate_table+timedelta(days=7*i) for i in range(int((enddate-initdate_table).days/7)+1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "41" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "int((enddate-initdate).days/7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de tablas" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "initdate_table = datetime(2021,5,1)\n", - "enddate_table = datetime(2021,12,31)\n", - "\n", - "init_index = np.where(np.array(dates[0]) == np.array(initdate_table))[0][0]\n", - "end_index = np.where(np.array(dates[0]) == np.array(enddate_table))[0][0] + 1 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construir DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 9])" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.around(np.array([1.1,2.5,8.9])).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_dict = [{'Uso_UCI_mov_'+str(escenarios[i]):np.around(simulation[i].V[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_d_dict = [{'Infectados_diarios_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "I_act_dict = [{'Infectados_activos_mov_'+str(escenarios[i]):np.around(simulation[i].I_det[init_index:end_index]).astype(int)} for i in range(len(escenarios))]\n", - "D_acc_dict = [{'Fallecidos_acumulados_mov_'+str(escenarios[i]):np.around(simulation[i].B[init_index:end_index]).astype(int)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_d_table = {'fechas':dates[0][init_index:end_index]}\n", - "I_act_table = {'fechas':dates[0][init_index:end_index]}\n", - "D_acc_table = {'fechas':dates[0][init_index:end_index]}\n", - "for i in vmi_dict:\n", - " vmi_table = {**vmi_table, **i}\n", - "for i in I_d_dict: \n", - " I_d_table = {**I_d_table, **i} \n", - "for i in I_act_dict: \n", - " I_act_table = {**I_act_table, **i} \n", - "for i in D_acc_dict:\n", - " D_acc_table = {**D_acc_table, **i}" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df = pd.DataFrame(vmi_table)\n", - "I_d_df = pd.DataFrame(I_d_table)\n", - "I_act_df = pd.DataFrame(I_act_table)\n", - "D_acc_df = pd.DataFrame(D_acc_table)" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " fechas Fallecidos_acumulados_mov_60 Fallecidos_acumulados_mov_80 \\\n", - "0 2021-05-01 14561 14561 \n", - "1 2021-05-02 14645 14645 \n", - "2 2021-05-03 14726 14726 \n", - "3 2021-05-04 14805 14805 \n", - "4 2021-05-05 14879 14880 \n", - ".. ... ... ... \n", - "240 2021-12-27 17611 20262 \n", - "241 2021-12-28 17611 20268 \n", - "242 2021-12-29 17611 20274 \n", - "243 2021-12-30 17612 20279 \n", - "244 2021-12-31 17612 20284 \n", - "\n", - " Fallecidos_acumulados_mov_90 Fallecidos_acumulados_mov_100 \\\n", - "0 14561 14561 \n", - "1 14645 14645 \n", - "2 14726 14726 \n", - "3 14805 14805 \n", - "4 14880 14881 \n", - ".. ... ... \n", - "240 23939 55784 \n", - "241 23961 55869 \n", - "242 23983 55950 \n", - "243 24005 56029 \n", - "244 24027 56105 \n", - "\n", - " Fallecidos_acumulados_mov_110 Fallecidos_acumulados_mov_120 \n", - "0 14561 14561 \n", - "1 14645 14645 \n", - "2 14726 14726 \n", - "3 14805 14805 \n", - "4 14881 14882 \n", - ".. ... ... \n", - "240 134720 216257 \n", - "241 135068 216576 \n", - "242 135410 216885 \n", - "243 135744 217182 \n", - "244 136072 217470 \n", - "\n", - "[245 rows x 7 columns]" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "D_acc_df" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [], - "source": [ - "vmi_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/UCI_may-dic.csv')\n", - "I_d_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_diarios_may-dic.csv')\n", - "I_act_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/I_activos_may-dic.csv')\n", - "D_acc_df.to_csv('/home/samuel/Dropbox/DLab/Covid/DataProjections/Chile/D_accu_may-dic.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### infectadios diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "plt.plot(dates[i],simulation[i].I_d)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_dict = [{'Id_mov_'+str(escenarios[i]):np.around(simulation[i].I_d_det)} for i in range(len(escenarios))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_table = {'fechas':dates[0]}\n", - "for i in Id_dict:\n", - " Id_table = {**Id_table, **i} \n", - "Id_df = pd.DataFrame(Id_table)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Id_10may = Id_df[(Id_df['fechas']>=datetime(2021,5,10)) & (Id_df['fechas']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 5, 15, 0, 0)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'ImportData' object has no attribute 'sochimi_tr'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimulation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mSEIRHVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtsim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_I\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk_I\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_R\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_R\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeroPrevFactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSeroPrevFactor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mexpinfection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexpinfection\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRealIC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mImi_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImi_det\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mIas_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIas_det\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimulation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mSEIRHVD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtsim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_I\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk_I\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk_R\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_R\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeroPrevFactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSeroPrevFactor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mexpinfection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexpinfection\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRealIC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mImi_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImi_det\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mIas_det\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIas_det\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/covid19geomodeller/src/SEIRHVD/class_SEIRHUVD5.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, tsim, beta, mu, alpha, k, chi, k_I, k_R, Htot, Vtot, H0, V0, B0, D0, R0, I0, I_d0, I_ac0, SeroPrevFactor, expinfection, population, RealIC, initdate, Imi_det, Ias_det, Ise_det, Icr_det, SimIC)\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# Aproximate Hospitals capacity:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m \u001b[0mHcmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msochimi_tr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHr_tot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 108\u001b[0m \u001b[0mtsat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msochimi_tr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0mHmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHr_tot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'ImportData' object has no attribute 'sochimi_tr'" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = state,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'simulation' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpE_Ias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpE_Ias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtE_Ias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtE_Ias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpE_Imi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpE_Imi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtE_Imi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtE_Imi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'simulation' is not defined" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plot" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=2*365)\n", - "enddate = datetime(2020,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "plt.title('Accumulated Infected')\n", - "plt.xlim(initdate,enddate)\n", - "plt.scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "plt.axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "plt.axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "plt.axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "plt.plot([], [], ' ', label='RF: RenewalFactor')\n", - "plt.plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "plt.plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "plt.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp\n", - "\n", - "enddate = initdate + timedelta(days=2*365)\n", - "enddate = datetime(2020,12,31)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].scatter(state.I_ac_r_dates,state.I_ac_r)\n", - "#axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[0,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[0,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[0,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "axs[0,1].scatter(state.Br_dates,state.Br,label='Muertes Totales')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_hosp,label='Muertes Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,label='Muertes No Hospitalizados')\n", - "#axs[0,1].scatter(state.hosp_dates,state.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "#axs[0,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "#axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[0,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[0,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,0].plot(dates[i],simulation[i].I_d_det,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,0].set_title('New Daily Infected')\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "axs[1,0].scatter(state.I_d_r_dates,state.I_d_r)\n", - "\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "\n", - "axs[1,0].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[1,0].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[1,0].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - " axs[1,1].plot(dates[i],simulation[i].V_need,linestyle='dashed',color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "axs[1,1].scatter(state.sochimi_dates,state.Vr)\n", - "#axs[1,1].scatter(state.sochimi_dates,state.VMI_confirmado,color = 'red')\n", - "axs[1,1].scatter(state.sochimi_dates,np.array(state.VMI_sospechoso)+np.array(state.VMI_confirmado),color = 'red')\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,1].axvline(x = datetime(2020,8,22), linestyle = 'dotted',color = 'grey',label='Inicio Cuarentena')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "\n", - "axs[1,1].axvline(x = datetime(2020,7,18), linestyle = 'dotted',color = 'grey',label='Cambio a fase 3')\n", - "axs[1,1].axvline(x = datetime(2020,9,11), linestyle = 'dashed',color = 'grey',label='Cambio a fase 4')\n", - "axs[1,1].axvline(x = datetime(2020,10,3), linestyle = 'dashdot',color = 'grey',label='Cambio a fase 2')\n", - "\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "fig.suptitle(state.name,fontsize=16)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(simulation[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lockdown data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = '../Data/Confinamiento_COVID19.xlsx'\n", - "Data = pd.read_excel(endpoint,header=1,index_col=0)\n", - "valdivia = Data['14101'].iloc[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(valdivia.astype(int)) \n", - "ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d') \n", - "fig.suptitle(state.name+' Lockdown',fontsize=16)\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "state.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(state.sochimi_tr,state.Vr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(state.sochimi_tr,state.Hr,label='Real Data')\n", - "plt.scatter(state.sochimi_tr,state.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(state.Ir==max(state.Ir))[0][0]\n", - "datapeakdate = state.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/SEIRHVD5_DataFit_SeroPrevDynamics.ipynb b/DataFit/SEIRHVD5_DataFit_SeroPrevDynamics.ipynb deleted file mode 100644 index 84aad6a..0000000 --- a/DataFit/SEIRHVD5_DataFit_SeroPrevDynamics.ipynb +++ /dev/null @@ -1,1047 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,8,24)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "plebiscito = datetime(2020,10,25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. RM region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "tstate = '13'\n", - "\n", - "# Import Data\n", - "RM = ImportData(tstate=tstate,initdate = initdate)\n", - "RM.importdata()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#RM.Hr = np.array(RM.UCI) + np.array(RM.UTI)\n", - "#RM.Hr_tot = np.array(RM.UCI_tot) + np.array(RM.UTI_tot)\n", - "RM.Hr = np.array(RM.UTI)\n", - "RM.Hr_tot = np.array(RM.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "\n", - "t_sp = (SPchange_date - initdate).days \n", - "\n", - "\n", - "beta = 0.12\n", - "mu = 1.6\n", - "k_I = 0\n", - "k_R = 0\n", - "\n", - "SeroPrevFactor =0.07\n", - "\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.6\n", - "rem_mob = 0.45\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.15\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pE_Ias = 0.428 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 4.0\n", - "\n", - "pE_Imi = 0.528 # Transition from exposed to Mild Infected \n", - "tE_Imi = 4.0\n", - "\n", - "pE_Icr = 0.012 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.032# Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 9.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 12.0\n", - "\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 5.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.95 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 3.0\n", - "\n", - "pHse_V = 0.05 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 5.0\n", - "\n", - "pHse_D = 1# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 5.0 \n", - "\n", - "pV_Hout = 0.4 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 18.0\n", - "\n", - "pV_D = 0.6 # Transition from Ventilators to Death \n", - "tV_D = 12.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 20.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "t_sp = 100" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "renewalFactor=0.6\n", - "initincrease=0\n", - "increasedays =initincrease+30\n", - "# Daily amount of people \n", - "dailyincrease = RM.population*SeroPrevFactor*renewalFactor/increasedays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case we add 30.000 persons to the simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "renewalFactor=0.6\n", - "initincrease=0\n", - "increasedays =initincrease+30\n", - "# Daily amount of people\n", - "dailyincrease = RM.population*SeroPrevFactor*renewalFactor/increasedays" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "chi = SeroPrevDynamics(t_sp,t_sp+increasedays,dailyincrease)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation1 = SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = chi, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = RM,Imi_det = Imi_det,Ias_det = Ias_det)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "simulation1.pE_Ias=pE_Ias\n", - "simulation1.tE_Ias=tE_Ias\n", - "simulation1.pE_Imi=pE_Imi\n", - "simulation1.tE_Imi=tE_Imi\n", - "simulation1.pE_Icr=pE_Icr\n", - "simulation1.tE_Icr=tE_Icr\n", - "simulation1.pE_Ise=pE_Ise\n", - "simulation1.tE_Ise=tE_Ise\n", - "simulation1.pIas_R=pIas_R\n", - "simulation1.tIas_R =tIas_R\n", - "simulation1.pImi_R=pImi_R\n", - "simulation1.tImi_R =tImi_R\n", - "simulation1.pIse_Hse=pIse_Hse\n", - "simulation1.tIse_Hse=tIse_Hse\n", - "simulation1.pIse_D=pIse_D\n", - "simulation1.tIse_D=tIse_D\n", - "simulation1.pIcr_V=pIcr_V\n", - "simulation1.tIcr_V=tIcr_V\n", - "simulation1.pIcr_D=pIcr_D\n", - "simulation1.tIcr_D=tIcr_D\n", - "simulation1.pHse_R=pHse_R\n", - "simulation1.tHse_R =tHse_R\n", - "simulation1.pHse_V=pHse_V\n", - "simulation1.tHse_V=tHse_V\n", - "simulation1.pHse_D=pHse_D\n", - "simulation1.tHse_D=tHse_D\n", - "simulation1.pV_Hout=pV_Hout\n", - "simulation1.tV_Hout =tV_Hout\n", - "simulation1.pV_D=pV_D\n", - "simulation1.tV_D =tV_D\n", - "simulation1.pHout_R=pHout_R\n", - "simulation1.tHout_R=tHout_R\n", - "simulation1.setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " message: 'The solver successfully reached the end of the integration interval.'\n", - " nfev: 2179\n", - " njev: 43\n", - " nlu: 43\n", - " sol: None\n", - " status: 0\n", - " success: True\n", - " t: array([0.00000000e+00, 1.56694148e-08, 3.13388296e-08, 1.56725487e-04,\n", - " 3.13419635e-04, 4.70113783e-04, 2.03705527e-03, 3.60399675e-03,\n", - " 5.17093823e-03, 6.73787971e-03, 2.24072945e-02, 3.80767094e-02,\n", - " 5.37461242e-02, 6.94155390e-02, 8.50849538e-02, 2.41779102e-01,\n", - " 3.98473250e-01, 5.55167398e-01, 7.11861547e-01, 8.68555695e-01,\n", - " 1.02524984e+00, 1.44087685e+00, 1.85650387e+00, 2.27213088e+00,\n", - " 2.68775789e+00, 3.10338490e+00, 3.51901191e+00, 3.93463892e+00,\n", - " 4.35026593e+00, 4.89743615e+00, 5.44460636e+00, 5.99177658e+00,\n", - " 6.53894679e+00, 7.06374454e+00, 7.58854228e+00, 8.11334003e+00,\n", - " 8.99801066e+00, 9.88268129e+00, 1.07673519e+01, 1.18320746e+01,\n", - " 1.28967973e+01, 1.39615200e+01, 1.54240483e+01, 1.68865765e+01,\n", - " 1.83491047e+01, 1.90511571e+01, 1.90950354e+01, 1.91389136e+01,\n", - " 1.91827919e+01, 1.91849858e+01, 1.91871797e+01, 1.91915676e+01,\n", - " 1.91959554e+01, 1.91990658e+01, 1.92015997e+01, 1.92041337e+01,\n", - " 1.92059486e+01, 1.92077636e+01, 1.92092301e+01, 1.92106966e+01,\n", - " 1.92121631e+01, 1.92139426e+01, 1.92157221e+01, 1.92175016e+01,\n", - " 1.92192811e+01, 1.92214060e+01, 1.92235309e+01, 1.92256558e+01,\n", - " 1.92277807e+01, 1.92299056e+01, 1.92327462e+01, 1.92355869e+01,\n", - " 1.92384275e+01, 1.92412681e+01, 1.92441088e+01, 1.92470588e+01,\n", - " 1.92500089e+01, 1.92529590e+01, 1.92559091e+01, 1.92586969e+01,\n", - " 1.92614848e+01, 1.92642727e+01, 1.92670136e+01, 1.92697545e+01,\n", - " 1.92724954e+01, 1.92866638e+01, 1.93008321e+01, 1.93150005e+01,\n", - " 1.93632991e+01, 1.94115977e+01, 1.94598963e+01, 1.95752797e+01,\n", - " 1.96906632e+01, 1.98060466e+01, 1.99214300e+01, 2.01045778e+01,\n", - " 2.02877256e+01, 2.04708734e+01, 2.06540212e+01, 2.09651559e+01,\n", - " 2.12762905e+01, 2.15874251e+01, 2.18985598e+01, 2.22096944e+01,\n", - " 2.26680901e+01, 2.31264857e+01, 2.35848814e+01, 2.40432771e+01,\n", - " 2.45016727e+01, 2.52260095e+01, 2.59503463e+01, 2.66746831e+01,\n", - " 2.73990198e+01, 2.81233566e+01, 3.00507122e+01, 3.19780678e+01,\n", - " 3.39054235e+01, 3.58327791e+01, 3.92895845e+01, 4.27463900e+01,\n", - " 4.62031954e+01, 4.96600009e+01, 5.31168063e+01, 5.70382463e+01,\n", - " 6.09596863e+01, 6.48811262e+01, 6.88025662e+01, 7.27240062e+01,\n", - " 7.27297686e+01, 7.27412934e+01, 7.27528183e+01, 7.27643431e+01,\n", - " 7.27758679e+01, 7.27873928e+01, 7.28574588e+01, 7.28605939e+01,\n", - " 7.28637289e+01, 7.28668639e+01, 7.28731340e+01, 7.28794041e+01,\n", - " 7.28856742e+01, 7.29483749e+01, 7.30110756e+01, 7.30737763e+01,\n", - " 7.31364770e+01, 7.34083941e+01, 7.36803111e+01, 7.39522282e+01,\n", - " 7.42241453e+01, 7.44960624e+01, 7.49077529e+01, 7.53194435e+01,\n", - " 7.57311341e+01, 7.61428247e+01, 7.65545152e+01, 7.69662058e+01,\n", - " 7.73778964e+01, 7.77895869e+01, 7.82012775e+01, 7.86129681e+01,\n", - " 7.90246587e+01, 7.94363492e+01, 7.98480398e+01, 8.02597304e+01,\n", - " 8.06714209e+01, 8.10831115e+01, 8.14948021e+01, 8.19064927e+01,\n", - " 8.23181832e+01, 8.27298738e+01, 8.31415644e+01, 8.35532550e+01,\n", - " 8.39649455e+01, 8.43766361e+01, 8.47883267e+01, 8.52000172e+01,\n", - " 8.56500172e+01, 8.61000172e+01, 8.65500172e+01, 8.70000172e+01,\n", - " 8.74500172e+01, 8.80000172e+01, 8.85500172e+01, 8.91000172e+01,\n", - " 8.96500172e+01, 9.02250172e+01, 9.08000172e+01, 9.13750172e+01,\n", - " 9.19500172e+01, 9.25250172e+01, 9.31000172e+01, 9.36750172e+01,\n", - " 9.42500172e+01, 9.72275554e+01, 1.00205093e+02, 1.00970041e+02,\n", - " 1.00985644e+02, 1.00993952e+02, 1.00999991e+02, 1.01006030e+02,\n", - " 1.01012068e+02, 1.01018107e+02, 1.01024146e+02, 1.01036224e+02,\n", - " 1.01048302e+02, 1.01060379e+02, 1.01181156e+02, 1.01301933e+02,\n", - " 1.01422710e+02, 1.01722463e+02, 1.02022217e+02, 1.02321970e+02,\n", - " 1.02621724e+02, 1.02972483e+02, 1.03323242e+02, 1.03674001e+02,\n", - " 1.04024760e+02, 1.04375519e+02, 1.05012076e+02, 1.05648634e+02,\n", - " 1.06285191e+02, 1.06921748e+02, 1.07558306e+02, 1.08658529e+02,\n", - " 1.09758753e+02, 1.10858977e+02, 1.11959200e+02, 1.13731222e+02,\n", - " 1.15503243e+02, 1.17275264e+02, 1.19047285e+02, 1.21390221e+02,\n", - " 1.23733156e+02, 1.26076092e+02, 1.28419028e+02, 1.31498549e+02,\n", - " 1.34578070e+02, 1.37657591e+02, 1.40737112e+02, 1.44750202e+02,\n", - " 1.48763292e+02, 1.52776382e+02, 1.56789472e+02, 1.61799714e+02,\n", - " 1.66809955e+02, 1.70941199e+02, 1.75072442e+02, 1.79203685e+02,\n", - " 1.83334929e+02, 1.86775117e+02, 1.90215305e+02, 1.93655493e+02,\n", - " 1.97095681e+02, 1.97602823e+02, 1.98109966e+02, 1.98617108e+02,\n", - " 1.99124250e+02, 2.00138535e+02, 2.01152820e+02, 2.02167105e+02,\n", - " 2.05677370e+02, 2.08037502e+02, 2.10397634e+02, 2.12757766e+02,\n", - " 2.16604588e+02, 2.20451411e+02, 2.23455176e+02, 2.26458941e+02,\n", - " 2.29462706e+02, 2.32466472e+02, 2.36514352e+02, 2.40562232e+02,\n", - " 2.44610112e+02, 2.48657992e+02, 2.52705872e+02, 2.56753752e+02,\n", - " 2.60801632e+02, 2.65336509e+02, 2.69871386e+02, 2.74406263e+02,\n", - " 2.78941140e+02, 2.83476016e+02, 2.88861803e+02, 2.94247590e+02,\n", - " 2.99633376e+02, 3.05019163e+02, 3.10404950e+02, 3.15790736e+02,\n", - " 3.21176523e+02, 3.26562310e+02, 3.31948096e+02, 3.37333883e+02,\n", - " 3.42719669e+02, 3.48105456e+02, 3.53491243e+02, 3.58877029e+02,\n", - " 3.65669073e+02, 3.72461116e+02, 3.79253159e+02, 3.86045202e+02,\n", - " 3.92837245e+02, 3.97877375e+02, 4.02917504e+02, 4.07957633e+02,\n", - " 4.12997763e+02, 4.18037892e+02, 4.24285366e+02, 4.30532839e+02,\n", - " 4.31748399e+02, 4.32963960e+02, 4.34179520e+02, 4.35395080e+02,\n", - " 4.37826201e+02, 4.40257322e+02, 4.42688443e+02, 4.48623021e+02,\n", - " 4.54557598e+02, 4.60492176e+02, 4.73183222e+02, 4.83186058e+02,\n", - " 4.93188894e+02, 5.03191730e+02, 5.13194567e+02, 5.24523913e+02,\n", - " 5.35853260e+02, 5.47182607e+02, 5.58511954e+02, 5.74145935e+02,\n", - " 5.89779916e+02, 6.05413898e+02, 6.21047879e+02, 6.36681860e+02,\n", - " 6.60199728e+02, 6.83717597e+02, 7.07235465e+02, 7.30753333e+02,\n", - " 7.65009183e+02, 7.99265033e+02, 8.33520883e+02, 9.25283427e+02,\n", - " 1.00000000e+03])\n", - " t_events: None\n", - " y: array([[5.15496155e+05, 5.15496155e+05, 5.15496155e+05, ...,\n", - " 3.98987471e+01, 3.98983926e+01, 3.98982674e+01],\n", - " [3.17937755e+04, 3.17937755e+04, 3.17937754e+04, ...,\n", - " 3.94223700e+00, 3.94219823e+00, 3.94218458e+00],\n", - " [4.33658179e+03, 4.33658173e+03, 4.33658168e+03, ...,\n", - " 5.20164191e-05, 5.20152257e-05, 5.20148049e-05],\n", - " ...,\n", - " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", - " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", - " [0.00000000e+00, 5.90736939e-07, 1.18147388e-06, ...,\n", - " 7.16453512e+03, 7.16561865e+03, 7.16650091e+03]])\n", - " y_events: None" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation1.integr_sci(0,tsim,0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import timedelta" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = t_sp\n", - "dates = [initdate+timedelta(days=i) for i in simulation1.t]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = simulation1.I_ac[-1]/simulation1.population\n", - "SeroPrevalenceDet = simulation1.I_ac_det[-1]/simulation1.population" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].plot(dates,simulation1.I_ac_det)\n", - "axs[0,0].scatter(RM.I_ac_r_dates,RM.I_ac_r)\n", - "axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence,2))+'%')\n", - "axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet,2))+'%')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "axs[0,1].set_title('Hospitalized Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].plot(dates,simulation1.B)\n", - "axs[0,1].scatter(RM.hosp_dates,RM.Br_hosp)\n", - "axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "axs[1,0].set_title('UTI Usage')\n", - "axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,0].plot(dates,simulation1.Hse+simulation1.Hout)\n", - "axs[1,0].scatter(RM.sochimi_dates,RM.UTI)\n", - "axs[1,0].plot(dates,simulation1.H_cap,label='UTI Capacity', color = 'red')\n", - "axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].plot(dates,simulation1.V)\n", - "axs[1,1].scatter(RM.sochimi_dates,RM.Vr)\n", - "axs[1,1].plot(dates,simulation1.V_cap,label='VMI Capacity', color = 'red')\n", - "axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.052377995456964255" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": 953, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 953, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 3)\n", - "axs[0,0].set_xlim(0,days)\n", - "axs[0,0].scatter(RM.tr,RM.Ir,label='Real Active Data')\n", - "axs[0,0].plot(simulation1.t,simulation1.I_det,label='Infected')\n", - "axs[0,0].set_title('Active Infected')\n", - "axs[0,0].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[0,1].set_xlim(0,days)\n", - "axs[0,1].plot(simulation1.t,simulation1.I_d_det,label='sim1')\n", - "axs[0,1].scatter(RM.I_d_r_tr,RM.I_d_r,label='Daily Real Data')\n", - "axs[0,1].set_title('Daily infected')\n", - "axs[0,1].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[0,2].set_xlim(0,days)\n", - "axs[0,2].plot(simulation1.t,simulation1.I_ac_det,label='sim1')\n", - "axs[0,2].scatter(RM.I_ac_r_tr,RM.I_ac_r,label='Accumulated Real Data')\n", - "axs[0,2].set_title('Accumulated Infected')\n", - "axs[0,2].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[1,0].set_xlim(0,days)\n", - "axs[1,0].plot(simulation1.t,simulation1.B,label='sim1')\n", - "axs[1,0].scatter(RM.hosp_tr,RM.Br_hosp,label='Real Data')\n", - "axs[1,0].set_title('Hospitalized Acmulated Deaths')\n", - "axs[1,0].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[1,1].set_xlim(0,days)\n", - "axs[1,1].plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UTI Beds')\n", - "axs[1,1].scatter(RM.sochimi_tr,RM.UCI,label='Real Data')\n", - "axs[1,1].plot(simulation1.t,simulation1.H_cap,label='Capacity', color = 'red')\n", - "axs[1,1].set_title('UTI Usage')\n", - "axs[1,1].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[1,2].set_xlim(0,days)\n", - "axs[1,2].plot(simulation1.t,simulation1.V,label='Vsat sim1')\n", - "axs[1,2].scatter(RM.sochimi_tr,RM.Vr,label='Real Data')\n", - "axs[1,2].plot(simulation1.t,simulation1.V_cap,label='Capacity Data', color = 'red')\n", - "axs[1,2].set_title('VMI Usage')\n", - "axs[1,2].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": 926, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": 841, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": 842, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": 843, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(RM.sochimi_tr,RM.Hr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(RM.Ir==max(RM.Ir))[0][0]\n", - "datapeakdate = RM.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/SEIRHVD5_Datafit_SPDynamics_Parallel.ipynb b/DataFit/SEIRHVD5_Datafit_SPDynamics_Parallel.ipynb deleted file mode 100644 index 1f5a7e5..0000000 --- a/DataFit/SEIRHVD5_Datafit_SPDynamics_Parallel.ipynb +++ /dev/null @@ -1,1341 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "SPchange_date = datetime(2020,8,14)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n", - "\n", - "fiestaspatrias = datetime(2020,9,18)\n", - "fiestaspatrias_day = (fiestaspatrias-initdate).days\n", - "plebiscito = datetime(2020,10,25)\n", - "plebiscito_day = (plebiscito-initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. RM region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing Sochimi Data 2\n", - "Importing Accumulated Deaths\n", - "Importing Active Infected by Minciencia\n", - "Importing Hospitalized/NonHospitalized Deaths\n", - "Done\n" - ] - } - ], - "source": [ - "tstate = '13'\n", - "\n", - "# Import Data\n", - "RM = ImportData(tstate=tstate,initdate = initdate)\n", - "RM.importdata()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "RM.Hr = np.array(RM.UTI)\n", - "RM.Hr_tot = np.array(RM.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 1000\n", - "t_sp = (SPchange_date - initdate).days\n", - "beta = 0.12\n", - "mu = 1.6\n", - "k_I = 0\n", - "k_R = 0\n", - "SeroPrevFactor =0.07\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.85\n", - "rem_mob = 0.45\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=t_sp)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.428 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.528 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.012 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.032# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.45 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.55 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### First Susceptibles increase 2020-08-24" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "increasedays = (fiestaspatrias - SPchange_date).days + 0\n", - "renewalFactor = 0.23\n", - "dailyincrease = RM.population*SeroPrevFactor*renewalFactor/increasedays\n", - "chi0 = SeroPrevDynamics(t_sp+5,t_sp+increasedays*0.7,t_sp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [0.15,0.3,0.45,0.6,0.75]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Days for susceptible increase\n", - "increasedays = 50\n", - "# Daily amount of people\n", - "dailyincrease = [RM.population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(fiestaspatrias_day-4,fiestaspatrias_day+increasedays*0.0001,fiestaspatrias_day+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chiplot = [[chi[i](t) for t in np.arange(0,tsim,0.1)] for i in range(len(renewalFactor))]\n", - "plt.title('Sero Prevalence Dynamics')\n", - "for i in range(len(chiplot)):\n", - " plt.plot(np.arange(0,tsim,0.1),chiplot[i],label='RenewalFactor: '+str(renewalFactor[i]))\n", - "plt.axvline(x = t_sp, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias_day, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito_day, linestyle = 'dotted',color = 'blue',label='Plebiscito') \n", - "plt.xlim(0,200)\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2020, 5, 15, 0, 0)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RM.I_ac_r_dates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n", - "InitialCondition Object Data\n" - ] - } - ], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,RealIC = RM,Imi_det = Imi_det,Ias_det = Ias_det) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.9s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 3.0s remaining: 4.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 3.1s remaining: 2.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 4.4s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 4.4s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 500\n", - "t_end = t_sp" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(renewalFactor)))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "dates = [[initdate+timedelta(days=i) for i in simulation[j].t] for j in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plt.title('VMI Usage')\n", - "plt.xlim(initdate,datetime(2020,12,31))\n", - "plt.scatter(RM.sochimi_dates,RM.Vr)\n", - "plt.scatter(RM.sochimi_dates,RM.Vr_tot)\n", - "plt.plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RM.Br_dates" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(dates[i],simulation[i].I_ac_det,label='RF: '+str(renewalFactor[i])+' | SP_R: '+str(round(100*SeroPrevalence[i],2))+'%'+' | SP_D:'+str(round(100*SeroPrevalenceDet[i],2))+'%',color=colors[i])\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,0].scatter(RM.I_ac_r_dates,RM.I_ac_r)\n", - "axs[0,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence: '+str(round(100*SeroPrevalence[i],2))+'%')\n", - "#axs[0,0].plot([], [], ' ', label='SeroPrevalence Detected: '+str(round(100*SeroPrevalenceDet[i],2))+'%')\n", - "axs[0,0].plot([], [], ' ', label='RF: RenewalFactor')\n", - "axs[0,0].plot([], [], ' ', label='SP_R: SeroPrevalence Real')\n", - "axs[0,0].plot([], [], ' ', label='SP_D: SeroPrevalence Detected')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(dates[i],simulation[i].B,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[0,1].set_title('Hospitalized Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[0,1].scatter(RM.Br_dates,RM.Br,label='Muertes')\n", - "#axs[0,1].scatter(RM.hosp_dates,RM.Br_Nonhosp,color = 'red',label='Muertes No Hospitalizados')\n", - "axs[0,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[0,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[0,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "#for i in range(len(simulation)): \n", - "# axs[1,0].plot(dates[i],simulation[i].Hse+simulation[i].Hout,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "#axs[1,0].set_title('UTI Usage')\n", - "#axs[1,0].set_xlim(initdate,datetime(2020,12,31))\n", - "#axs[1,0].scatter(RM.sochimi_dates,RM.UTI)\n", - "#axs[1,0].plot(dates[0],simulation[0].H_cap,label='UTI Capacity', linestyle = 'dashed', color = 'grey')\n", - "#axs[1,0].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "#axs[1,0].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "#axs[1,0].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "#axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(RM.sochimi_dates,RM.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.05713125351823005,\n", - " 0.0635882201789234,\n", - " 0.07003900986540046,\n", - " 0.07652593403702519,\n", - " 0.08294049715684915]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SeroPrevalenceDet" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'Figure' object is not callable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'RenewalFactor: '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenewalFactor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'VMI Usage'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitdate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: 'Figure' object is not callable" - ] - } - ], - "source": [ - "fig(1,1)\n", - "for i in range(len(simulation)): \n", - " axs[1,1].plot(dates[i],simulation[i].V,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "axs[1,1].set_title('VMI Usage')\n", - "axs[1,1].set_xlim(initdate,datetime(2020,12,31))\n", - "axs[1,1].scatter(RM.sochimi_dates,RM.Vr)\n", - "axs[1,1].plot(dates[0],simulation[0].V_cap,label='VMI Capacity', linestyle = 'dashed', color = 'grey')\n", - "axs[1,1].axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "axs[1,1].axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "axs[1,1].axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "axs[1,1].legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de Peak " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = [np.where(np.array(dates[i])>datetime(2020,9,1))[0][0] for i in range(len(renewalFactor))] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].I_d,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily new infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id = [dates[i][np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_Id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d = [simulation[i].I_d[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI = [simulation[i].V[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D = [simulation[i].D[np.where(simulation[i].I_d==max(simulation[i].I_d[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_I_d_D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VMIneed = [[simulation[i].V_d[j] + simulation[i].Icr_D_d[j] for j in range(len(simulation[i].V))] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],VMIneed[i],label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('New VMI daily need')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI = [dates[i][np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI = [simulation[i].V[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d = [simulation[i].I_d[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_I_d " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d = [dates[i][np.where(VMIneed[i]==max(VMIneed[i][idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakdates_VMI_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D = [simulation[i].D[np.where(simulation[i].V==max(simulation[i].V[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_VMI_D " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RM.Dr_hosp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Deaths\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D = [simulation[i].D[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_dates = [dates[i][np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_VMI = [simulation[i].V[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]\n", - "peak_D_I_d = [simulation[i].I_d[np.where(simulation[i].D==max(simulation[i].D[idx[i]:]))[0][0]] for i in range(len(renewalFactor))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_VMI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak_D_I_d\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(simulation)):\n", - " plt.plot(dates[i],simulation[i].D,label='RenewalFactor: '+str(renewalFactor[i]),color=colors[i])\n", - "plt.axvline(x = SPchange_date, linestyle = 'dotted',color = 'grey',label='Inicio Apertura')\n", - "plt.axvline(x = fiestaspatrias, linestyle = 'dotted',color = 'red',label='18 Sept')\n", - "plt.axvline(x = plebiscito, linestyle = 'dotted',color = 'blue',label='Plebiscito')\n", - "plt.title('Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIRD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.S,label='S')\n", - "plt.plot(simulation1.t,simulation1.E,label='E')\n", - "plt.plot(simulation1.t,simulation1.I,label='I')\n", - "plt.plot(simulation1.t,simulation1.R,label='R')\n", - "plt.plot(simulation1.t,simulation1.B,label='B')\n", - "plt.axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "plt.title('SEIRD')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(RM.sochimi_tr,RM.Hr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(RM.Ir==max(RM.Ir))[0][0]\n", - "datapeakdate = RM.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/SEIRHVD_DataFit_SeroPrev.ipynb b/DataFit/SEIRHVD_DataFit_SeroPrev.ipynb deleted file mode 100644 index 6a358de..0000000 --- a/DataFit/SEIRHVD_DataFit_SeroPrev.ipynb +++ /dev/null @@ -1,878 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - " elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac') \n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/SEIRHVD/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "\n", - "from class_SEIRHUVD4 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,5,15)\n", - "# Date for change in SeroPrevalence\n", - "initdate2 = datetime(2020,8,24)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data\n", - "To import data we use an ImportData object which is initialized with the region's cut and the initial date. RM region is represented by cut = 13." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "tstate = '13'\n", - "\n", - "# Import Data\n", - "RM = ImportData(tstate=tstate,initdate = initdate)\n", - "RM.importdata()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Datos de Hospitalizados a fitear" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#RM.Hr = np.array(RM.UCI) + np.array(RM.UTI)\n", - "#RM.Hr_tot = np.array(RM.UCI_tot) + np.array(RM.UTI_tot)\n", - "RM.Hr = np.array(RM.UTI)\n", - "RM.Hr_tot = np.array(RM.UTI_tot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsimtot = 1000\n", - "\n", - "tsim1 = (initdate2 - initdate).days \n", - "tsim2 = tsimtot-tsim1\n", - "\n", - "beta = 0.13\n", - "mu = 1.2\n", - "k = 0\n", - "\n", - "SeroPrevFactor1=0.06\n", - "SeroPrevFactor2=0.025\n", - "\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsim1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantines \n", - "max_mob = 0.55\n", - "rem_mob = 0.45\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=tsim1)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q2.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 0.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pE_Ias = 0.414 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.016 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.02 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 12.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 12.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation1 = SEIRHVD(tsim1,beta,mu,alpha,k=k,SeroPrevFactor=SeroPrevFactor1,expinfection=expinfection,RealIC = RM,Imi_det = Imi_det,Ias_det = Ias_det)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation1.pE_Ias=pE_Ias\n", - "simulation1.tE_Ias=tE_Ias\n", - "simulation1.pE_Imi=pE_Imi\n", - "simulation1.tE_Imi=tE_Imi\n", - "simulation1.pE_Icr=pE_Icr\n", - "simulation1.tE_Icr=tE_Icr\n", - "simulation1.pE_Ise=pE_Ise\n", - "simulation1.tE_Ise=tE_Ise\n", - "simulation1.pIas_R=pIas_R\n", - "simulation1.tIas_R =tIas_R\n", - "simulation1.pImi_R=pImi_R\n", - "simulation1.tImi_R =tImi_R\n", - "simulation1.pIse_Hse=pIse_Hse\n", - "simulation1.tIse_Hse=tIse_Hse\n", - "simulation1.pIse_D=pIse_D\n", - "simulation1.tIse_D=tIse_D\n", - "simulation1.pIcr_V=pIcr_V\n", - "simulation1.tIcr_V=tIcr_V\n", - "simulation1.pIcr_D=pIcr_D\n", - "simulation1.tIcr_D=tIcr_D\n", - "simulation1.pHse_R=pHse_R\n", - "simulation1.tHse_R =tHse_R\n", - "simulation1.pHse_V=pHse_V\n", - "simulation1.tHse_V=tHse_V\n", - "simulation1.pHse_D=pHse_D\n", - "simulation1.tHse_D=tHse_D\n", - "simulation1.pV_Hout=pV_Hout\n", - "simulation1.tV_Hout =tV_Hout\n", - "simulation1.pV_D=pV_D\n", - "simulation1.tV_D =tV_D\n", - "simulation1.pHout_R=pHout_R\n", - "simulation1.tHout_R=tHout_R\n", - "simulation1.setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation1.integr_sci(0,tsim1,0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation 2\n", - "Second SeroPrev period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation2 = SEIRHVD(tsim2,beta,mu,alpha,k=k,SeroPrevFactor=SeroPrevFactor2,Imi_det = Imi_det,Ias_det = Ias_det,SimIC=simulation1,initdate=initdate2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation2.pE_Ias=pE_Ias\n", - "simulation2.tE_Ias=tE_Ias\n", - "simulation2.pE_Imi=pE_Imi\n", - "simulation2.tE_Imi=tE_Imi\n", - "simulation2.pE_Icr=pE_Icr\n", - "simulation2.tE_Icr=tE_Icr\n", - "simulation2.pE_Ise=pE_Ise\n", - "simulation2.tE_Ise=tE_Ise\n", - "simulation2.pIas_R=pIas_R\n", - "simulation2.tIas_R =tIas_R\n", - "simulation2.pImi_R=pImi_R\n", - "simulation2.tImi_R =tImi_R\n", - "simulation2.pIse_Hse=pIse_Hse\n", - "simulation2.tIse_Hse=tIse_Hse\n", - "simulation2.pIse_D=pIse_D\n", - "simulation2.tIse_D=tIse_D\n", - "simulation2.pIcr_V=pIcr_V\n", - "simulation2.tIcr_V=tIcr_V\n", - "simulation2.pIcr_D=pIcr_D\n", - "simulation2.tIcr_D=tIcr_D\n", - "simulation2.pHse_R=pHse_R\n", - "simulation2.tHse_R =tHse_R\n", - "simulation2.pHse_V=pHse_V\n", - "simulation2.tHse_V=tHse_V\n", - "simulation2.pHse_D=pHse_D\n", - "simulation2.tHse_D=tHse_D\n", - "simulation2.pV_Hout=pV_Hout\n", - "simulation2.tV_Hout =tV_Hout\n", - "simulation2.pV_D=pV_D\n", - "simulation2.tV_D =tV_D\n", - "simulation2.pHout_R=pHout_R\n", - "simulation2.tHout_R=tHout_R\n", - "simulation2.setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run simulation 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation2.integr_sci(0,tsim2,0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 250\n", - "t_end = tsim1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(2, 3)\n", - "axs[0,0].set_xlim(0,days)\n", - "axs[0,0].scatter(RM.tr,RM.Ir,label='Real Active Data')\n", - "axs[0,0].plot(simulation1.t,simulation1.I_det,label='Infected')\n", - "axs[0,0].plot(simulation2.t+tsim1,simulation2.I_det,label='Infected')\n", - "axs[0,0].set_title('Active Infected')\n", - "axs[0,0].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[0,1].set_xlim(0,days)\n", - "axs[0,1].plot(simulation2.t+tsim1,simulation2.I_d_det,label='sim2')\n", - "axs[0,1].plot(simulation1.t,simulation1.I_d_det,label='sim1')\n", - "axs[0,1].scatter(RM.I_d_r_tr,RM.I_d_r,label='Daily Real Data')\n", - "axs[0,1].set_title('Daily infected')\n", - "axs[0,1].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[0,2].set_xlim(0,days)\n", - "axs[0,2].plot(simulation2.t+tsim1,simulation2.I_ac_det,label='sim2')\n", - "axs[0,2].plot(simulation1.t,simulation1.I_ac_det,label='sim1')\n", - "axs[0,2].scatter(RM.I_ac_r_tr,RM.I_ac_r,label='Accumulated Real Data')\n", - "axs[0,2].set_title('Accumulated Infected')\n", - "axs[0,2].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[1,0].set_xlim(0,days)\n", - "axs[1,0].plot(simulation2.t+tsim1,simulation2.B,label='sim2')\n", - "axs[1,0].plot(simulation1.t,simulation1.B,label='sim1')\n", - "axs[1,0].scatter(RM.hosp_tr,RM.Br_hosp,label='Real Data')\n", - "axs[1,0].set_title('Hospitalized Acmulated Deaths')\n", - "axs[1,0].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[1,1].set_xlim(0,days)\n", - "axs[1,1].plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UTI Beds')\n", - "axs[1,1].plot(simulation2.t+tsim1,simulation2.Hse+simulation2.Hout,label='UTI Beds')\n", - "axs[1,1].scatter(RM.sochimi_tr,RM.UTI,label='Real Data')\n", - "axs[1,1].plot(simulation1.t,simulation1.H_cap,label='Capacity', color = 'red')\n", - "axs[1,1].plot(simulation2.t+tsim1,simulation2.H_cap,label='Capacity', color = 'red')\n", - "axs[1,1].set_title('UTI Usage')\n", - "axs[1,1].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "axs[1,2].set_xlim(0,days)\n", - "axs[1,2].plot(simulation2.t+tsim1,simulation2.V,label='VSat sim2')\n", - "axs[1,2].plot(simulation1.t,simulation1.V,label='Vsat sim1')\n", - "axs[1,2].scatter(RM.sochimi_tr,RM.Vr,label='Real Data')\n", - "axs[1,2].plot(simulation1.t,simulation1.V_cap,label='Capacity Data', color = 'red')\n", - "axs[1,2].plot(simulation2.t+tsim1,simulation2.V_cap,label='Capacity Data', color = 'red')\n", - "axs[1,2].set_title('VMI Usage')\n", - "axs[1,2].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n", - "\n", - "\n", - "#axs[2,1].plot(simulation2.t+tsim1,simulation2.V_D_d,label='V_D sim2')\n", - "#axs[2,1].plot(simulation2.t+tsim1,simulation2.Hse_D_d,label='Hse_D sim2')\n", - "#axs[2,1].plot(simulation2.t+tsim1,simulation2.Icr_D_d,label='Icr_D sim2')\n", - "#axs[2,1].plot(simulation2.t+tsim1,simulation2.Ise_D_d,label='Ise_D sim2')\n", - "#axs[2,1].plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "#axs[2,1].plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "#axs[2,1].plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "#axs[2,1].plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "#axs[2,1].set_title('Death by cause')\n", - "#axs[2,1].axvline(x = t_end, linestyle = 'dotted',color = 'grey')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily new Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plt.plot(simulation2.t+tsim1,simulation2.I_d_det,label='sim2')\n", - "plt.plot(simulation1.t,simulation1.I_d_det,label='sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('Daily New Infected')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Deaths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation2.t+tsim1,simulation2.D,label='sim2')\n", - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### VMI Usage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation2.t+tsim1,simulation2.V_cap,label='VMI capacity sim2')\n", - "plt.plot(simulation1.t,simulation1.V_cap,label='VMI capacity sim1')\n", - "plt.plot(simulation1.t,simulation1.V,label='VMI sim1')\n", - "plt.plot(simulation2.t+tsim1,simulation2.V,label='VMI sim2')\n", - "plt.legend(loc=0)\n", - "plt.title('VMI')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Vr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation2.t+tsim1,simulation2.H_cap,label='sim2')\n", - "plt.plot(simulation1.t,simulation1.H_cap,label='sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse+simulation1.Hout,label='UCI/UTI Beds')\n", - "plt.plot(simulation2.t+tsim1,simulation2.Hse+simulation2.Hout,label='UCI/UTI Beds')\n", - "plt.legend(loc=0)\n", - "plt.title('Hospitalization')\n", - "\n", - "plt.scatter(RM.sochimi_tr,RM.Hr,label='Real Data')\n", - "plt.scatter(RM.sochimi_tr,RM.Hr_tot,label='Real Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deaths by cause" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation2.t+tsim1,simulation2.D,label='sim2')\n", - "plt.plot(simulation1.t,simulation1.D,label='sim1')\n", - "\n", - "\n", - "plt.plot(simulation2.t+tsim1,simulation2.V_D_d,label='V_D sim2')\n", - "plt.plot(simulation2.t+tsim1,simulation2.Hse_D_d,label='Hse_D sim2')\n", - "plt.plot(simulation2.t+tsim1,simulation2.Icr_D_d,label='Icr_D sim2')\n", - "plt.plot(simulation2.t+tsim1,simulation2.Ise_D_d,label='Ise_D sim2')\n", - "\n", - "plt.plot(simulation1.t,simulation1.V_D_d,label='V_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Hse_D_d,label='Hse_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Icr_D_d,label='Icr_D sim1')\n", - "plt.plot(simulation1.t,simulation1.Ise_D_d,label='Ise_D sim1')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Deaths by cause')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hospital saturation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation2.t+tsim1,simulation2.H_sat,label='HSat sim2')\n", - "plt.plot(simulation1.t,simulation1.H_sat,label='HSat sim1')\n", - "plt.plot(simulation2.t+tsim1,simulation2.V_sat,label='VSat sim2')\n", - "plt.plot(simulation1.t,simulation1.V_sat,label='Vsat sim1')\n", - "plt.legend(loc=0)\n", - "plt.title('Saturation')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error Calculation\n", - "The following plots will show the simulation results with the real data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakidx = np.where(RM.Ir==max(RM.Ir))[0][0]\n", - "datapeakdate = RM.sochimi_dates[peakidx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.peak_date" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "datapeakdate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta = (simulation.peak_date -datapeakdate).days" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Peak_delta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Errors" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = simulation1\n", - "t_end = tsim1\n", - "t_end_idx = np.where(np.array(RM.tr)>=t_end)[0][0]\n", - "idx = np.searchsorted(simulation.t,RM.tr[:t_end_idx])\n", - "E_I = np.sum(abs(RM.Ir[:t_end_idx]-simulation.I_det[idx]))/(np.mean(RM.Ir[:t_end_idx])*t_end_idx)\n", - "\n", - "t_end_idx = np.where(np.array(RM.I_d_r_tr)>=t_end)[0][0]\n", - "idx = np.searchsorted(simulation.t,RM.I_d_r_tr[:t_end_idx])\n", - "E_Id = np.sum(abs(RM.I_d_r[:t_end_idx]-simulation.I_d_det[idx]))/(np.mean(RM.I_d_r[:t_end_idx])*t_end_idx)\n", - "\n", - "t_end_idx = np.where(np.array(RM.I_ac_r_tr)>=t_end)[0][0]\n", - "idx = np.searchsorted(simulation.t,RM.I_ac_r_tr[:t_end_idx])\n", - "E_Iac = np.sum(abs(RM.I_ac_r[:t_end_idx]-simulation.I_ac_det[idx]))/(np.mean(RM.I_ac_r[:t_end_idx])*t_end_idx)\n", - "\n", - "t_end_idx = np.where(np.array(RM.Br_tr)>=t_end)[0][0]\n", - "idx = np.searchsorted(simulation.t,RM.Br_tr[:t_end_idx])\n", - "E_D = np.sum(abs(RM.Br[:t_end_idx]-simulation.B[idx]))/(np.mean(RM.Br[:t_end_idx])*t_end_idx)\n", - "\n", - "t_end_idx = np.where(np.array(RM.sochimi_tr)>=t_end)[0][0]\n", - "idx = np.searchsorted(simulation.t,RM.sochimi_tr[:t_end_idx])\n", - "E_UCI = np.sum(abs(RM.Hr[:t_end_idx]-(simulation.Hse[idx]+simulation.Hout[idx])))/(np.mean(RM.Hr[:t_end_idx])*t_end_idx)\n", - "\n", - "t_end_idx = np.where(np.array(RM.sochimi_tr)>=t_end)[0][0]\n", - "idx = np.searchsorted(simulation.t,RM.sochimi_tr[:t_end_idx])\n", - "E_VMI = np.sum(abs(RM.Vr[:t_end_idx]-simulation.V[idx]))/(np.mean(RM.Vr[:t_end_idx])*t_end_idx)\n", - "\n", - "Err = {'Active Infected':E_I,'Daily Infected':E_Id,'Accumulated Infected':E_Iac,'Deaths':E_D,'UCI/UTI':E_UCI,'VMI':E_VMI}\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Err = {'Active Infected':E_I,'Daily Infected':E_Id,'Accumulated Infected':E_Iac,'Deaths':E_D,'UCI/UTI':E_UCI,'VMI':E_VMI}\n", - "print(Err)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/USA/NYState_Dec2020.ipynb b/DataFit/USA/NYState_Dec2020.ipynb deleted file mode 100644 index add0464..0000000 --- a/DataFit/USA/NYState_Dec2020.ipynb +++ /dev/null @@ -1,1189 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Data Fit\n", - "\n", - "This Jupyter notebooks implements the SEIRHVD model for fitting the actual Chilean data in order to make projections in VMI and bed usage, amount of infected and deaths. This model uses the SEIRHVD 4.1 version. A visual representation of the model is available at: [SEIRHVD Miró Model](https://miro.com/app/board/o9J_ktzN4xA=/)\n", - "\n", - "This implements a single instance to facilitate understanding the model prior to run multiple data fittings at a time.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.dates as mdates \n", - "from datetime import datetime\n", - "from numpy import linalg as LA\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIRHVD/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "sys.path.insert(1, '../src/SEIRHVD/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "\n", - "from class_SEIRHUVD5 import SEIRHVD \n", - "from Quarantine import Quarantine\n", - "from Quarantine import SeroPrevDynamics\n", - "from importdata import ImportData" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Time Variables\n", - "Declare Initial date for simulating and data fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# First simulation Initial date\n", - "initdate = datetime(2020,9,1)\n", - "\n", - "# Current date\n", - "currentdate = datetime.now()\n", - "currentday = (currentdate - initdate).days\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# holydays\n", - "holiday = [[datetime(2020,11,26),'Thanksgiving'],\n", - " [datetime(2020,11,3),'Presidential Election'],\n", - " [datetime(2020,10,31),'Halloween'],\n", - " [datetime(2020,12,25),'Christmas']]\n", - " #[datetime(2020,10,31),'Trump Rallies']\n", - " #]\n", - "\n", - "thanksgiving = datetime(2020,11,26)\n", - "presidentialelection = datetime(2020,11,3)\n", - "halloween = datetime(2020,10,31)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# NY State population\n", - "population = 19450000" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '/home/samuel/Dropbox/DLab/Data/USA/ny_state_NYT.csv'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/home/samuel/Dropbox/DLab/Data/USA/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatadir\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'ny_state_NYT.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mhospdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatadir\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'ny_state_BEDS.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Infected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 684\u001b[0m )\n\u001b[1;32m 685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/samuel/Dropbox/DLab/Data/USA/ny_state_NYT.csv'" - ] - } - ], - "source": [ - "datadir = '/home/samuel/Dropbox/DLab/Data/USA/'\n", - "data = pd.read_csv(datadir+'ny_state_NYT.csv')\n", - "hospdata = pd.read_csv(datadir+'ny_state_BEDS.csv')\n", - "\n", - "# Infected\n", - "I_ac_r = data['cases']\n", - "I_d_r = data['cases'].diff()\n", - "dates = [datetime.strptime(data['date'][i],'%Y-%m-%d') for i in range(len(data))]\n", - "\n", - "# Deaths\n", - "D_ac_r = data['deaths']\n", - "D_d_r = data['deaths'].diff()\n", - "\n", - "\n", - "# hospitals\n", - "hosp_beds_cap = hospdata['inpatient_beds']\n", - "hosp_beds_gen = hospdata['inpatient_beds_used']\n", - "hosp_beds_covid = hospdata['inpatient_beds_used_covid']\n", - "\n", - "UCI_cap = hospdata['total_staffed_adult_icu_beds']\n", - "UCI_gen = hospdata['staffed_adult_icu_bed_occupancy']\n", - "UCI_covid = hospdata['staffed_icu_adult_patients_confirmed_covid']\n", - "\n", - "hosp_dates = [datetime.strptime(hospdata['date'][i],'%Y-%m-%d') for i in range(len(dates))]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data plots" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(dates,I_ac_r,label='Accumulated Infections')\n", - "plt.plot(dates,D_ac_r,label='Accumulated Deaths')\n", - "plt.legend(loc=0)\n", - "plt.title('Ny State - EPI accumulated')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(dates,I_d_r,label='Daily Infections')\n", - "plt.plot(dates,D_d_r,label='Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.title('Ny State - EPI daily')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 242, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(dates,np.log10(I_d_r),label='Daily Infections')\n", - "#plt.plot(dates,D_d_r,label='Daily Deaths')\n", - "plt.legend(loc=0)\n", - "plt.title('Ny State - EPI daily')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 977, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(hosp_dates,hosp_beds_cap,label='Hospital capacity',color = 'red',linestyle = 'dashed')\n", - "plt.plot(hosp_dates,hosp_beds_gen,label='Hospital use total')\n", - "plt.plot(hosp_dates,hosp_beds_covid,label='Hospital use covid')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Ny State - Hospitals general beds')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 979, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(hosp_dates,UCI_cap,label='UCI capacity',color = 'red',linestyle = 'dashed')\n", - "plt.plot(hosp_dates,UCI_gen,label='UCI use total')\n", - "plt.plot(hosp_dates,UCI_covid,label='UCI use covid')\n", - "\n", - "plt.legend(loc=0)\n", - "plt.title('Ny State - ICU')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initial values\n", - "Los acumulados de 2 semanas antes del inicio de la simulacion" - ] - }, - { - "cell_type": "code", - "execution_count": 907, - "metadata": {}, - "outputs": [], - "source": [ - "rday = (initdate - timedelta(days=14)-dates[0]).days\n", - "R0 = I_ac_r[rday] - D_ac_r[rday]\n", - "\n", - "data_initday = (initdate-dates[0]).days\n", - "hospdata_initday = (initdate-hosp_dates[0]).days\n", - "\n", - "Htot0 = hosp_beds_cap[hospdata_initday] + 10000\n", - "Vtot0 = UCI_cap[hospdata_initday]+ 10000\n", - "\n", - "H0 = hosp_beds_covid[hospdata_initday]\n", - "V0 = UCI_covid[hospdata_initday] \n", - "\n", - "B0 = D_ac_r[data_initday]\n", - "D0 = D_d_r[data_initday]\n", - "\n", - "\n", - "I0 = I_ac_r[data_initday] - I_ac_r[rday]\n", - "I_d0 = I_d_r[data_initday]\n", - "I_ac0 = I_ac_r[data_initday]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 908, - "metadata": {}, - "outputs": [], - "source": [ - "# Total simulation time\n", - "tsim = 365\n", - "\n", - "beta = 0.088\n", - "mu = 0.03\n", - "k_I = 0\n", - "k_R = 0.1\n", - "SeroPrevFactor = 0.1\n", - "expinfection=0 # Proportion in which the exposed infect - 0: nothing, 1: equally as Infected" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quarantines\n", - "Build Quarantine Object:\n", - " \n", - " Q = Quarantine(rem_mob,max_mob=max_mob,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - " alpha = Q.alpha\n", - " \n", - " Plot Quarantine dynamics:\n", - " Q.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 909, - "metadata": {}, - "outputs": [], - "source": [ - "# Quarantine relaxation\n", - "#SPchange_date = datetime(2020,9,11)\n", - "#t_sp = (SPchange_date - initdate).days\n", - "\n", - "# Quarantines \n", - "max_mob = 0.6\n", - "rem_mob = 0.48\n", - "\n", - "fqtdays = (datetime(2020,11,1)-initdate).days\n", - "\n", - "Q1 = Quarantine(rem_mob,max_mob,qp=0,iqt=0,fqt=fqtdays)\n", - "Q2 = Quarantine(rem_mob)\n", - "\n", - "alpha = Q1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Underreport\n", - "Fraction of Infected detected/reported \n", - "\n", - "$Imi_{det}$: Fraction of Mild detected \n", - "$Ias_{det}$: fraction of asymptomatic detected \n", - "\n", - "If both are 1, means that all infected are detected so \n", - "\\begin{align}\n", - "I_{det} = I\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": 910, - "metadata": {}, - "outputs": [], - "source": [ - "Imi_det = 1\n", - "Ias_det = 1\n", - "Ise_det = 1\n", - "Icr_det = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Parameters:\n", - "The following parameters determine transitions probability and duration between different states. The values presented here are the default values set in the model. You can change them as it's showed in the following executable lines.\n", - "\n", - "pE_Ias = 0.4 # Transition from exposed to Asymptomatic Infected \n", - "tE_Ias = 5.0\n", - "\n", - "pE_Imi = 0.55 # Transition from exposed to Mild Infected \n", - "tE_Imi = 5.0\n", - "\n", - "pE_Icr = 0.01666 # Transition from exposed to Critical Infected \n", - "tE_Icr = 3.0\n", - "\n", - "pE_Ise = 0.03334 # Transition from exposed to Serious Infected \n", - "tE_Ise = 3.0\n", - "\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered \n", - "tIas_R = 10.0\n", - "\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered \n", - "tImi_R = 15.0\n", - "\n", - "pIse_Hse = 1.0 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated) \n", - "tIse_Hse = 3.0 \n", - "\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated) \n", - "tIse_D = 3.0 \n", - "\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated) \n", - "tIcr_V = 3.0 \n", - "\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated) \n", - "tIcr_D = 3.0 \n", - "\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered \n", - "tHse_R = 11.0\n", - "\n", - "pHse_V = 0.03 # Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated) \n", - "tHse_V = 3.0\n", - "\n", - "pHse_D = 0.03 # Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated) \n", - "tHse_D = 3.0 \n", - "\n", - "pV_Hout = 0.5 # Transition from Ventilators to Hospital Recovery (Hout) \n", - "tV_Hout = 15.0\n", - "\n", - "pV_D = 0.5 # Transition from Ventilators to Death \n", - "tV_D = 15.0\n", - "\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered \n", - "tHout_R = 4.0\n", - "\n", - "pD_B = 1.0 # Transition from Dead to buried \n", - "tD_B = 1.0 \n", - "\n", - "betaD = 0 # Contagion by deads rate \n", - "eta = 0.0 # Immunity loss rate " - ] - }, - { - "cell_type": "code", - "execution_count": 911, - "metadata": {}, - "outputs": [], - "source": [ - "#state parameters: \n", - "pE_Ias = 0.3 # Transition from exposed to Asymptomatic Infected\n", - "tE_Ias = 4.0\n", - "pE_Imi = 0.43 # Transition from exposed to Mild Infected\n", - "tE_Imi = 4.0\n", - "pE_Icr = 0.006 # Transition from exposed to Critical Infected\n", - "tE_Icr = 3.0\n", - "pE_Ise = 0.254# Transition from exposed to Serious Infected\n", - "tE_Ise = 3.0\n", - "pIas_R = 1.0 # Transition from Asymptomatic Infected to Recovered\n", - "tIas_R = 9.0\n", - "pImi_R = 1.0 # Transition from Mild Infected to Recovered\n", - "tImi_R = 12.0\n", - "pIse_Hse = 1 # Transition from Serious Infected to Serious Hospitalized (When Hospital capacity is not saturated)\n", - "tIse_Hse = 5.0\n", - "pIse_D = 1.0 # Transition from Serious Infected to Death (When Hospital capacity is saturated)\n", - "tIse_D = 3.0\n", - "pIcr_V = 1.0 # Transition from Critical Infected to Ventilator (When Ventilators capacity is not saturated)\n", - "tIcr_V = 3.0\n", - "pIcr_D = 1.0 # Transition from Serious Infected to Death (When Ventilators capacity is saturated)\n", - "tIcr_D = 3.0\n", - "pHse_R = 0.97 # Transition from Serious Hospitalized to Recovered\n", - "tHse_R = 3.5\n", - "pHse_V = 0.03# Transition from Serious Hospitalized to Ventilators (When Ventilators capacity is not saturated)\n", - "tHse_V = 4.0\n", - "pHse_D = 0.85# Transition from Serious Hospitalized to Death (When Ventilators capacity is saturated)\n", - "tHse_D = 4.0\n", - "pV_Hout = 0.34 # Transition from Ventilators to Hospital Recovery (Hout)\n", - "tV_Hout = 16.0\n", - "pV_D = 0.66 # Transition from Ventilators to Death\n", - "tV_D = 12.0\n", - "pHout_R = 1.0 # Transition from Hospital Recovery (Hout) to Recovered\n", - "tHout_R = 10.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SeroPrevalence Dynamics\n", - "The susceptibles increase through a step function which last for the specified amount of days. Throughout these days it adds a \"dailyincrease\" amount of persons each day.\n", - "\n", - "The total increase of people is increasedays*dailyincrease" - ] - }, - { - "cell_type": "code", - "execution_count": 912, - "metadata": {}, - "outputs": [], - "source": [ - "spdate = datetime(2020,10,10)\n", - "t_sp = (spdate - initdate).days\n", - "\n", - "increasedays = 30\n", - "renewalFactor = 0.4 #0.23\n", - "dailyincrease = population*SeroPrevFactor*renewalFactor/increasedays\n", - "t_sp_temp = t_sp + 0\n", - "chi0 = SeroPrevDynamics(t_sp_temp+5,t_sp_temp+increasedays*0.7,t_sp_temp+increasedays,dailyincrease,form='line')\n", - "#chi0 = SeroPrevDynamics(t_sp,t_sp+increasedays*1,t_sp+increasedays,dailyincrease,'quadratic',df = 1)\n", - "chi = [chi0]\n", - "#renewalFactor = [renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 913, - "metadata": {}, - "outputs": [], - "source": [ - "def chisum(a,b):\n", - " def aux(t):\n", - " return a(t)+b(t)\n", - " return aux" - ] - }, - { - "cell_type": "code", - "execution_count": 914, - "metadata": {}, - "outputs": [], - "source": [ - "renewalFactor = [-0.2,-0.1,0,0.1,0.2,0.3]" - ] - }, - { - "cell_type": "code", - "execution_count": 915, - "metadata": {}, - "outputs": [], - "source": [ - "# Increase date \n", - "increasedate = datetime(2020,12,15)\n", - "increaseday = (increasedate - initdate).days\n", - "# Days for susceptible increase\n", - "increasedays = 30\n", - "# Daily amount of people\n", - "dailyincrease = [population*SeroPrevFactor*i/increasedays for i in renewalFactor]" - ] - }, - { - "cell_type": "code", - "execution_count": 916, - "metadata": {}, - "outputs": [], - "source": [ - "chi = [chisum(chi0,SeroPrevDynamics(increaseday,increaseday+increasedays*0.0001,increaseday+increasedays,i)) for i in dailyincrease]" - ] - }, - { - "cell_type": "code", - "execution_count": 917, - "metadata": {}, - "outputs": [], - "source": [ - "#taux = np.array(range(180))\n", - "#for i in range(len(chi)):\n", - "# plt.plot(taux,chi[i](taux))\n", - "#plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "## Initial SeroPrev period\n", - "Initialize Simulation Object, set params and run the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 918, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = [SEIRHVD(tsim,beta,mu,alpha,k_I=k_I,k_R = k_R, chi = i, \n", - " SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,Htot=int(Htot0),\n", - " Vtot=int(Vtot0),H0=H0,V0=V0,B0=B0,D0=D0,R0=R0,I0=I0,I_d0=I_d0,I_ac0=I_ac0,\n", - " population=population,Imi_det = Imi_det,Ias_det = Ias_det,initdate=initdate) for i in chi]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Modify simulation state parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 919, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n", - "Compartimental model State parameters changed\n" - ] - } - ], - "source": [ - "for i in range(len(simulation)):\n", - " simulation[i].pE_Ias=pE_Ias\n", - " simulation[i].tE_Ias=tE_Ias\n", - " simulation[i].pE_Imi=pE_Imi\n", - " simulation[i].tE_Imi=tE_Imi\n", - " simulation[i].pE_Icr=pE_Icr\n", - " simulation[i].tE_Icr=tE_Icr\n", - " simulation[i].pE_Ise=pE_Ise\n", - " simulation[i].tE_Ise=tE_Ise\n", - " simulation[i].pIas_R=pIas_R\n", - " simulation[i].tIas_R =tIas_R\n", - " simulation[i].pImi_R=pImi_R\n", - " simulation[i].tImi_R =tImi_R\n", - " simulation[i].pIse_Hse=pIse_Hse\n", - " simulation[i].tIse_Hse=tIse_Hse\n", - " simulation[i].pIse_D=pIse_D\n", - " simulation[i].tIse_D=tIse_D\n", - " simulation[i].pIcr_V=pIcr_V\n", - " simulation[i].tIcr_V=tIcr_V\n", - " simulation[i].pIcr_D=pIcr_D\n", - " simulation[i].tIcr_D=tIcr_D\n", - " simulation[i].pHse_R=pHse_R\n", - " simulation[i].tHse_R =tHse_R\n", - " simulation[i].pHse_V=pHse_V\n", - " simulation[i].tHse_V=tHse_V\n", - " simulation[i].pHse_D=pHse_D\n", - " simulation[i].tHse_D=tHse_D\n", - " simulation[i].pV_Hout=pV_Hout\n", - " simulation[i].tV_Hout =tV_Hout\n", - " simulation[i].pV_D=pV_D\n", - " simulation[i].tV_D =tV_D\n", - " simulation[i].pHout_R=pHout_R\n", - " simulation[i].tHout_R=tHout_R\n", - " simulation[i].setnewparams()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting new params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "* **integr_sci**: Fast solver, sometimes stiffness beat it \n", - "* **integr:** Slow solver, more robust with stiffness " - ] - }, - { - "cell_type": "code", - "execution_count": 920, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": 921, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 1.5s remaining: 3.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 1.6s remaining: 1.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 1.6s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 1.7s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 1.7s finished\n", - "ready\n" - ] - } - ], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr_sci(0,tsim,0.1)\n", - " return simulation[i]\n", - "\n", - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(simulation,i,tsim) for i in range(len(simulation)))\n", - "simulation = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 922, - "metadata": {}, - "outputs": [], - "source": [ - "# Days to plot\n", - "days = 300\n", - "enddate = initdate + timedelta(days=2*365)\n", - "enddate = datetime(2021,2,28)" - ] - }, - { - "cell_type": "code", - "execution_count": 971, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False\n", - "if saveplot:\n", - " %matplotlib inline\n", - "else:\n", - " %matplotlib tk\n", - "#%matplotlib tk\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily info" - ] - }, - { - "cell_type": "code", - "execution_count": 975, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow(np.linspace(0,1,len(holiday)))\n", - "\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "# --------------------------------------- #\n", - "# New Daily Infections #\n", - "# --------------------------------------- #\n", - "\n", - "\n", - "#axs[0,0].plot([], [], ' ', label='beta = '+str(beta))\n", - "#axs[0,0].plot([], [], ' ', label='mu = '+str(mu))\n", - "#axs[0,0].plot([], [], ' ', label='seroprev = '+str(SeroPrevFactor))\n", - "\n", - "axs[0,0].scatter(dates[data_initday:],I_d_r[data_initday:],label='Real Data')\n", - "for i in range(len(simulation)):\n", - " axs[0,0].plot(simulation[i].dates,simulation[i].I_d,label='RF: '+str(renewalFactor[i]))\n", - "\n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[0,0].axvline(x = holiday[i][0], linestyle = 'dashed',color = colors[i],label=holiday[i][1])\n", - "\n", - "axs[0,0].axvline(x = datetime(2021,1,28), linestyle = 'dashed',color = 'grey',label='Peak period start')\n", - "axs[0,0].axvline(x = datetime(2021,2,10), linestyle = 'dashed',color = 'grey',label='Peak period end')\n", - "\n", - "axs[0,0].set_title('New Infections',size=25)\n", - "axs[0,0].set_xlim(initdate+timedelta(days=14),enddate)\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].tick_params(labelsize=20)\n", - "axs[0,0].set_xlabel('Date',size=25)\n", - "axs[0,0].set_ylabel('New Infections',size=25)\n", - "axs[0,0].legend(loc=0,fontsize='16',title_fontsize='25')\n", - "\n", - "\n", - "\n", - "# --------------------------------------- #\n", - "# New Daily Deaths #\n", - "# --------------------------------------- #\n", - "\n", - "axs[0,1].scatter(dates[data_initday:],D_d_r[data_initday:],label='Real Data')\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[0,1].plot(simulation[i].dates,simulation[i].D,label='RF: '+str(renewalFactor[i]))\n", - " \n", - " \n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[0,1].axvline(x = holiday[i][0], linestyle = 'dashed',color = colors[i],label=holiday[i][1])\n", - " \n", - "axs[0,1].set_title('New Deaths',size=25)\n", - "axs[0,1].set_xlim(initdate+timedelta(days=14),enddate)\n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].tick_params(labelsize=20)\n", - "axs[0,1].set_xlabel('Date',size=25)\n", - "axs[0,1].set_ylabel('New Deaths',size=25)\n", - "axs[0,1].legend(loc=0,fontsize='16',title_fontsize='25')\n", - "\n", - "# --------------------------------------- #\n", - "# Normal Hospital beds #\n", - "# --------------------------------------- #\n", - "\n", - "axs[1,0].scatter(hosp_dates[hospdata_initday:],hosp_beds_covid[hospdata_initday:],label='Real Data')\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[1,0].plot(simulation[i].dates,simulation[i].H,label='RF: '+str(renewalFactor[i]))\n", - " \n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[1,0].axvline(x = holiday[i][0], linestyle = 'dashed',color = colors[i],label=holiday[i][1])\n", - "\n", - "axs[1,0].set_title('Hospital beds use',size=25)\n", - "axs[1,0].set_xlim(initdate+timedelta(days=14),enddate)\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,0].tick_params(labelsize=20)\n", - "axs[1,0].set_xlabel('Date',size=25)\n", - "axs[1,0].set_ylabel('Hospital beds use',size=25)\n", - "axs[1,0].legend(loc=0,fontsize='16',title_fontsize='25')\n", - "\n", - "\n", - "# --------------------------------------- #\n", - "# ICU #\n", - "# --------------------------------------- #\n", - "\n", - "\n", - "axs[1,1].scatter(hosp_dates[hospdata_initday:],UCI_covid[hospdata_initday:],label='Real Data')\n", - "\n", - "for i in range(len(simulation)):\n", - " axs[1,1].plot(simulation[i].dates,simulation[i].V,label='RF: '+str(renewalFactor[i]))\n", - " \n", - "\n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[1,1].axvline(x = holiday[i][0], linestyle = 'dashed',color = colors[i],label=holiday[i][1])\n", - "\n", - "\n", - "axs[1,1].set_title('ICU use',size=25)\n", - "axs[1,1].set_xlim(initdate+timedelta(days=14),enddate)\n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,1].tick_params(labelsize=20)\n", - "axs[1,1].set_xlabel('Date',size=25)\n", - "axs[1,1].set_ylabel('ICU use',size=25)\n", - "axs[1,1].legend(loc=0,fontsize='16',title_fontsize='25')\n", - "\n", - "\n", - "fig.suptitle('NewYork State',fontsize=30)\n", - "\n", - "if saveplot:\n", - " fig.savefig('/home/samuel/Dropbox/DLab/Data/USA/NYForecast_Dec2020.pdf',dpi=100,format='pdf')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Accumulated info" - ] - }, - { - "cell_type": "code", - "execution_count": 904, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'NewYork State')" - ] - }, - "execution_count": 904, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(holiday)))\n", - "\n", - "fig, axs = plt.subplots(2, 2)\n", - "\n", - "# --------------------------------------- #\n", - "# Accumulated Infected #\n", - "# --------------------------------------- #\n", - "axs[0,0].set_title('Accumulated Infected')\n", - "axs[0,0].set_xlim(initdate,enddate)\n", - "axs[0,0].plot([], [], ' ', label='beta = '+str(beta))\n", - "axs[0,0].plot([], [], ' ', label='mu = '+str(mu))\n", - "axs[0,0].plot([], [], ' ', label='seroprev = '+str(SeroPrevFactor))\n", - "\n", - "axs[0,0].scatter(dates[data_initday:],I_ac_r[data_initday:],label='Real')\n", - "axs[0,0].plot(sim.dates,sim.I_ac,label='Simulation')\n", - "\n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[0,0].axvline(x = holiday[i][0], linestyle = 'dotted',color = colors[i],label=holiday[i][1])\n", - "\n", - "\n", - "axs[0,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,0].legend(loc=0)\n", - "\n", - "\n", - "\n", - "# --------------------------------------- #\n", - "# Accumulated Deaths #\n", - "# --------------------------------------- #\n", - "\n", - "\n", - "axs[0,1].set_title('Accumulated Deaths')\n", - "axs[0,1].set_xlim(initdate,enddate)\n", - "\n", - "axs[0,1].scatter(dates[data_initday:],D_ac_r[data_initday:],label='Real')\n", - "axs[0,1].plot(sim.dates,sim.B,label='Simulation')\n", - "\n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[0,1].axvline(x = holiday[i][0], linestyle = 'dotted',color = colors[i],label=holiday[i][1])\n", - " \n", - "axs[0,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[0,1].legend(loc=0)\n", - "\n", - "\n", - "# --------------------------------------- #\n", - "# Normal Hospital beds #\n", - "# --------------------------------------- #\n", - "\n", - "axs[1,0].set_title('Hospital beds use')\n", - "axs[1,0].set_xlim(initdate,enddate)\n", - "\n", - "axs[1,0].scatter(hosp_dates[hospdata_initday:],hosp_beds_covid[hospdata_initday:],label='Real')\n", - "axs[1,0].plot(sim.dates,sim.H,label='Simulation')\n", - "\n", - "# plot holidays:\n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[1,0].axvline(x = holiday[i][0], linestyle = 'dotted',color = colors[i],label=holiday[i][1])\n", - "\n", - "axs[1,0].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,0].legend(loc=0)\n", - "\n", - "\n", - "# --------------------------------------- #\n", - "# UCI #\n", - "# --------------------------------------- #\n", - "\n", - "axs[1,1].set_title('UCI use')\n", - "axs[1,1].set_xlim(initdate,enddate)\n", - "\n", - "axs[1,1].scatter(hosp_dates[hospdata_initday:],UCI_covid[hospdata_initday:],label='Real')\n", - "axs[1,1].plot(sim.dates,sim.V,label='Simulation')\n", - "\n", - "# plot holidays:\n", - "# plot holidays:\n", - "for i in range(len(holiday)):\n", - " axs[1,1].axvline(x = holiday[i][0], linestyle = 'dotted',color = colors[i],label=holiday[i][1])\n", - " \n", - "axs[1,1].fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n", - "axs[1,1].legend(loc=0)\n", - "\n", - "\n", - "fig.suptitle('NewYork State',fontsize=16)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plot" - ] - }, - { - "cell_type": "code", - "execution_count": 905, - "metadata": {}, - "outputs": [], - "source": [ - "sim = simulation[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 747, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Accumulated Infected')" - ] - }, - "execution_count": 747, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Accumulated Infected\n", - "plt.scatter(dates[data_initday:],I_ac_r[data_initday:],label='Real')\n", - "plt.plot(sim.dates,sim.I_ac,label='Simulation')\n", - "plt.title('Accumulated Infected')" - ] - }, - { - "cell_type": "code", - "execution_count": 748, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'New Daily Infected')" - ] - }, - "execution_count": 748, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Daily Infected\n", - "plt.scatter(dates[data_initday:],I_d_r[data_initday:],label='Real')\n", - "plt.plot(sim.dates,sim.I_d,label='Simulation')\n", - "plt.title('New Daily Infected')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Accumulated Deaths')" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Accumulated Deaths\n", - "plt.scatter(dates[data_initday:],D_ac_r[data_initday:],label='Real')\n", - "plt.plot(sim.dates,sim.B,label='Simulation')\n", - "plt.title('Accumulated Deaths')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'New Daily Deaths')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# New Daily Deaths\n", - "plt.scatter(dates[data_initday:],D_d_r[data_initday:],label='Real')\n", - "plt.plot(sim.dates,sim.D,label='Simulation')\n", - "plt.title('New Daily Deaths')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'General beds Use')" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Hospital Use\n", - "plt.scatter(hosp_dates[hospdata_initday:],hosp_beds_covid[hospdata_initday:],label='Real')\n", - "plt.plot(sim.dates,sim.H,label='Simulation')\n", - "plt.title('General beds Use')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'UCI Use')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# UCI Use\n", - "plt.scatter(hosp_dates[hospdata_initday:],UCI_covid[hospdata_initday:],label='Real')\n", - "plt.plot(sim.dates,sim.V,label='Simulation')\n", - "plt.title('UCI Use')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "SeroPrevalence = [simulation[i].I_ac[-1]/simulation[i].population for i in range(len(simulation))]\n", - "SeroPrevalenceDet = [simulation[i].I_ac_det[-1]/simulation[i].population for i in range(len(simulation))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/DataFit/ValparaisoFit.py b/DataFit/ValparaisoFit.py deleted file mode 100644 index 866de26..0000000 --- a/DataFit/ValparaisoFit.py +++ /dev/null @@ -1,95 +0,0 @@ -import sys -from pathlib import Path -sys.path.insert(1, '../src/SEIRHVD/') -sys.path.insert(1, 'src/SEIRHVD/') -sys.path.insert(1, '../src/utils/') -sys.path.insert(1, 'src/utils/') -import numpy as np -from datetime import datetime -import matplotlib.pyplot as plt -import pandas as pd - -from SEIRHVD_local import SEIRHVD_local -from Quarantine import Quarantine - - -tstate = '05' -# Fecha Inicial -initdate = datetime(2020,5,15) -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0.6 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos -tsim = 500 # Tiempo de simulacion -# Creación del objeto de simulación -simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate) -quarantines = [[500.0, 0.85, 0.7, 0.0, 0.0, 500.0, 0.0]] -simulation.inputarray = np.array(quarantines) -simulation.addquarantine() -# Valores iniciales -I_act0 = 100 -dead = 0 -population = 10000 -H0 = 1 -V0 = 1 -# Capacidades hospitalarias -Htot = 1000 -Vtot = 1000 -simulation.initialvalues(I_act0,dead,population,H0,V0,Htot,Vtot,R=0,D=0,H_cr = 0) -# Simular -simulation.simulate() -simulation.plotinfectadosactivos() - - - - -# Import Quarantine data} -def importQuarantines(tstate): - """ - Import historical quarantines - tstate could be a region, a comuna, or a list with a combination of both. - """ - endpoint = 'https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto29/Cuarentenas-Totales.csv' - aux = pd.read_csv(endpoint) - aux['Codigo CUT Comuna'] = aux['Código CUT Comuna'].map(lambda i: str(i).zfill(5)) - aux = aux.loc[[tstate in x[:len(tstate)] for x in aux['Codigo CUT Comuna']]] - - if type(tstate) == list: - cuarentenas = pd.DataFrame() - for i in tstate: - cuarentenas = cuarentenas.append(aux.loc[[i in x[:len(i)] for x in aux['Codigo CUT Comuna']]]) - else: - cuarentenas = aux.loc[[tstate in x[:len(tstate)] for x in aux['Codigo CUT Comuna']]] - - return cuarentenas - - -# Valpo -tstate = '05' -# Bio Bio -tstate = '08' -# Araucania -tstate = '09' - -# Import Data -initdate = datetime(2020,5,15) -sochimi,sochimi_dates ,sochimi_tr,Hr, Hr_tot, Vr ,Vr_tot = a.importsochimi(tstate=tstate,initdate=initdate) -I_d_r_smooth, I_d_r_tr, I_d_r_dates = a.importDailyInfected(tstate=tstate,initdate = initdate) - -# Plot Data - -# Infected -plt.plot(I_d_r_dates,I_d_r_smooth) -plt.title('Daily Infected - Region '+tstate) -plt.show() - -# Bed use -plt.plot(sochimi_dates,Hr,label='Bed Use') -plt.plot(sochimi_dates,Hr_tot,label='Bed Capacity') -plt.plot(sochimi_dates,Vr,label='VMI use') -plt.plot(sochimi_dates,Vr_tot,label='VMI Capacity') -plt.title('SOCHIMI - Region '+tstate) -plt.legend(loc=0) -plt.show() diff --git a/research/CV19-Re_beta.ipynb b/research/CV19-Re_beta.ipynb deleted file mode 100644 index cd6fb9c..0000000 --- a/research/CV19-Re_beta.ipynb +++ /dev/null @@ -1,1403 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIR model parameter determination from Re estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samuel/anaconda3/lib/python3.8/site-packages/rpy2/robjects/pandas2ri.py:17: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.\n", - " from pandas.core.index import Index as PandasIndex\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import toml\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "from datetime import datetime\n", - "import matplotlib.dates as mdates\n", - "from matplotlib.ticker import MaxNLocator\n", - "\n", - "import epyestim\n", - "import epyestim.covid19 as covid19\n", - "from epyestim.distributions import discretise_gamma\n", - "from datetime import timedelta\n", - "\n", - "# rpy2 imports\n", - "from rpy2.robjects.packages import importr\n", - "from rpy2.robjects import pandas2ri\n", - "import rpy2.robjects as robjects\n", - "pandas2ri.activate()\n", - "# R library\n", - "global eps\n", - "eps = importr(\"EpiEstim\")\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../')\n", - "from src2.models.cv19sim import cv19sim" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Pop-up plots\n", - "import platform\n", - "OS = platform.system()\n", - "if OS == 'Linux' or OS == 'Darwin':\n", - " %matplotlib tk \n", - "elif OS == 'Windows':\n", - " %matplotlib qt " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bassi A. Equations" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def getdelta(y,x=None,scale='linear'):\n", - " if scale == 'linear':\n", - " y = np.log(y+0.001)\n", - " #delta = np.diff(y)\n", - " delta = np.gradient(y)\n", - " return delta\n", - "\n", - "def delta_eq(beta,S,N,sigma,gamma):\n", - " delta = 1/2*(np.sqrt(4*sigma*beta*S/N+(sigma-gamma)**2)-sigma-gamma)\n", - " return delta\n", - "def Re_delta_calc(delta,sigma,gamma):\n", - " Ti = 1/gamma\n", - " Te = 1/sigma\n", - " Ts = Ti + Te\n", - " return 1 + delta*Ts + sigma*gamma/((sigma+gamma)**2)*(delta*Ts)**2 \n", - "\n", - "def Re_delta_a_calc(delta,sigma,gamma):\n", - " Ti = 1/gamma\n", - " Te = 1/sigma\n", - " Ts = Ti + Te\n", - " return 1 + delta*Ts\n", - "\n", - "def Re_analitico_calc(beta,gamma,S,N):\n", - " Re = beta/gamma*S/N\n", - " return Re\n", - "\n", - "def beta_Re(Re,S,N,gamma):\n", - " return Re*gamma*N/S\n", - "\n", - "def beta_delta(delta,S,N,gamma,sigma):\n", - " Re = Re_delta_calc(delta,sigma,gamma)\n", - " return Re*gamma*N/S\n", - "\n", - "def beta_Id(I_d,S,N,gamma,sigma):\n", - " delta = getdelta(I_d)\n", - " Re = Re_delta(delta,sigma,gamma)\n", - " return Re*gamma*N/S" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de datos sintéticos\n", - "Trabajaremos con los infectados nuevos diarios, dado que esos son los datos que se miden en la realidad. El objetivo será tratar de reconstruir los parámetros originales. Truncaremos los datos para reducir el efecto de estabilización inicial. Partiremos con datos desde que los infectados activos superen los 100 casos. \n", - "Por ahora despreciaremos los efectos del subreporte y del ruido de medición." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "cfgfile = '../config_files/SEIR2.toml'\n", - "cfg = toml.load(cfgfile) # no es necesario, pero es util para ver el archivo de configuracion" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.4\n", - "I0 = 10\n", - "mu = 1\n", - "tE_I = 3\n", - "tI_R = 5\n", - "sigma = 1/tE_I\n", - "gamma = 1/tI_R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tiempo Serial\n", - "Uno de los mayores desafíos es tener un tiempo serial bien calculado.\n", - "Analíticamente la definición del tiempo serial es: \n", - "$ T_s = T_{EI} + T_{IR} $ \n", - "Donde $T_{EI}$ y $T_{IR}$ son las medias de las distribuciones de tiempos de incubación e infecciosidad respectivamente. \n", - "Para este análisis trabajaremos primero con los tiempos seriales de la misma simulación." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "Ts = tE_I + tI_R" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sims = cv19sim(cfg,beta = beta,mu=mu,I=I0,I_d=I0*beta,tE_I = tE_I,tI_R=tI_R,t_end=250)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "sims.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(sims.sims[0].I_d,label='Nuevos Infectados Diarios')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Se consideran los datos desde que hayan 100 casos" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Tiempo de los datos inicial\n", - "t0 = np.where(sims.sims[0].I >= 100)[0][0]\n", - "#t0 = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "I_d = sims.sims[0].I_d[t0:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cálculo R analítico\n", - "$R_e = \\beta \\frac{S}{N}T_{IR}$" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Re_analitico = Re_analitico_calc(beta,gamma,sims.sims[0].S[t0:],sims.sims[0].N)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trabajo con la data sintética" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "plt.scatter(range(len(I_d)),I_d,label='New daily infected')\n", - "plt.title('Synthetic Data')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Método 1: A. Bassi" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculo de delta\n", - "delta = getdelta(I_d)\n", - "# Calculo de Re\n", - "Re_delta = Re_delta_calc(delta,sigma,gamma)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "axs2.set_ylabel('Delta',color='tab:red')\n", - "axs2.plot(range(len(delta)),delta,label='delta',color='tab:red')\n", - "\n", - "axs2.tick_params(axis='y', labelcolor='tab:red')\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(I_d)),I_d,color='tab:blue',label='Infectados nuevos diarios',linestyle='solid')\n", - "axs.set_yscale('log')\n", - "#axs.plot(sim.t,sim.I,color='tab:blue',label='Activos')\n", - "axs.set_ylabel('Infectados',color='tab:blue')\n", - "axs.tick_params(axis='y', labelcolor='tab:blue')\n", - "\n", - "#axs2.set_xlim(0,200)\n", - "fig.suptitle('Delta e Id')\n", - "axs.legend(loc=1)\n", - "axs2.legend(loc=3)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Método 2: Epyestim" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Format data, create synthetic dates\n", - "initdate = datetime(2020,3,1)\n", - "dates = [initdate+timedelta(days=int(j)) for j in sims.sims[0].t[t0:]]\n", - "cases = pd.Series(data=I_d,index=dates)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "gt_distribution = np.zeros(50)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "gt_distribution[Ts-1] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def gt_distribution_continuous(sigma,gamma):\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "R_epistim_Ts = covid19.r_covid(cases,delay_distribution=np.array([1]),gt_distribution=gt_distribution,n_samples=300)['R_mean']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Estudios de Re: Comparación R analítico, R delta y R delta aprox" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "axs2.set_ylabel('Re',color='tab:red')\n", - "axs2.plot(range(len(Re_analitico[1:])),Re_analitico[1:],label='Analitico')\n", - "axs2.plot(range(len(Re_delta)),Re_delta,label='delta')\n", - "#axs2.plot(range(len(Re_delta_a)),Re_delta_a,label='delta aprox')\n", - "axs2.plot(range(len(R_epistim_Ts)),R_epistim_Ts,color='black',label='Epyestim',linestyle='dashed')\n", - "axs2.axhline(1,color='grey',linestyle='dashed')\n", - "axs2.tick_params(axis='y', labelcolor='tab:red')\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(I_d)),I_d,color='tab:blue',label='Diarios',linestyle='solid')\n", - "axs.set_yscale('log')\n", - "axs2.axvline(np.where(I_d==max(I_d))[0][0],color='grey',linestyle='dashed')\n", - "#axs.plot(sim.t,sim.I,color='tab:blue',label='Activos')\n", - "axs.set_ylabel('Infectados',color='tab:blue')\n", - "axs.tick_params(axis='y', labelcolor='tab:blue')\n", - "\n", - "\n", - "axs2.set_ylim(0,4)\n", - "fig.suptitle('Re e Id')\n", - "axs.legend(loc=1)\n", - "axs2.legend(loc=3)\n", - "fig.show()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculo de beta\n", - "$ \\beta(t) = R_e \\frac{N}{S(t)} \\gamma $" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "beta_Re_delta = gamma*Re_delta*sims.sims[0].N/sims.sims[0].S[t0:]\n", - "#beta_Re_delta_a = gamma*Re_delta_a*sims.sims[0].N/sims.sims[0].S[t0:]\n", - "beta_Re_analitico = gamma*Re_analitico*sims.sims[0].N/sims.sims[0].S[t0:]\n", - "beta_Re_epyestim_Ts = gamma*R_epistim_Ts*sims.sims[0].N/sims.sims[0].S[t0+7:]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "\n", - "axs2.plot(range(len(Re_analitico[1:])),Re_analitico[1:],color = 'tab:red',label='Re analitico')\n", - "axs2.plot(range(len(Re_delta)),Re_delta,label='Re delta',color = 'tab:red',linestyle='dashed')\n", - "#axs2.plot(range(len(R_epistim_Ts)),R_epistim_Ts,color='tab:red',label='Epyestim',linestyle='dotted')\n", - "#axs2.plot(range(len(Re_delta_a)),Re_delta_a,label='delta aprox')\n", - "axs2.axhline(1,color='red',linestyle='dotted',alpha=0.5)\n", - "\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(beta_Re_analitico)),beta_Re_analitico,label='Beta analitico',color = 'tab:blue')\n", - "axs.plot(range(len(beta_Re_delta)),beta_Re_delta,label='Beta delta',color = 'tab:blue',linestyle='dashed')\n", - "\n", - "\n", - "axs2.fill_between(range(len(Re_analitico[1:])), Re_analitico[1:], Re_delta[0:-1],color='tab:grey', alpha=0.4)\n", - "\n", - "axs.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted',label='Min beta')\n", - "\n", - "axs2.plot(range(len(R_epistim_Ts)),R_epistim_Ts,color='tab:red',label='Epyestim',linestyle='dotted')\n", - "axs.plot(range(len(beta_Re_epyestim_Ts)),beta_Re_epyestim_Ts,label='beta Epyestim',color = 'tab:blue',linestyle='dotted')\n", - "\n", - "axs2.tick_params(axis='y', labelcolor='tab:red',size=10)\n", - "axs.tick_params(axis='y', labelcolor='tab:blue',size=10)\n", - "\n", - "axs2.set_ylabel('Re',color='tab:red',size=12)\n", - "axs.set_ylabel('Beta',color='tab:blue',size=12)\n", - "\n", - "axs.set_ylim(0.3,0.6)\n", - "axs2.set_ylim(0,2.5)\n", - "\n", - "fig.suptitle('Re y beta')\n", - "\n", - "lines, labels = axs.get_legend_handles_labels()\n", - "lines2, labels2 = axs2.get_legend_handles_labels()\n", - "axs2.legend(lines + lines2, labels + labels2, loc=[0.05,0.1])\n", - "#axs.legend(loc=[0.05,0.05])\n", - "axs2.legend(loc=[0.05,0.17])\n", - "# Create a Rectangle patch\n", - "#rect = patches.Rectangle([.67,.24],.3,.4, linewidth=1, edgecolor='b', facecolor='white')\n", - "\n", - "# Add the patch to the Axes\n", - "#axs.add_patch(rect)\n", - "ax_zoom = axs2.inset_axes([.67,.5,.3,.4])\n", - "\n", - "init = 80\n", - "end = 120\n", - "ax_zoom.plot(np.arange(init,end),Re_analitico[init:end],color = 'tab:red',linestyle='solid')\n", - "ax_zoom.plot(np.arange(init,end),Re_delta[init:end],color = 'tab:red',linestyle='dashed')\n", - "ax_zoom.fill_between(np.arange(init,end),Re_analitico[init:end],Re_delta[init:end],color='tab:grey', alpha=0.2)\n", - "ax_zoom.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted')\n", - "\n", - "ax_zoom.set_title('Re difference',size=10)\n", - "\n", - "if True:\n", - " \n", - " ax_zoom.plot(np.arange(init,end),R_epistim_Ts[init:end],color='tab:red',label='Epyestim',linestyle='dotted')\n", - "#ax_zoom.set_xticks([])\n", - "#ax_zoom.set_yticks([])\n", - "axs2.indicate_inset_zoom(ax_zoom)\n", - "\n", - "fig.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "\n", - "axs2.plot(range(len(Re_analitico[1:])),Re_analitico[1:],color = 'tab:red',label='Re analitico')\n", - "axs2.plot(range(len(Re_delta)),Re_delta,label='Re delta',color = 'tab:red',linestyle='dashed')\n", - "#axs2.plot(range(len(R_epistim_Ts)),R_epistim_Ts,color='tab:red',label='Epyestim',linestyle='dotted')\n", - "#axs2.plot(range(len(Re_delta_a)),Re_delta_a,label='delta aprox')\n", - "axs2.axhline(1,color='red',linestyle='dotted',alpha=0.5)\n", - "\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(beta_Re_analitico)),beta_Re_analitico,label='Beta analitico',color = 'tab:blue')\n", - "axs.plot(range(len(beta_Re_delta)),beta_Re_delta,label='Beta delta',color = 'tab:blue',linestyle='dashed')\n", - "#axs.plot(range(len(beta_Re_epyestim_Ts)),beta_Re_epyestim_Ts,label='beta Epyestim',color = 'tab:blue',linestyle='dotted')\n", - "\n", - "axs2.fill_between(range(len(Re_analitico[1:])), Re_analitico[1:], Re_delta[0:-1],color='tab:grey', alpha=0.4)\n", - "\n", - "axs.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted',label='Min beta')\n", - "\n", - "axs2.tick_params(axis='y', labelcolor='tab:red',size=10)\n", - "axs.tick_params(axis='y', labelcolor='tab:blue',size=10)\n", - "\n", - "axs2.set_ylabel('Re',color='tab:red',size=12)\n", - "axs.set_ylabel('Beta',color='tab:blue',size=12)\n", - "\n", - "#axs.set_ylim(0.3,0.6)\n", - "#axs2.set_ylim(0,2.5)\n", - "\n", - "fig.suptitle('Re y beta')\n", - "\n", - "lines, labels = axs.get_legend_handles_labels()\n", - "lines2, labels2 = axs2.get_legend_handles_labels()\n", - "axs2.legend(lines + lines2, labels + labels2, loc=[0.05,0.1])\n", - "#axs.legend(loc=[0.05,0.05])\n", - "#axs2.legend(loc=[0.05,0.17])\n", - "\n", - "ax_zoom = axs2.inset_axes([.67,.24,.3,.4])\n", - "\n", - "init = 80\n", - "end = 120\n", - "ax_zoom.plot(np.arange(init,end),Re_analitico[init:end],color = 'tab:red',linestyle='solid')\n", - "ax_zoom.plot(np.arange(init,end),Re_delta[init:end],color = 'tab:red',linestyle='dashed')\n", - "ax_zoom.fill_between(np.arange(init,end),Re_analitico[init:end],Re_delta[init:end],color='tab:grey', alpha=0.2)\n", - "ax_zoom.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted')\n", - "\n", - "ax_zoom.set_title('Re difference',size=10)\n", - "#ax_zoom.set_xticks([])\n", - "#ax_zoom.set_yticks([])\n", - "axs2.indicate_inset_zoom(ax_zoom)\n", - "\n", - "fig.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Caracterización del error del cálculo de beta para diversos parámetros" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "beta2 = np.linspace(0.1,0.6,26)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "tE_I2 = np.linspace(1,5,41)\n", - "tI_R2 = tE_I2\n", - "sigma2 = 1/tE_I2\n", - "gamma2 = 1/tI_R2" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "sims2 = cv19sim(cfg,beta = list(beta2),mu=mu,I=I0,I_d=10,tE_I = list(tE_I2),tI_R = 'tE_I',t_end=250) " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "sims2.integrate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculos" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "I_d = []\n", - "delta2 = []\n", - "beta2_delta = []\n", - "beta2_error = []\n", - "for i in range(len(beta2)):\n", - " aux = []\n", - " auxdelta = []\n", - " auxbeta = []\n", - " auxerror = []\n", - " for j in range(len(tE_I2)):\n", - " Id_aux = sims2.sims[j][i].I_d\n", - " betaaux = sims2.sims[j][i].beta(0)\n", - " gamma_aux = 1/sims2.sims[j][i].tI_R(0)\n", - " sigma_aux = 1/sims2.sims[j][i].tE_I(0)\n", - " S_aux = sims2.sims[j][i].S\n", - " N_aux = sims2.sims[j][i].N\n", - " delta_aux = getdelta(Id_aux)\n", - " betaaux_delta = beta_delta(delta_aux,S_aux,N_aux,gamma_aux,sigma_aux)\n", - " auxdelta.append(delta_aux)\n", - " auxbeta.append(betaaux_delta)\n", - " auxerror.append(((betaaux_delta - betaaux)**2).mean())\n", - " delta2.append(auxdelta)\n", - " I_d.append(Id_aux)\n", - " beta2_delta.append(auxbeta)\n", - " beta2_error.append(auxerror)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(beta2)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(26, 41, 250)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.shape(beta2_delta)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err = beta2_error\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I2,beta2,err,levels)\n", - "cp2 = ax.contour(tE_I2,beta2,err,[0,0.005,err_max],colors='white',linestyles='dashed')\n", - "#cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Error obtaining beta from delta')\n", - "ax.set_xlabel('Infectious time')\n", - "ax.set_ylabel('Beta')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(26, 41, 250)" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.shape(beta2_delta)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "i = 14\n", - "\n", - "for j in range(int(len(tE_I2)/2)):\n", - " plt.plot(beta2_delta[i][2*j],label='tE_I: '+\"{0:.3g}\".format(tE_I2[2*j])+' days')\n", - "plt.axhline(beta2[i])\n", - "plt.title('Beta: '+str(beta2[i]))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import cm" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,int(len(beta2)/2+1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], - "source": [ - "j = 20\n", - "for i in range(int(len(beta2)/2)):\n", - " plt.plot(beta2_delta[i][2*j],label='beta: '+\"{0:.3g}\".format(beta2[i]),color=colors[i])\n", - " plt.axhline(beta2[i],linestyle='dashed',color=colors[i])\n", - "plt.title('tE_I: '+str(tE_I2[2*j]))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [], - "source": [ - "# I\n", - "j = 20\n", - "for i in range(int(len(beta2)/2)):\n", - " plt.plot(sims2.sims[2*j][i].I_d,label='beta: '+\"{0:.3g}\".format(sims2.sims[2*j][i].beta(0)),color=colors[i])\n", - " #plt.axhline(beta2[i],linestyle='dashed',color=colors[i])\n", - "plt.title('tE_I: '+str(tE_I2[2*j]))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "j = 20\n", - "\n", - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "for i in range(int(len(beta2)/2)):\n", - " axs2.plot(beta2_delta[i][2*j],label='beta: '+\"{0:.3g}\".format(beta2[i]),color=colors[i])\n", - " axs2.axhline(beta2[i],linestyle='dashed',color=colors[i])\n", - "\n", - "\n", - "axs = axs2.twinx()\n", - "\n", - "\n", - "for i in range(int(len(beta2)/2)):\n", - " plt.plot(sims2.sims[2*j][i].I_d,label='beta: '+\"{0:.3g}\".format(sims2.sims[2*j][i].beta(0)),color=colors[i])\n", - " #plt.axhline(beta2[i],linestyle='dashed',color=colors[i])\n", - "plt.title('tE_I: '+str(tE_I2[2*j]))\n", - "plt.legend(loc=0)\n", - "plt.show()\n", - "\n", - "axs.plot(range(len(beta_Re_analitico)),beta_Re_analitico,label='Beta analitico',color = 'tab:blue')\n", - "axs.plot(range(len(beta_Re_delta)),beta_Re_delta,label='Beta delta',color = 'tab:blue',linestyle='dashed')\n", - "#axs.plot(range(len(beta_Re_epyestim_Ts)),beta_Re_epyestim_Ts,label='beta Epyestim',color = 'tab:blue',linestyle='dotted')\n", - "\n", - "axs2.fill_between(range(len(Re_analitico[1:])), Re_analitico[1:], Re_delta[0:-1],color='tab:grey', alpha=0.4)\n", - "\n", - "axs.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted',label='Min beta')\n", - "\n", - "axs2.tick_params(axis='y', labelcolor='tab:red',size=10)\n", - "axs.tick_params(axis='y', labelcolor='tab:blue',size=10)\n", - "\n", - "axs2.set_ylabel('Re',color='tab:red',size=12)\n", - "axs.set_ylabel('Beta',color='tab:blue',size=12)\n", - "\n", - "#axs.set_ylim(0.3,0.6)\n", - "#axs2.set_ylim(0,2.5)\n", - "\n", - "fig.suptitle('tE_I: '+str(tE_I2[2*j]))\n", - "\n", - "lines, labels = axs.get_legend_handles_labels()\n", - "lines2, labels2 = axs2.get_legend_handles_labels()\n", - "axs2.legend(lines + lines2, labels + labels2, loc=[0.05,0.1])\n", - "#axs.legend(loc=[0.05,0.05])\n", - "#axs2.legend(loc=[0.05,0.17])\n", - "\n", - "ax_zoom = axs2.inset_axes([.67,.24,.3,.4])\n", - "\n", - "init = 80\n", - "end = 120\n", - "ax_zoom.plot(np.arange(init,end),Re_analitico[init:end],color = 'tab:red',linestyle='solid')\n", - "ax_zoom.plot(np.arange(init,end),Re_delta[init:end],color = 'tab:red',linestyle='dashed')\n", - "ax_zoom.fill_between(np.arange(init,end),Re_analitico[init:end],Re_delta[init:end],color='tab:grey', alpha=0.2)\n", - "ax_zoom.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted')\n", - "\n", - "ax_zoom.set_title('Re difference',size=10)\n", - "#ax_zoom.set_xticks([])\n", - "#ax_zoom.set_yticks([])\n", - "axs2.indicate_inset_zoom(ax_zoom)\n", - "\n", - "fig.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3000.0" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sims2.sims[0][0].I_d[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(sims2.sims[0][0].I_d)" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sims2.sims[0][0].beta(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "10%8" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(3,4,figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "for i in range(12):\n", - " for j in range(int(len(tE_I2)/2)):\n", - " axs[i%3,i%4].plot(beta2_delta[2*i][2*j],label='tE_I: '+\"{0:.3g}\".format(tE_I2[2*j])+' days')\n", - " axs[i%3,i%4].axhline(beta2[2*i])\n", - " axs[i%3,i%4].set_title('Beta: '+str(beta2[2*i]))\n", - " axs[i%3,i%4].legend(loc=0)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Análisis de sensibilidad de tiempo serial\n", - "Para hacer este estudio utilizaremos los mismos datos sintéticos y calcularemos el Re y beta utilizando distintos gammas y sigmas para ver como crece el error a medida que nos alejamos de los valores correctos" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "tE_I_s = np.arange(2,6,0.1)\n", - "tI_R_s = np.arange(2,6,0.1)\n", - "Ts_s = np.array([[i+j for i in tE_I_s] for j in tI_R_s])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "sigma_s = 1/tE_I_s\n", - "gamma_s = 1/tI_R_s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### MSE y RMSE Re vs Re_analitico\n", - "Re calculado para las distintas combinaciones de $\\sigma$ y $\\gamma$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "P = 1\n", - "# R delta\n", - "Re_delta_s_a = np.zeros(np.shape(Ts_s), dtype=object) \n", - "Re_delta_s = np.zeros(np.shape(Ts_s), dtype=object)\n", - "error_s = np.zeros(np.shape(Ts_s))\n", - "error_s_a = np.zeros(np.shape(Ts_s))\n", - "for i in range(len(sigma_s)):\n", - " for j in range(len(gamma_s)):\n", - " Re_delta_s[i,j] = Re_delta_calc(delta,sigma_s[i],gamma_s[j])\n", - " Re_delta_s_a[i,j] = Re_delta_a_calc(delta,sigma_s[i],gamma_s[j])\n", - "\n", - " error_s[i,j] = ((P*Re_delta_s[i,j] - P*Re_analitico[0:])**2).mean()\n", - " error_s_a[i,j] = ((P*Re_delta_s_a[i,j] - P*Re_analitico[0:])**2).mean() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err =error_s\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('MSE vs Re analítico')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err = np.sqrt(error_s)\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('RMSE vs Re analítico')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RMSE Re vs Re_delta\n", - "Re calculado para las distintas combinaciones de $\\sigma$ y $\\gamma$ vs el Re calculado con $\\delta$ para los valores con los que se hizo la simulación original" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "P = 1\n", - "# R delta\n", - "Re_delta_s_a = np.zeros(np.shape(Ts_s), dtype=object) \n", - "Re_delta_s = np.zeros(np.shape(Ts_s), dtype=object)\n", - "error_s_d = np.zeros(np.shape(Ts_s))\n", - "error_s_a_d = np.zeros(np.shape(Ts_s))\n", - "for i in range(len(sigma_s)):\n", - " for j in range(len(gamma_s)):\n", - " Re_delta_s[i,j] = Re_delta_calc(delta,sigma_s[i],gamma_s[j])\n", - " Re_delta_s_a[i,j] = Re_delta_a_calc(delta,sigma_s[i],gamma_s[j]) \n", - "\n", - " error_s_d[i,j] = ((P*Re_delta_s[i,j] - P*Re_delta[:])**2).mean()\n", - " error_s_a_d[i,j] = ((P*Re_delta_s_a[i,j] - P*Re_delta[:])**2).mean()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err =error_s_d\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('MSE vs Re delta')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err = np.sqrt(error_s_d)\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('RMSE vs Re delta')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error cálculo Re para distintos R0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta2 = list(np.arange(0.2,0.6,0.01))\n", - "R0 = np.array(beta2)*tI_R" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sims2 = cv19sim(cfg,beta = beta2,mu=mu,I=I0,I_d=I0*beta,tE_I = tE_I,tI_R=tI_R,t_end=250)\n", - "sims2.integrate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculo de R por delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t0=1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Re_analitico2 = [beta2[i]*sims2.sims[i].S[t0:-1]/sims2.sims[i].N*tI_R for i in range(len(beta2))]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "delta2 = [getdelta(sims2.sims[i].I_d[t0:]) for i in range(len(beta2))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# R delta\n", - "Re_delta2 = []\n", - "for j in delta2:\n", - " Re_delta2.append(Re_delta_calc(j,sigma,gamma))\n", - "\n", - "Re_delta2=np.array(Re_delta2)\n", - "\n", - "# R delta aprox\n", - "#Re_delta_a = 1 + delta*Ts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Error\n", - "err2 = []\n", - "for i in range(len(beta2)):\n", - " err2.append(np.sqrt((Re_analitico2[i][0:100]-Re_delta2[i][0:100]).mean()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(R0,err2)\n", - "plt.title('RMSE R vs R0')\n", - "plt.xlabel('R0')\n", - "plt.ylabel('RMSE')\n", - "plt.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error cálculo Re para distintos R0 y Ts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta3 = list(np.arange(0.2,0.6,0.01))\n", - "R0 = np.array(beta2)*tI_R\n", - "Ts3 = np.arange(1,6,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sims3 = []\n", - "for i in Ts3:\n", - " sims3.append(cv19sim(cfg,beta = beta2,mu=mu,I=I0,I_d=I0*beta,tE_I = i/2,tI_R=i/2,t_end=250))\n", - "\n", - "for i in range(len(Ts3)):\n", - " sims3[i].integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sims = np.array([i.sims for i in sims3])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t0 = 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Re_analitico3 = [[sims[i,j].beta(0)*sims[i,j].S[t0:-1]/sims[i,j].N*sims[i,j].tI_R(0) for j in range(len(beta3))]for i in range(len(Ts3))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "delta3 = [[getdelta(sims[i,j].I_d[t0:-1]) for j in range(len(beta3))]for i in range(len(Ts3))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 40\n", - "j = 39\n", - "aux = delta3[i][j]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(sims[i,j].I_d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(range(len(aux)),aux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# R delta\n", - "Re_delta3 = []\n", - "for j in delta2:\n", - " aux = []\n", - " for i in range(len(j)):\n", - " aux.append(1 + j[i]*Ts + sigma*gamma/(sigma + gamma)*(j[i]*Ts)**2)\n", - " Re_delta2.append(aux)\n", - "Re_delta2=np.array(Re_delta2)\n", - "\n", - "# R delta aprox\n", - "#Re_delta_a = 1 + delta*Ts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# To Do:\n", - "[ ] Comprobar el orden de las variables en los arreglos sigma-gamma \n", - "[ ] Verificar el calculo de R porque no me están calzando bien los R con las magnitudes de los otros.\n", - "[ ] Grid-plot \n", - "\n", - "\n", - "[ ] Estudiar cuanto aumenta el error a medida que cambian el tiempo serial, gamma y sigma \n", - "\n", - "[x] Calcular Beta desde el R \n", - "[ ] Reconstruir las curvas con los parámetros obtenidos \n", - "[ ] Tratar de compatibilizar los cálculos de R de Felipe y de la nueva librería, y compararlos con el cálculo analítico \n", - "\n", - "[ ] Revisar calculo del error entre los cálculos de R para el contourplot. \n", - "[ ] Revisar calculo del error entre los cálculos de R. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/CV19-ReproductiveNumber.ipynb b/research/CV19-ReproductiveNumber.ipynb deleted file mode 100644 index e16d2ec..0000000 --- a/research/CV19-ReproductiveNumber.ipynb +++ /dev/null @@ -1,469 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CV19SIM: EPIcSuite simulator interface\n", - "\n", - "Perdón la pobresa, work in progress. Será enriquecido " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import toml\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "from datetime import datetime\n", - "import matplotlib.dates as mdates\n", - "from matplotlib.ticker import MaxNLocator\n", - "\n", - "import epyestim\n", - "import epyestim.covid19 as covid19\n", - "from epyestim.distributions import discretise_gamma\n", - "from datetime import timedelta" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samuel/anaconda3/lib/python3.8/site-packages/rpy2/robjects/pandas2ri.py:17: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.\n", - " from pandas.core.index import Index as PandasIndex\n" - ] - } - ], - "source": [ - "# rpy2 imports\n", - "from rpy2.robjects.packages import importr\n", - "from rpy2.robjects import pandas2ri\n", - "import rpy2.robjects as robjects\n", - "pandas2ri.activate()\n", - "# R library\n", - "global eps\n", - "eps = importr(\"EpiEstim\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#Pop-up plots\n", - "import platform\n", - "OS = platform.system()\n", - "if OS == 'Linux' or OS == 'Darwin':\n", - " %matplotlib tk \n", - "elif OS == 'Windows':\n", - " %matplotlib qt \n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/samuel/covid19geomodeller\n" - ] - } - ], - "source": [ - "cd .." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from src2.models.cv19sim import cv19sim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Funciones para cálculo de R" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Analitico para simulación\n", - "def R_analytic(sim):\n", - " t = np.array(sim.t)\n", - " beta = np.array([sim.beta(j) for j in t])\n", - " S = np.array(sim.S)\n", - " tI_R = np.array([sim.tI_R(j) for j in t])\n", - " N = sim.N\n", - " return beta*S*tI_R/N" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Felipe\n", - "def R_effective(smoothed, window):\n", - " df = pd.DataFrame({\"dates\": smoothed.index.tolist(), \"cases\": smoothed.values})\n", - " rdf = pandas2ri.py2ri(df)\n", - " r = robjects.r\n", - " window = window # always 5, test\n", - " start = r.seq(2, df.values.shape[0]-window)\n", - " end = r.seq( 2+ window,df.values.shape[0])\n", - " results = eps.estimate_R(rdf[1], method=\"parametric_si\", config = \n", - " eps.make_config(t_start = start,\n", - " t_end = end,\n", - " mean_si = 4.03,\n", - " std_si = np.sqrt(6.5/(0.62**2))))\n", - " results2 = dict(results.items())\n", - " rhat = pandas2ri.ri2py(results2[\"R\"])\n", - " rhat.index = smoothed.index[2+window-1:]\n", - " return rhat" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Felipe\n", - "def R_effectivegamma(smoothed, window):\n", - " df = pd.DataFrame({\"dates\": smoothed.index.tolist(), \"cases\": smoothed.values})\n", - " rdf = pandas2ri.py2ri(df)\n", - " r = robjects.r\n", - " window = window # always 5, test\n", - " start = r.seq(2, df.values.shape[0]-window)\n", - " end = r.seq( 2+ window,df.values.shape[0])\n", - " results = eps.estimate_R(rdf[1], method=\"parametric_si\", config = \n", - " eps.make_config(si_parametric_distr = \"L\",t_start = start,\n", - " t_end = end,\n", - " mean_si = 5,\n", - " std_si = 2))\n", - " results2 = dict(results.items())\n", - " rhat = pandas2ri.ri2py(results2[\"R\"])\n", - " rhat.index = smoothed.index[2+window-1:]\n", - " return rhat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generación de datos sintéticos" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "cfgfile = 'config_files/SEIR.toml'\n", - "cfg = toml.load(cfgfile) # no es necesario, pero es util para ver el archivo de configuracion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creación objeto de multisimulación" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.5\n", - "#beta = '{\"function\":\"Events\",\"values\":[0.3,0.3,0.3],\"days\":[[0,135],[135,190],[190,500]]}'\n", - "tI_R = 3\n", - "tE_I = 3\n", - "population = 100000\n", - "I = 100\n", - "I_d = 20\n", - "R = 0\n", - "mu = 1.5" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "sims = cv19sim(cfg,beta=beta,tI_R=tI_R,tE_I=tE_I,R=R,population=population,I_det=I, I_d_det =I_d,mu=mu,t_end=500)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Resolver EDOS\n", - "sims.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "sim = sims.sims[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#plt.plot(sim.t,sim.S,label='S')\n", - "#plt.plot(sim.t,sim.E,label='E')\n", - "plt.plot(sim.t,sim.I_d,label='I')\n", - "#plt.plot(sim.t,sim.R,label='R')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cálculo de R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Solución Analítica\n", - "$$\n", - " R_e(t) = \\beta(t)\\frac{S(t)}{N}t_{IR}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "initdate = datetime(2020,3,1)\n", - "dates = [initdate+timedelta(days=int(j)) for j in sim.t]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "cases = pd.Series(data=sim.I_d,index=dates)\n", - "cases_ac = pd.Series(data=sim.I_ac,index=dates)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "R_felipe95 = R_effective(cases,7)['Quantile.0.95(R)']\n", - "R_felipe25 = R_effective(cases,7)['Quantile.0.25(R)']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "Ra = R_analytic(sim)\n", - "R_felipe = R_effective(cases,7)['Mean(R)']\n", - "R_felipegamma = R_effectivegamma(cases,7)['Mean(R)']\n", - "R_epistim = covid19.r_covid(cases[:-200],delay_distribution=np.array([1]),n_samples=300)['R_mean']" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "axs2.set_ylabel('R',color='tab:red')\n", - "axs2.plot(sim.t,Ra,color='tab:blue',label='Analytic')\n", - "#axs2.plot(sim.t[:len(R_felipe25)],R_felipe95,alpha=0.5,color='tab:red',label='Felipestim',linestyle='dashed')\n", - "#axs2.plot(sim.t[:len(R_felipe95)],R_felipe25,alpha=0.5,color='tab:red',label='Felipestim',linestyle='dashed')\n", - "\n", - "axs2.plot(sim.t[:len(R_felipe)],R_felipe,color='tab:red',label='Felipestim',linestyle='dashed')\n", - "#axs2.plot(sim.t[:len(R_felipe)],R_felipegamma,color='tab:green',label='Felipestimgamma',linestyle='dashed')\n", - "axs2.plot(sim.t[:len(R_epistim)],R_epistim,color='black',label='Epyestim',linestyle='dashed')\n", - "axs2.tick_params(axis='y', labelcolor='tab:red')\n", - "axs2.axhline(1,color='grey',linestyle='dotted')\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(sim.t,sim.I_d,color='tab:blue',label='Diarios',linestyle='solid')\n", - "#axs.plot(sim.t,sim.I,color='tab:blue',label='Activos')\n", - "axs.set_ylabel('Infectados',color='tab:blue')\n", - "axs.tick_params(axis='y', labelcolor='tab:blue')\n", - "\n", - "#axs2.set_xlim(0,200)\n", - "fig.suptitle('Infectados y R')\n", - "axs.legend(loc=1)\n", - "axs2.legend(loc=2)\n", - "fig.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "from epyestim.distributions import discretise_gamma" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Epyestim tests" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "si_distrb = covid19.generate_standard_si_distribution()\n", - "delay_distrb = covid19.generate_standard_infection_to_reporting_distribution()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(si_distrb)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function bagging_r in module epyestim.main:\n", - "\n", - "bagging_r(confirmed_cases: pandas.core.series.Series, gt_distribution: numpy.ndarray, delay_distribution: numpy.ndarray, a_prior: float, b_prior: float, smoothing_window: int, r_window_size: Union[int, NoneType] = None, r_interval_dates: Union[List[datetime.date], NoneType] = None, n_samples: int = 100, quantiles: Iterable[float] = (0.025, 0.5, 0.975), auto_cutoff: bool = True) -> pandas.core.frame.DataFrame\n", - " Compute aggregated bootstrapped R and returns aggregate quantiles\n", - "\n" - ] - } - ], - "source": [ - "help(epyestim.bagging_r)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ContagionDynamics/ModelComparison.ipynb b/research/ContagionDynamics/ModelComparison.ipynb deleted file mode 100644 index 0b8d7de..0000000 --- a/research/ContagionDynamics/ModelComparison.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import mplcyberpunk" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame?" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use(\"cyberpunk\")\n", - "mplcyberpunk.make_lines_glow()\n", - "mplcyberpunk.add_underglow()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "a = [0,1,2,3,4,5,6,8,9]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(a)\n", - "mplcyberpunk.make_lines_glow()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/COVID19_Examination_Dynamics.ipynb b/research/ExamDynamics/COVID19_Examination_Dynamics.ipynb deleted file mode 100644 index ac775a6..0000000 --- a/research/ExamDynamics/COVID19_Examination_Dynamics.ipynb +++ /dev/null @@ -1,1285 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examination Dynamics EDOs Study\n", - "\n", - "This jupyter notebook shows how periodic examinations affect a pandemy evolution.\n", - "\n", - "## Table of Contents:\n", - "* Model definition\n", - "* Import Libraries\n", - "* Single Simulation Study\n", - " * Parameter Settings\n", - " * SEIR plot\n", - " * Accumulated Infected\n", - " * New Daily Infected\n", - " * Ammount of exams\n", - "* Sensitivity Analysis: \n", - " * Examination Rate\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence\n", - " * Examination Period\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence\n", - " * Examination Duty\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence \n", - " * Examination Accuracy\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence \n", - "* Multidimensional Analysis:\n", - " * Examination rate vs Examination periods\n", - " * Examination Rate vs Duty\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Definition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{equation} \n", - "\\frac{dS}{dt} = -\\beta \\alpha \\frac{S I}{N+k_{I}I+k_{R}R}\n", - "\\end{equation}\n", - "\n", - "\\begin{equation} \n", - "\\frac{dE}{dt} = \\beta \\alpha \\frac{S I}{N+k_{I}I+k_{R}R} - \\sigma E \n", - "\\end{equation}\n", - "\\begin{equation} \n", - "\\frac{dI}{dt} = \\sigma E - \\gamma I - \\lambda_{e} \\sqcap(\\omega_{e} t) P_{I}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "\\frac{dR}{dt} = \\gamma I + \\lambda_{e} \\sqcap(\\omega_{e} t) P_{I}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "S_i+E_i+I_i+R_i = N_i\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "P_{I} = \\frac{I}{N}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "e(t) = \\lambda_{e} \\sqcap(\\omega_{e} t)\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "e_{I}(t) = \\lambda_{e} \\sqcap(\\omega_{e} t)\n", - "\\end{equation}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epidemiological Parameters\n", - "* **beta:** Infection rate\n", - "* **mu:** Initial exposed obtained from the initial infected mu=E0/I0\n", - "* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported)\n", - "* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics\n", - "* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIR/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows': \n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "\n", - "from class_SEIRQ import SEIR\n", - "from Quarantine import Quarantine\n", - "from Quarantine import Exams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 500\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "\n", - "testaccuracy = 0.9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single simulation study" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = 20000 # Persons per day\n", - "period = 1\n", - "duty = 1\n", - "# Construction of examination dynamics\n", - "psi = Exams(examrate,period,duty) # " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "simulation finished\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "\n", - "simulation = SEIR(tsim=tsim,alpha=s1.alpha,psi = psi,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy)\n", - "simulation.integr_sci(0,tsim,0.01)\n", - "# No exams dynamic for reference:\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIR Plot with Active infected" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.S,label='S',color = 'blue')\n", - "plt.plot(simulation.t,simulation.E,label='E',color = 'cyan')\n", - "plt.plot(simulation.t,simulation.I,label='I',color = 'red')\n", - "plt.plot(simulation.t,simulation.R,label='R',color = 'green')\n", - "plt.plot(simulation_reference.t,simulation_reference.S,label='S - No exams',color = 'blue',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.E,label='E - No exams',color = 'cyan',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.I,label='I - No exams',color = 'red',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.R,label='R - No exams',color = 'green',linestyle='dashed')\n", - "\n", - "plt.title('Epidemiological Plot')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Accumulated Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_ac,label='I_ac with exams')\n", - "plt.plot(simulation_reference.t,simulation_reference.I_ac,label='I_ac No Exams', linestyle='dashed')\n", - "plt.title('Accumulated Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot New Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_d,label='I_d with exams')\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='I_d no exams', linestyle='dashed')\n", - "plt.title('New Daily Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Amount of exams" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.e,label='Exams')\n", - "plt.title('Amount of exams performed')\n", - "plt.legend(loc=0)\n", - "plt.show()\n", - "print('We performed '+str(int(np.round(simulation.e[-1])))+' exams in total')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sensitivity Analysis: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination rate\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "duty = 1\n", - "examrate = list(np.arange(0.1,5,0.1)*population/100)# Persons per day\n", - "period = 1\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] \n", - "\n", - "testaccuracy=0.9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 2 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 3 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 4 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 5 tasks | elapsed: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 6 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=12)]: Done 7 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=12)]: Done 9 tasks | elapsed: 2.6s\n", - "[Parallel(n_jobs=12)]: Done 10 tasks | elapsed: 2.6s\n", - 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"[Parallel(n_jobs=12)]: Done 41 out of 49 | elapsed: 3.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 42 out of 49 | elapsed: 3.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 43 out of 49 | elapsed: 3.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 44 out of 49 | elapsed: 3.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 45 out of 49 | elapsed: 3.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 46 out of 49 | elapsed: 3.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 47 out of 49 | elapsed: 3.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 49 out of 49 | elapsed: 3.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 49 out of 49 | elapsed: 3.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "def simulate(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "sims = []\n", - "for i in range(len(examrate)): \n", - " sims.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy))\n", - " \n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],tsim) for i in range(len(sims)))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicatros\n", - "peak = []\n", - "for i in range(len(examrate)): \n", - " peak.append(100*simulation[i].peak/population)\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " peaktime.append(simulation[i].peak_t) \n", - " \n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " prevalence.append(simulation[i].prevalence_total[-1]) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,peak,label='Infected during peak')\n", - "plt.title('Peak size (%) per examrate')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(peak)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,peaktime,label='Time when peak occurs')\n", - "plt.title('Peak time shift')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(peaktime)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prevalence" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,prevalence,label='% Percentage of total population')\n", - "plt.title('Prevalence at the end of the simulation')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(prevalence)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Period\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Duty\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Accuracy\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multidimensional Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination rate vs Examination periods\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 0.5\n", - "\n", - "examrate = [1000,5000,10000,20000,50000] # Persons per day\n", - "period = [1,2,5,10,15,30]\n", - "\n", - "psi = [[Exams(i,j,duty) for j in period] for i in examrate] \n", - "\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Exam campaings" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samuel/anaconda3/lib/python3.8/inspect.py:2987: RuntimeWarning: coroutine 'InteractiveShell.run_cell_async' was never awaited\n", - " arg_val = kwargs.pop(param_name)\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n" - ] - } - ], - "source": [ - "n = 3\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]\n", - "#plt.plot(time,exams[i][j])\n", - "\n", - "fig, axs = plt.subplots(len(examrate), len(period))\n", - "for i in range(len(examrate)):\n", - " for j in range(len(period)):\n", - " axs[i,j].plot(time,exams[i][j],label='Examination Rate:'+str(examrate[i])+' Period: '+str(period[j]))\n", - " axs[i,j].legend(loc=0)\n", - " #axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.7s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 6 | elapsed: 2.3s remaining: 4.7s\n", - 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"[Parallel(n_jobs=12)]: Done 3 out of 6 | elapsed: 3.7s remaining: 3.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 6 | elapsed: 5.0s remaining: 2.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 15.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 6 | elapsed: 15.2s finished\n", - "ready\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Period Analysis\n", - "\n", - "### Period analysis over a single amount of exam rate" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'simulation_reference' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation_reference\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msimulation_reference\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mI_d\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'No exams'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'dashed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msimulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mI_d\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Examination Period: '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiod\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'simulation_reference' is not defined" - ] - } - ], - "source": [ - "j = 0\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='No exams',color = 'black',linestyle='dashed')\n", - "for i in range(len(period)):\n", - " plt.plot(simulation[j][i].t,simulation[j][i].I_d,label='Examination Period: '+str(period[i]))\n", - "plt.legend(loc=0)\n", - "plt.title('New Daily Infected | '+str(examrate[j])+' exams/day')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple rates for each period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(examrate)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(period)/n), n)\n", - "for j in range(len(period)):\n", - " axs[int(j/n),j%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for i in range(len(examrate)):\n", - " axs[int(j/n),j%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination Period: '+str(period[j]))\n", - " axs[int(j/n),j%n].legend(loc=0)\n", - " axs[int(j/n),j%n].set_title('Period: '+str(period[j])+' days')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exam Analysis\n", - "\n", - "### Exam analysis over a single amount of period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "j = -1\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - "for i in range(len(examrate)):\n", - " plt.plot(simulation[i][j].t,simulation[i][j].I_d,label='Exams per day: '+str(examrate[i]))\n", - "plt.legend(loc=0)\n", - "plt.title('New Daily Infected | Campaing periods: '+str(period[j]))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple periods for each exam rate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(period)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(period)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination Period: '+str(period[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contour plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 0.5\n", - "population = 1000000\n", - "examrate = np.array([0.1,0.3,0.5,0.8,1,1.5,2,2.5,3,4,5])*population/100 # Persons per day\n", - "period = [1,5,10,20,30,40,50,60,70,80,90]\n", - "\n", - "psi = [[Exams(i,j,duty) for j in period] for i in examrate] \n", - "\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "simulation2 = simulation\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(100*simulation2[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(simulation2[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)): \n", - " aux.append(simulation2[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak size proportion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size Proportion')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time shift" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SeroPrevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examrate vs duty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = [1000,5000,10000,20000,50000] # Persons per day\n", - "period = 30\n", - "duty = [0.1,0.25,0.5,0.75,1]\n", - "psi = [[Exams(i,period,j) for j in duty] for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Reference simulation\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(100*simulation[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GridPlot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(duty)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(duty)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination duty: '+str(duty[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(peak).transpose()[j],label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Relative Peak size (%)')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Peak Size')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(peaktime)[j],label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Peak day')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Peak time')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(prevalence).transpose()[j]*100,label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Prevalence %')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Prevalence')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contour plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak size')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Seroprevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,prevalence) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Prevalence')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/COVID19_Exams_ABernardin.ipynb b/research/ExamDynamics/COVID19_Exams_ABernardin.ipynb deleted file mode 100644 index 5cd3b9d..0000000 --- a/research/ExamDynamics/COVID19_Exams_ABernardin.ipynb +++ /dev/null @@ -1,332 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.integrate import odeint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PCR testing strategy in a COVID pandemic\n", - "\n", - "We have expanded a SEIR model where the population is being tested, and when an infectious person is detected, it gets isolated. The equations of the expandend SEIR model are:\n", - "\n", - "\\begin{equation}\n", - " \\begin{aligned}\n", - " \\frac{dS}{dt} &= \\frac{-\\beta SI}{N}-\\frac{\\beta SI_T}{N}-\\epsilon S\\\\\n", - " \\frac{dS_T}{dt} &= \\frac{-\\beta S_TI}{N}-\\frac{\\beta S_TI_T}{N}+\\epsilon S\\\\\n", - " \\frac{dE}{dt} &= \\frac{\\beta SI}{N}+\\frac{\\beta SI_T}{N} + \\frac{\\beta S_TI}{N}+\\frac{\\beta S_TI_T}{N} - \\sigma E - \\epsilon E\\\\\n", - " \\frac{dE_T}{dt} &= -\\sigma E_T + \\epsilon E\\\\\n", - " \\frac{dI}{dt} &= \\sigma E + \\sigma E_T - \\gamma I - \\epsilon I\\\\\n", - " \\frac{dI_T}{dt} &= - \\theta I_T - \\gamma I_T + \\epsilon I\\\\\n", - " \\frac{dI_I}{dt} &= \\theta I_T - \\gamma I_I\\\\\n", - " \\frac{dR}{dt} &= \\gamma (I+I_T+I_I) - \\epsilon R\\\\\n", - " \\frac{dR}{dt} &= \\epsilon R\\\\\n", - " \\end{aligned}\n", - "\\end{equation}\n", - "\n", - "where $\\epsilon$ is the testing rate and $\\theta$ the isolation rate. Now, if we consider that tested Infected, $I_T$ are immediately isolated, the above system reduce to:\n", - "\n", - "\\begin{equation}\n", - " \\begin{aligned}\n", - " \\frac{dS}{dt} &= \\frac{-\\beta SI}{N}-\\epsilon S\\\\\n", - " \\frac{dS_T}{dt} &= \\frac{-\\beta S_TI}{N}+\\epsilon S\\\\\n", - " \\frac{dE}{dt} &= \\frac{\\beta SI}{N} + \\frac{\\beta S_TI}{N}-\\sigma E-\\epsilon E\\\\\n", - " \\frac{dE_T}{dt} &= -\\sigma E_T + \\epsilon E\\\\\n", - " \\frac{dI}{dt} &= \\sigma E + \\sigma E_T - \\gamma I - \\epsilon I\\\\\n", - " \\frac{dI_T}{dt} &= - \\gamma I_T + \\epsilon I\\\\\n", - " \\frac{dR}{dt} &= \\gamma (I+I_T)-\\epsilon R\\\\\n", - " \\frac{dR_T}{dt} &= \\epsilon R\\\\\n", - " \\end{aligned}\n", - "\\end{equation}\n", - "\n", - "here, by simplicity, because we are reducing one parameter, we implement the second group of equations. In this way the system is composed by 4 parameters: $\\beta$, $\\sigma$, $\\gamma$ wich are biological parameters, and $\\epsilon$ which is the testing parameter.\n", - "\n", - "If external flux is entering the system, we should add $fI$ to the infected state, where $f$ is the proportion of new infected regarding actual infected.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#SEIR testing strategy model function\n", - "def model_SEIR_testing(y, t):\n", - " S0 = y[0]\n", - " S0_T = y[1]\n", - " E0 = y[2]\n", - " E0_T = y[3]\n", - " I0 = y[4]\n", - " I0_T = y[5]\n", - " R0 = y[6]\n", - " R0_T = y[7]\n", - " \n", - " dSdt = -beta*S0*I0/N - epsilon*S0\n", - " dS_Tdt = -beta*S0_T*I0/N + epsilon*S0\n", - " dEdt = beta*S0*I0/N + beta*S0_T*I0/N - sigma*E0 - epsilon*E0\n", - " dE_Tdt = -sigma*E0_T + epsilon*E0\n", - " dIdt = sigma*E0 + sigma*E0_T - gamma*I0 - epsilon*I0\n", - " dI_Tdt = -gamma*I0_T + epsilon*I0\n", - " dRdt = gamma*(I0+I0_T) - epsilon*R0\n", - " dR_Tdt = epsilon*R0\n", - " \n", - " return [dSdt, dS_Tdt, dEdt, dE_Tdt, dIdt, dI_Tdt, dRdt, dR_Tdt]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initial condition and running the model" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# initial parameters\n", - "beta = 0.2\n", - "sigma = 0.2 # 1/5 \n", - "gamma = 0.1 # 1/14\n", - "epsilon = 0.2#2\n", - " \n", - "# initial states conditions\n", - "Si = 1000000\n", - "Si_T = 0\n", - "Ei = 0\n", - "Ei_T = 0\n", - "Ii = 100\n", - "Ii_T = 0\n", - "Ri = 0\n", - "Ri_T = 0\n", - "\n", - "N = Si + Si_T + Ei + Ei_T + Ii + Ii_T + Ri + Ri_T\n", - "t = np.linspace(0, 400., 4000) # time grid, (from, to, number_of_step)\n", - "y0 = [Si, Si_T, Ei, Ei_T, Ii, Ii_T, Ri, Ri_T] # initial conditions vector\n", - " \n", - "# solve the ODEs\n", - "soln = odeint(model_SEIR_testing, y0, t) # (model, initial_conditions, time_series)\n", - "\n", - "# reshape the output of the solver to plot the states\n", - "S = soln[:, 0]\n", - "S_T = soln[:, 1]\n", - "E = soln[:, 2]\n", - "E_T = soln[:, 3]\n", - "I = soln[:, 4]\n", - "I_T = soln[:, 5]\n", - "R = soln[:, 6]\n", - "R_T = soln[:, 7]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# initial parameters\n", - "beta = 0.4\n", - "sigma = 0.2 # 1/5 \n", - "gamma = 0.071 # 1/14\n", - "epsilon = 0.2\n", - " \n", - "# initial states conditions\n", - "Si = 10000\n", - "Si_T = 0\n", - "Ei = 0\n", - "Ei_T = 0\n", - "Ii = 1\n", - "Ii_T = 0\n", - "Ri = 0\n", - "Ri_T = 0\n", - "\n", - "N = Si + Si_T + Ei + Ei_T + Ii + Ii_T + Ri + Ri_T\n", - "t = np.linspace(0, 400., 4000) # time grid, (from, to, number_of_step)\n", - "y0 = [Si, Si_T, Ei, Ei_T, Ii, Ii_T, Ri, Ri_T] # initial conditions vector\n", - " \n", - "# solve the ODEs\n", - "soln = odeint(model_SEIR_testing, y0, t) # (model, initial_conditions, time_series)\n", - "\n", - "# reshape the output of the solver to plot the states\n", - "S = soln[:, 0]\n", - "S_T = soln[:, 1]\n", - "E = soln[:, 2]\n", - "E_T = soln[:, 3]\n", - "I = soln[:, 4]\n", - "I_T = soln[:, 5]\n", - "R = soln[:, 6]\n", - "R_T = soln[:, 7]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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d5s23HQZmlqcpmTOQtBz4CPA4sCgidqdNe4BFaXkJsKPmYTtT23jtY51nvaQeST19fX1TUfqQc+e08ebbR6b0mGZmZ4u6w0DSbOBPgV+PiLdrt0VEAFHvOWqOtyEiuiOiu6ura6oOC8B55zgMzCxfdYWBpBYqQfDdiPhhan4zDf+Qvu9N7buAZTUPX5raxmtvqEVzWx0GZpateu4mEnAX8GJE/M+aTQ8A1TuC1gH317TfkO4qugI4kIaTHgKultSZJo6vTm0NtWhuGz9/9xjHBvyWFGaWn1Idj/048K+A5yQ9k9r+C/BV4F5JNwKvA59L2x4E1gC9wCHg8wAR0S/pK8CTab8vR0R/HXVNyqK5bQD0vXuUJfNmNfr0ZmZNNekwiIifABpn81Vj7B/ATeMcayOwcbK1TIXzUhjsOXDEYWBm2fErkJNz57YCeN7AzLLkMEhqewZmZrlxGCTzO8q0tRTYtf9ws0sxM2s4h0EiifPnt/P6W4eaXYqZWcM5DGqcP7+DHf0OAzPLj8OgxgUL2nmj/xCVG5/MzPLhMKhx/vx2Dh8/QZ/fvdTMMuMwqHH+gnYA3vC8gZllxmFQ44L5KQw8b2BmmXEY1Fja2U6xILb3HWx2KWZmDeUwqFEuFVixsIOf7Xmn2aWYmTWUw2CUD503h5ffdBiYWV4cBqNcuGgOb/Qf4uDRgWaXYmbWMA6DUf7BeXMA3Dsws6w4DEa5MIWB5w3MLCcOg1GWdbYzt63Eszv3N7sUM7OGcRiMUiiIVRd00vPavmaXYmbWMA6DMXRf0Mm2ve9y4NDxZpdiZtYQDoMxrLqgE4Cndrh3YGZ5cBiM4dJl82gpir9/5a1ml2Jm1hAOgzG0l0tcvmIBj/xsb7NLMTNrCIfBOK688Fx6977rdzA1syw4DMZx5YXnAvAXW/c0uRIzs+nnMBjH8oUdfHjpOdy3Zac/+czM3vMcBhP4bPcyfrbnHZ7bdaDZpZiZTSuHwQT++YffR0e5yB/97avNLsXMbFo5DCZwzqwWbvjYcn707P+jd6/fq8jM3rscBqfwq//0/cxuLfFf/8/znjsws/csh8EpzO8o86U1/5DHtvez8f++1uxyzMymhcPgNPyLX1jGL120iNv+bCs/fm53s8sxM5tyDoPTIIlvXH8pH142jy987yn+4NFeBk4MNrssM7MpM2PCQNJqSS9J6pV0c7PrGa29XOJ7/+YK1lyymN976CU+dcdP+OFTOzl87ESzSzMzq5tmwqSopCLwMvBLwE7gSeBXImLreI/p7u6Onp6eBlU4LCL48+f38PubX6Z377uUSwUuXzGfi993Dh86bzbnzZ1F15xWFnSUmVUu0loqIKnhdZqZjSZpS0R0j7Wt1OhixnEZ0BsR2wEk3QOsBcYNg2aRxLX/aDGrLzmPv9/+Fn+5dS9/98rPuesn2zl+4uRglaCtVKStpUCpWKAgEKp8lygUoCBRkJBgJsXGTAmxmVGF2czQ2V7m3n/70Sk/7kwJgyXAjpr1ncDlo3eStB5YD3D++ec3prJxSOJjH1jIxz6wEIBjA4O80X+Qve8cpe+do/QfPMbh4yc4cuwERwYGOXzsBAODQUQwGEEEDAZD64MBJ2ZAL23IDCklZkohZjPE3LaWaTnuTAmD0xIRG4ANUBkmanI5I5RLBT547hw+eO6cZpdiZnbGZsoE8i5gWc360tRmZmYNMFPC4ElgpaQVksrA9cADTa7JzCwbM2KYKCIGJP0a8BBQBDZGxAtNLsvMLBszIgwAIuJB4MFm12FmlqOZMkxkZmZN5DAwMzOHgZmZOQzMzIwZ8t5EkyGpD3h9kg9fCPx8CsuZKq7rzLiuM+O6zsx7sa4LIqJrrA1nbRjUQ1LPeG/W1Eyu68y4rjPjus5MbnV5mMjMzBwGZmaWbxhsaHYB43BdZ8Z1nRnXdWayqivLOQMzMxsp156BmZnVcBiYmVleYSBptaSXJPVKurkJ539N0nOSnpHUk9rmS9osaVv63pnaJemOVOuzklZNYR0bJe2V9HxN2xnXIWld2n+bpHXTVNdvS9qVrtkzktbUbLsl1fWSpGtq2qf05yxpmaRHJW2V9IKkL6b2pl6zCepq6jWT1CbpCUk/TXX999S+QtLj6Rw/SG9Xj6TWtN6bti8/Vb1TXNe3Jb1ac70uTe0N+91PxyxKelrSj9J6Y69XRGTxReWtsV8B3g+UgZ8CFzW4hteAhaPafhe4OS3fDHwtLa8BfkzlI4CvAB6fwjo+AawCnp9sHcB8YHv63pmWO6ehrt8G/vMY+16UfoatwIr0sy1Ox88ZWAysSstzgJfT+Zt6zSaoq6nXLD3v2Wm5BXg8XYd7getT+zeBf5eWvwB8My1fD/xgonqnoa5vA58ZY/+G/e6n4/5H4HvAj9J6Q69XTj2Dy4DeiNgeEceAe4C1Ta4JKjVsSsubgOtq2u+OiseAeZIWT8UJI+JvgP4667gG2BwR/RGxD9gMrJ6GusazFrgnIo5GxKtAL5Wf8ZT/nCNid0Q8lZbfAV6k8rndTb1mE9Q1noZcs/S8302rLekrgCuB+1L76OtVvY73AVdJ0gT1TnVd42nY776kpcCngG+lddHg65VTGCwBdtSs72TifzjTIYC/kLRF0vrUtigidqflPcCitNzoes+0jkbW92upm76xOhTTrLpSl/wjVP5XOWOu2ai6oMnXLA15PAPspfLH8hVgf0QMjHGOofOn7QeABY2oKyKq1+u2dL1ul9Q6uq5R55+On+PXgd8EBtP6Ahp8vXIKg5ngFyNiFXAtcJOkT9RujEpfr+n3+s6UOpI7gQ8AlwK7gd9vViGSZgN/Cvx6RLxdu62Z12yMupp+zSLiRERcSuXzzC8DLmx0DWMZXZekS4BbqNT3C1SGfn6rkTVJ+jSwNyK2NPK8o+UUBruAZTXrS1Nbw0TErvR9L/C/qfwjebM6/JO+7027N7reM62jIfVFxJvpH/Ag8EcMd3sbWpekFip/cL8bET9MzU2/ZmPVNVOuWaplP/Ao8FEqwyzVT1esPcfQ+dP2c4C3GlTX6jTcFhFxFPhjGn+9Pg78sqTXqAzRXQl8g0Zfr3omPM6mLyof8bmdysRKdZLs4gaevwOYU7P8d1TGGX+PkZOQv5uWP8XIyasnprie5YycqD2jOqj8D+pVKhNonWl5/jTUtbhm+TeojIkCXMzIybLtVCZCp/znnJ773cDXR7U39ZpNUFdTrxnQBcxLy7OAvwU+DfwJIydEv5CWb2LkhOi9E9U7DXUtrrmeXwe+2ozf/XTsTzI8gdzQ6zVlf1zOhi8qdwe8TGX88ksNPvf70w/qp8AL1fNTGet7GNgG/GX1lyr9Av5BqvU5oHsKa/k+leGD41TGFW+cTB3Av6YySdULfH6a6vpOOu+zwAOM/EP3pVTXS8C10/VzBn6RyhDQs8Az6WtNs6/ZBHU19ZoB/xh4Op3/eeC/1fwbeCI99z8BWlN7W1rvTdvff6p6p7iuR9L1eh74XwzfcdSw3/2a436S4TBo6PXy21GYmVlWcwZmZjYOh4GZmTkMzMzMYWBmZjgMzMwMh4GZmeEwMDMz4P8Dnfeo8Uh/hZcAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(S)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 10)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m10\u001b[0m\n\u001b[0;31m plt.plot(t, E_T, labilon = 0.0el='Exposed tested')\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "plt.plot(t, S, label='Susceptible')\n", - "plt.plot(t, E, label='Exposed')\n", - "plt.plot(t, I, label='Infected')\n", - "plt.plot(t, R, label='Removed')\n", - "plt.title(\"states\")\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "plt.plot(t, S_T, label='Susceptible tested')\n", - "plt.plot(t, E_T, labilon = 0.0el='Exposed tested')\n", - "plt.plot(t, I_T, label='Infected tested')\n", - "plt.plot(t, R_T, label='Removed tested')\n", - "plt.title(\"tested states\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "epsilon" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.05526265376997764" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "R[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5670.175313936595" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "R[-1]+R_T[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "a = S+S_T+E+E_T+I+I_T+R+R_T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/research/ExamDynamics/COVID19_SEIRQ.ipynb b/research/ExamDynamics/COVID19_SEIRQ.ipynb deleted file mode 100644 index f6a8b5b..0000000 --- a/research/ExamDynamics/COVID19_SEIRQ.ipynb +++ /dev/null @@ -1,1450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examination Dynamics Study\n", - "\n", - "This jupyter notebook shows how periodic examinations affect a pandemy evolution.\n", - "\n", - "## Table of Contents:\n", - "* Model definition\n", - "* Import Libraries\n", - "* Single Simulation Study\n", - " * Parameter Settings\n", - " * SEIR plot\n", - " * Accumulated Infected\n", - " * New Daily Infected\n", - " * Ammount of exams\n", - "* Sensitivity Analysis: \n", - " * Examination Rate\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence\n", - " * Examination Period\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence\n", - " * Examination Duty\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence \n", - " * Examination Accuracy\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence \n", - "* Multidimensional Analysis:\n", - " * Examination rate vs Examination periods\n", - " * Examination Rate vs Duty\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Definition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{equation} \n", - "\\frac{dS}{dt} = -\\beta \\alpha \\frac{S I}{N+k_{I}I+k_{R}R}\n", - "\\end{equation}\n", - "\n", - "\\begin{equation} \n", - "\\frac{dE}{dt} = \\beta \\alpha \\frac{S I}{N+k_{I}I+k_{R}R} - \\sigma E \n", - "\\end{equation}\n", - "\\begin{equation} \n", - "\\frac{dI}{dt} = \\sigma E - \\gamma I - \\lambda_{e} \\sqcap(\\omega_{e} t) P_{I}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "\\frac{dR}{dt} = \\gamma I + \\lambda_{e} \\sqcap(\\omega_{e} t) P_{I}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "S_i+E_i+I_i+R_i = N_i\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "P_{I} = \\frac{I}{N}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "e(t) = \\lambda_{e} \\sqcap(\\omega_{e} t)\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "e_{I}(t) = \\lambda_{e} \\sqcap(\\omega_{e} t)\n", - "\\end{equation}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epidemiological Parameters\n", - "* **beta:** Infection rate\n", - "* **mu:** Initial exposed obtained from the initial infected mu=E0/I0\n", - "* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported)\n", - "* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics\n", - "* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows': \n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "\n", - "from class_SEIRQ import SEIR\n", - "from Quarantine import Quarantine\n", - "from Quarantine import Exams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 500\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "\n", - "testaccuracy = 0.9\n", - "# Proportion of effectively quarantined detected infected\n", - "lambda_Q=1\n", - "# Time between test and quarantine\n", - "T_T = 1 \n", - "# Time in quarantine\n", - "T_Q = 14\n", - "# Tracing: How many extra infected you can detect for each infected detected through random search\n", - "lambda_Tr=0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single simulation study" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = 20000 # Persons per day\n", - "period = 1\n", - "duty = 1\n", - "psi = Exams(examrate,period,duty) # " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "\n", - "simulation = SEIR(tsim=tsim,alpha=s1.alpha,psi = psi,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy,\n", - " lambda_Q=lambda_Q,T_T = T_T,T_Q = T_Q,lambda_Tr=lambda_Tr)\n", - "simulation.integr_sci(0,tsim,0.01)\n", - "# No exams dynamic for reference:\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIR Plot with Active infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.S,label='S',color = 'blue')\n", - "plt.plot(simulation.t,simulation.E,label='E',color = 'cyan')\n", - "plt.plot(simulation.t,simulation.I,label='I',color = 'red')\n", - "plt.plot(simulation.t,simulation.R,label='R',color = 'green')\n", - "plt.plot(simulation.t,simulation.Q,label='Q',color = 'purple')\n", - "plt.plot(simulation_reference.t,simulation_reference.S,label='S - No exams',color = 'blue',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.E,label='E - No exams',color = 'cyan',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.I,label='I - No exams',color = 'red',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.R,label='R - No exams',color = 'green',linestyle='dashed')\n", - "\n", - "plt.title('Epidemiological Plot')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quarantine dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.Q,label='Q',color = 'purple')\n", - "plt.plot(simulation.t,simulation.I_T,label='I_t',color = 'red')\n", - "plt.title('Epidemiological Plot')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Accumulated Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_ac,label='I_ac with exams')\n", - "plt.plot(simulation_reference.t,simulation_reference.I_ac,label='I_ac No Exams', linestyle='dashed')\n", - "plt.title('Accumulated Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot New Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_d,label='I_d with exams')\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='I_d no exams', linestyle='dashed')\n", - "plt.title('New Daily Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Amount of exams" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.e,label='Exams')\n", - "plt.title('Amount of exams performed')\n", - "plt.legend(loc=0)\n", - "plt.show()\n", - "print('We performed '+str(int(np.round(simulation.e[-1])))+' exams in total')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sensitivity Analysis: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination rate\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "duty = 1\n", - "examrate = list(np.arange(0.1,5,0.1)*population/100)# Persons per day\n", - "period = 1\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] \n", - "\n", - "testaccuracy=0.9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "def simulate(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "sims = []\n", - "for i in range(len(examrate)): \n", - " sims.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy))\n", - " \n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],tsim) for i in range(len(sims)))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicatros\n", - "peak = []\n", - "for i in range(len(examrate)): \n", - " peak.append(100*simulation[i].peak/population)\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " peaktime.append(simulation[i].peak_t) \n", - " \n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " prevalence.append(simulation[i].prevalence_total[-1]) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,peak,label='Infected during peak')\n", - "plt.title('Peak size (%) per examrate')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(peak)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,peaktime,label='Time when peak occurs')\n", - "plt.title('Peak time shift')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(peaktime)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,prevalence,label='% Percentage of total population')\n", - "plt.title('Prevalence at the end of the simulation')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(prevalence)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Period\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Duty\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Accuracy\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multidimensional Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination rate vs Examination periods\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 0.5\n", - "\n", - "examrate = [1000,5000,10000,20000,50000] # Persons per day\n", - "period = [1,2,5,10,15,30]\n", - "\n", - "psi = [[Exams(i,j,duty) for j in period] for i in examrate] \n", - "\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Exam campaings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = 3\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]\n", - "#plt.plot(time,exams[i][j])\n", - "\n", - "fig, axs = plt.subplots(len(examrate), len(period))\n", - "for i in range(len(examrate)):\n", - " for j in range(len(period)):\n", - " axs[i,j].plot(time,exams[i][j],label='Examination Rate:'+str(examrate[i])+' Period: '+str(period[j]))\n", - " axs[i,j].legend(loc=0)\n", - " #axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Period Analysis\n", - "\n", - "### Period analysis over a single amount of exam rate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "j = 0\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='No exams',color = 'black',linestyle='dashed')\n", - "for i in range(len(period)):\n", - " plt.plot(simulation[j][i].t,simulation[j][i].I_d,label='Examination Period: '+str(period[i]))\n", - "plt.legend(loc=0)\n", - "plt.title('New Daily Infected | '+str(examrate[j])+' exams/day')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple rates for each period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(examrate)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(period)/n), n)\n", - "for j in range(len(period)):\n", - " axs[int(j/n),j%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for i in range(len(examrate)):\n", - " axs[int(j/n),j%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination Period: '+str(period[j]))\n", - " axs[int(j/n),j%n].legend(loc=0)\n", - " axs[int(j/n),j%n].set_title('Period: '+str(period[j])+' days')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exam Analysis\n", - "\n", - "### Exam analysis over a single amount of period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "j = -1\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - "for i in range(len(examrate)):\n", - " plt.plot(simulation[i][j].t,simulation[i][j].I_d,label='Exams per day: '+str(examrate[i]))\n", - "plt.legend(loc=0)\n", - "plt.title('New Daily Infected | Campaing periods: '+str(period[j]))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple periods for each exam rate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(period)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(period)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination Period: '+str(period[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contour plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 0.5\n", - "population = 1000000\n", - "examrate = np.array([0.1,0.3,0.5,0.8,1,1.5,2,2.5,3,4,5])*population/100 # Persons per day\n", - "period = [1,5,10,20,30,40,50,60,70,80,90]\n", - "\n", - "psi = [[Exams(i,j,duty) for j in period] for i in examrate] \n", - "\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "simulation2 = simulation\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(100*simulation2[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(simulation2[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)): \n", - " aux.append(simulation2[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak size proportion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size Proportion')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time shift" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SeroPrevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examrate vs duty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = [1000,5000,10000,20000,50000] # Persons per day\n", - "period = 30\n", - "duty = [0.1,0.25,0.5,0.75,1]\n", - "psi = [[Exams(i,period,j) for j in duty] for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Reference simulation\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(100*simulation[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GridPlot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(duty)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(duty)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination duty: '+str(duty[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(peak).transpose()[j],label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Relative Peak size (%)')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Peak Size')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(peaktime)[j],label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Peak day')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Peak time')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(prevalence).transpose()[j]*100,label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Prevalence %')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Prevalence')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contour plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak size')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Seroprevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,prevalence) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Prevalence')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test results delay analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "duty = 1\n", - "examrate = list(np.arange(0.1,5,0.1)*population/100)# Persons per day\n", - "period = 1\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] \n", - "\n", - "testaccuracy=0.9\n", - "T_T = [1,2,3,4,5]\n", - "lambda_Tr = 0\n", - "\n", - "# Proportion of effectively quarantined detected infected\n", - "lambda_Q=1\n", - "\n", - "# Time in quarantine\n", - "T_Q = 14\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = np.array([1000,5000,10000,20000,50000]) # Persons per day\n", - "period = 30\n", - "duty = 1\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T[j],T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "simulation finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0532s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0522s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0492s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0555s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0411s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "ready\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Reference simulation\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(100*simulation[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GridPlot" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'New Daily Infected')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(T_T)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(T_T)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination results delay: '+str(T_T[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n", - "fig.suptitle('New Daily Infected')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Infected waiting for test results')" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(T_T)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_T,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(T_T)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_T,label='Examination results delay: '+str(T_T[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n", - "fig.suptitle('Infected waiting for test results')" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(T_T,examrate*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size Proportion')\n", - "ax.set_xlabel('Results Delay')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/Exams Rate and Delay.ipynb b/research/ExamDynamics/Exams Rate and Delay.ipynb deleted file mode 100644 index e3a77b7..0000000 --- a/research/ExamDynamics/Exams Rate and Delay.ipynb +++ /dev/null @@ -1,2755 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examination dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIR/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows': \n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "\n", - "from class_SEIRQ import SEIR\n", - "from Quarantine import Quarantine\n", - "from Quarantine import Exams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 0 # E0/I0 initial rate\n", - "sigma = 0.2\n", - "gamma = 0.1\n", - "\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 100000\n", - "# Initial Active Infected \n", - "I0 = 100\n", - "I_ac0 = 100\n", - "\n", - "# Quarantine\n", - "s1 = Quarantine(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exam Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 1\n", - "period = 1\n", - "testaccuracy=0.9\n", - "lambda_Tr = 0 # traceability\n", - "lambda_Q=1 # Proportion of effectively quarantined detected infected\n", - "T_Q = 14 # Time in quarantine\n", - "\n", - "# Exam Rate\n", - "examrate = np.arange(1,20,1)*population/100# Persons per day\n", - "\n", - "# Delay Time\n", - "T_T = [1,2,3,4,5]\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(examrate)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta,sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T[j],T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "simulation finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 1.0s remaining: 1.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 1.0s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 1.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 1.0s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.7s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.7s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.7s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.7s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0654s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.6s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.6s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0471s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0405s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0490s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0354s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0362s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0362s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0388s.) 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0443s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0508s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "ready\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Reference simulation\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,sigma=sigma,gamma=gamma,I0=I0,population=population,expinfection=0,SeroPrevFactor=1)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(100*simulation[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)\n", - " \n", - " \n", - "# Calculate indicators\n", - "peakvsnoexam = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(100*simulation[i][j].peak/simulation_reference.peak)\n", - " peakvsnoexam.append(aux)\n", - " \n", - "# Calculate indicators\n", - "totinfected = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(T_T)):\n", - " aux.append(100*simulation[i][j].I_ac[-1]/simulation_reference.I_ac[-1])\n", - " totinfected.append(aux) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'New Daily Infected')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(T_T)))\n", - "n = 4\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(T_T)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination results delay: '+str(T_T[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]/population*100)+'%')\n", - "fig.suptitle('New Daily Infected')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(T_T)))\n", - "n = 4\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_T,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(T_T)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_T,label='Examination results delay: '+str(T_T[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n", - "fig.suptitle('Infected waiting for test results')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(T_T,examrate*100/population,peakvsnoexam) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size % respect to no exams scenario')\n", - "ax.set_xlabel('Results Delay (days)')\n", - "ax.set_ylabel('Normalized (%) Exams Rate')\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.min(totinfected)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(T_T,examrate*100/population,totinfected,10) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size % respect to no exams scenario')\n", - "ax.set_xlabel('Results Delay (days)')\n", - "ax.set_ylabel('Normalized (%) Exams Rate')\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "totinfected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "examrate/population*100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Delay Days Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(examrate)):\n", - " plt.plot(T_T,peakvsnoexam[i],label='Exam Rate: '+str(examrate[i]))\n", - "plt.title('Exam results delay effects: Peak Size respect to no exam application')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(examrate)):\n", - " plt.plot(T_T,peak[i]/peak[i][0],label='Exam Rate: '+str(examrate[i]))\n", - "plt.title('Exam results delay effects: Peak Size respect to minimum delay')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Beta Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mu = 0 # E0/I0 initial rate\n", - "sigma = 0.2\n", - "gamma = 0.1\n", - "\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 100000\n", - "# Initial Active Infected \n", - "I0 = 100\n", - "I_ac0 = 100\n", - "\n", - "# Quarantine\n", - "s1 = Quarantine(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 1\n", - "period = 1\n", - "testaccuracy=0.9\n", - "lambda_Tr = 0 # traceability\n", - "lambda_Q=1 # Proportion of effectively quarantined detected infected\n", - "T_Q = 14 # Time in quarantine\n", - "\n", - "# Exam Rate\n", - "examrate = np.arange(1,20,1)*population/100# Persons per day\n", - "# Delay Time\n", - "T_T = 2\n", - "beta = np.arange(0.1,0.2,0.01)\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims_reference = []\n", - "for i in range(len(beta)):\n", - " sims_reference.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta[i],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T,T_Q = T_Q,lambda_Tr=lambda_Tr))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For parallel simulation\n", - "def simulate_reference(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "# Reference simulation\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation_reference = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate_reference)(sims_reference[i],tsim) for i in range(len(beta)))\n", - "\n", - "print('simulation finished')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta[j],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T,T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(100*simulation[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)\n", - " \n", - " \n", - "# Calculate indicators\n", - "peakvsnoexam = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(100*simulation[i][j].peak/simulation_reference[j].peak)\n", - " peakvsnoexam.append(aux)\n", - "# Calculate indicators\n", - "totinfected = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(100*simulation[i][j].I_ac[-1]/simulation_reference[j].I_ac[-1])\n", - " totinfected.append(aux) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Grid Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(beta)))\n", - "n = 4\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(beta)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Beta: '+str(beta[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]/population*100)+'%')\n", - "fig.suptitle('New Daily Infected')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(T_T)))\n", - "n = 4\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_T,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(T_T)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_T,label='Examination results delay: '+str(T_T[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n", - "fig.suptitle('Infected waiting for test results')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(beta,examrate*100/population,totinfected,20) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Total Infected respect to no exams scenario')\n", - "ax.set_xlabel('beta')\n", - "ax.set_ylabel('Normalized (%) Exams Rate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Beta, delay and exam rate\n", - "exam rate i \n", - "beta j \n", - "delay days k \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mu = 0 # E0/I0 initial rate\n", - "sigma = 0.2\n", - "gamma = 0.1\n", - "\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 100000\n", - "# Initial Active Infected \n", - "I0 = 100\n", - "I_ac0 = 100\n", - "\n", - "# Quarantine\n", - "s1 = Quarantine(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 1\n", - "period = 1\n", - "testaccuracy=0.9\n", - "lambda_Tr = 0 # traceability\n", - "lambda_Q=1 # Proportion of effectively quarantined detected infected\n", - "T_Q = 14 # Time in quarantine\n", - "\n", - "# Exam Rate\n", - "examrate = np.arange(1,21,1)*population/100# Persons per day\n", - "\n", - "# Delay Time\n", - "T_T = [1,2,3,4,5]\n", - "# Beta\n", - "beta = np.arange(0.11,0.21,0.01)\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims_reference = []\n", - "for i in range(len(beta)):\n", - " sims_reference.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta[i],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = 1,T_Q = T_Q,lambda_Tr=lambda_Tr))" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 10 | elapsed: 1.1s remaining: 4.3s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 10 | elapsed: 1.2s remaining: 2.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 10 | elapsed: 1.3s remaining: 1.9s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 10 | elapsed: 1.3s remaining: 1.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 10 | elapsed: 1.3s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 10 | elapsed: 1.3s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 10 | elapsed: 1.4s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 10 | elapsed: 1.4s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 10 | elapsed: 1.4s finished\n", - "simulation finished\n" - ] - } - ], - "source": [ - "# For parallel simulation\n", - "def simulate_reference(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "# Reference simulation\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation_reference = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate_reference)(sims_reference[i],tsim) for i in range(len(beta)))\n", - "\n", - "print('simulation finished')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta[j],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T[k],T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " aux.append(aux2)\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0625s.) 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0423s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0343s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0412s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0450s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0413s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0639s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0603s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0636s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0659s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0616s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0394s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0358s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0320s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0558s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0465s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0499s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0488s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0657s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0611s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0600s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0450s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0425s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0361s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0452s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0415s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0556s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0514s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0533s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0680s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0588s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0474s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0404s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0382s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0423s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0408s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0422s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0900s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0682s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0718s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0658s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0529s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0475s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0375s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0385s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0363s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0408s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0525s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0430s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0642s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0569s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0457s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0484s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0418s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0382s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0355s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0399s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0470s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0497s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0488s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0576s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0473s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0432s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0401s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.0s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0423s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0398s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0433s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0384s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0528s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0440s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0683s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0471s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0453s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0424s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0380s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0373s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0672s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0465s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0442s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0533s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0540s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0529s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0449s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0425s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0418s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0406s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0352s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0395s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0387s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0486s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0573s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0481s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0450s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0497s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0458s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0398s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0415s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0358s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0379s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0504s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0421s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0451s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0538s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0483s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0459s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0440s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0437s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0379s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0408s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0625s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0352s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0561s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0578s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0484s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0449s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0476s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0442s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0385s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0400s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0506s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0399s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0478s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0563s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0540s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0440s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0407s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0433s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0375s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0440s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0400s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0402s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0465s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0471s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0409s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0411s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0443s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0392s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0365s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0422s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0405s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0374s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0469s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0538s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0504s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0530s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0683s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0387s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0416s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0392s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0450s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0452s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0517s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0497s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0544s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0457s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0420s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0377s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0393s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0431s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0447s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0504s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0509s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0471s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0483s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0477s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0476s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0417s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0401s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0437s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0390s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0410s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0533s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0499s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0463s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0492s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0497s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0494s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0467s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0658s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0446s.) 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 5 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 5 | elapsed: 0.1s finished\n", - "ready\n" - ] - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "# For parallel simulation\n", - "def simulate(simulation,k,tsim):\n", - " simulation[k].integr_sci(0,tsim,0.1)\n", - " return simulation[k]\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i][j],k,tsim) for k in range(len(sims[i][j]))))\n", - " simulation.append(aux)\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(100*simulation[i][j][k].peak/population)\n", - " aux.append(aux)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(simulation[i][j][k].peak_t)\n", - " aux.append(aux2)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(simulation[i][j][k].prevalence_total[-1]) \n", - " aux.append(aux2)\n", - " prevalence.append(aux)\n", - "prevalence = np.array(prevalence)\n", - " \n", - "# Calculate indicators\n", - "peakvsnoexam = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(100*simulation[i][j][k].peak/simulation_reference[j].peak)\n", - " aux.append(aux2)\n", - " peakvsnoexam.append(aux)\n", - "peakvsnoexam = np.array(peakvsnoexam)\n", - "# Calculate indicators\n", - "totinfected = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(100*simulation[i][j][k].I_ac[-1]/simulation_reference[j].I_ac[-1])\n", - " aux.append(aux2)\n", - " totinfected.append(aux)\n", - "\n", - "totinfected = np.array(totinfected)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(20, 10, 5)" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.shape(prevalence)" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "levels = np.arange(0,105,5)\n", - "#levels = [0,5,100]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 1: Grid Epidemic plots\n" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'New Daily Infected')" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(beta)))\n", - "n = 4\n", - "j = 4\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference[j].t,simulation_reference[j].I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for k in range(len(T_T)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j][k].t,simulation[i][j][k].I_d,label='Delay days: '+str(T_T[k]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(round(examrate[i]/population*100))+'%')\n", - "fig.suptitle('New Daily Infected')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 2: Tests vs Delay para distintos beta" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Total infected proportion to the dynamic with no exams')" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Contour Plot\n", - "n = 3\n", - "fig,ax=plt.subplots(4,3)\n", - "for j in range(len(beta)): \n", - " cp = ax[int(j/n),j%n].contourf(T_T,examrate*100/population,totinfected[:,j,:],levels) \n", - " ax[int(j/n),j%n].set_title('Beta: '+str(round(beta[j],2)))\n", - " ax[int(j/n),j%n].set_xlabel('Delay days')\n", - " ax[int(j/n),j%n].set_ylabel('Normalized (%) Exams Rate')\n", - " fig.colorbar(cp, ax=ax[int(j/n),j%n])\n", - "plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n", - "fig.suptitle('Total infected proportion to the dynamic with no exams')" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Total infected proportion to the dynamic with no exams')" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Contour Plot\n", - "n = 3\n", - "fig,ax=plt.subplots(4,3)\n", - "for j in range(len(beta)): \n", - " cp = ax[int(j/n),j%n].contourf(T_T,examrate*100/population,100*np.array(prevalence)[:,j,:],np.arange(0,105,5))\n", - " ax[int(j/n),j%n].set_title('Beta: '+str(round(beta[j],2)))\n", - " ax[int(j/n),j%n].set_xlabel('Delay days')\n", - " ax[int(j/n),j%n].set_ylabel('Normalized (%) Exams Rate')\n", - " fig.colorbar(cp, ax=ax[int(j/n),j%n])\n", - "plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n", - "fig.suptitle('Total infected proportion to the dynamic with no exams')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.array)prevalence[]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 3: Test vs Beta para distintos delay" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Total infected proportion to the dynamic with no exams')" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Contour Plot\n", - "n = 3\n", - "fig,ax=plt.subplots(2,3)\n", - "for k in range(len(T_T)): \n", - " cp = ax[int(k/n),k%n].contourf(beta,examrate*100/population,totinfected[:,:,k],levels) \n", - " ax[int(k/n),k%n].set_title('Exams delay: '+str(T_T[k]))\n", - " ax[int(k/n),k%n].set_xlabel('beta')\n", - " ax[int(k/n),k%n].set_ylabel('Normalized (%) Exams Rate')\n", - " fig.colorbar(cp, ax=ax[int(k/n),k%n])\n", - "plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n", - "fig.suptitle('Total infected proportion to the dynamic with no exams')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot4: Delay vs beta para distintas tasas de exámenes" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Total infected proportion to the dynamic with no exams')" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Contour Plot\n", - "n = 5\n", - "fig,ax=plt.subplots(4,5)\n", - "for i in range(len(examrate)): \n", - " cp = ax[int(i/n),i%n].contourf(T_T,beta,totinfected[i,:,:],levels) \n", - " ax[int(i/n),i%n].set_title('Exam rate: '+str(100*(examrate[i])/population)+'%')\n", - " ax[int(i/n),i%n].set_xlabel('Delay days')\n", - " ax[int(i/n),i%n].set_ylabel('beta') \n", - " fig.colorbar(cp, ax=ax[int(i/n),i%n])\n", - "plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n", - "fig.suptitle('Total infected proportion to the dynamic with no exams')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Paper" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.47711349, 0.63294227, 0.84410591, 1.14627178, 1.61384922],\n", - " [ 0.32223769, 0.439631 , 0.60972358, 0.87794451, 1.36195184],\n", - " [ 0.28481356, 0.40209671, 0.58715853, 0.92163202, 1.69887542],\n", - " [ 0.27992607, 0.41244367, 0.64648241, 1.16656516, 3.12934415],\n", - " [ 0.2934401 , 0.45673748, 0.79398159, 1.85917381, 10.43145576],\n", - " [ 0.32051309, 0.53669286, 1.10064459, 4.53909038, 25.22309423],\n", - " [ 0.36301331, 0.67326635, 1.86682231, 14.27904076, 37.39294846],\n", - " [ 0.42484026, 0.91829709, 4.64881205, 26.19544848, 46.69279338],\n", - " [ 0.51621884, 1.43764188, 13.05607676, 35.97417246, 53.98044273],\n", - " [ 0.65859689, 2.9297631 , 23.04251525, 43.9706888 , 59.80002151]])" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "totinfected[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/Paper_UCL.ipynb b/research/ExamDynamics/Paper_UCL.ipynb deleted file mode 100644 index 743c7dd..0000000 --- a/research/ExamDynamics/Paper_UCL.ipynb +++ /dev/null @@ -1,15915 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examination dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "Paper_UCL\n", - "\n", - "Logout\n", - "Python 3 Trusted \n", - "File\n", - "Edit\n", - "View\n", - "Insert\n", - "Cell\n", - "Kernel\n", - "Widgets\n", - "Help\n", - "Run\n", - "Markdown\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIR/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "import time\n", - "import easygui\n", - "#import matplotlib\n", - "#matplotlib.axes.Axes.text\n", - "#matplotlib.pyplot.text\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows': \n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "from class_SEIRQ import SEIR\n", - "from Quarantine import Quarantine\n", - "from Quarantine import Exams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "plt.rcParams.update({\n", - " \"text.usetex\": True,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\": [\"Helvetica\"]})\n", - "plt.rcParams.update({\n", - " \"text.usetex\": True,\n", - " \"font.family\": \"serif\",\n", - " \"font.serif\": [\"Palatino\"],\n", - "})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Beta, delay and exam rate\n", - "* **i**: exam rate; examrate = np.arange(1,21,1)*population/100# Persons per day \n", - "* **j**: beta; beta = np.arange(0.11,0.21,0.01) \n", - "* **k**: delay days; T_T = [1,2,3,4,5] \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mu = 0 # E0/I0 initial rate\n", - "sigma = 0.2\n", - "gamma = 1/14\n", - "\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 100000\n", - "# Initial Active Infected \n", - "I0 = 100\n", - "I_ac0 = 100\n", - "\n", - "# Quarantine\n", - "s1 = Quarantine(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 1\n", - "period = 1\n", - "testaccuracy=0.9\n", - "lambda_Tr = 0 # traceability\n", - "lambda_Q=1 # Proportion of effectively quarantined detected infected\n", - "T_Q = 14 # Time in quarantine\n", - "\n", - "# Exam Rate\n", - "examrate = np.arange(1,21,1)*population/100# Persons per day\n", - "\n", - "# Delay Time\n", - "T_T = np.arange(0.0,7.5,0.5)\n", - "# Beta\n", - "beta = np.arange(0.11,0.21,0.005)\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims_reference = []\n", - "for i in range(len(beta)):\n", - " sims_reference.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta[i],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = 1,T_Q = T_Q,lambda_Tr=lambda_Tr))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "levels = np.arange(0,105,5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reference Simulation\n", - "Here we simulate situations without exams that will serve as references" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - 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"[Parallel(n_jobs=12)]: Done 14 out of 20 | elapsed: 1.7s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 20 | elapsed: 1.7s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 16 out of 20 | elapsed: 1.7s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 17 out of 20 | elapsed: 1.7s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 18 out of 20 | elapsed: 1.7s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 20 out of 20 | elapsed: 1.7s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 20 out of 20 | elapsed: 1.7s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "'OK'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For parallel simulation\n", - "def simulate_reference(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "# Reference simulation\n", - "# Run simulation\n", - "start = time.time()\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation_reference = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate_reference)(sims_reference[i],tsim) for i in range(len(beta)))\n", - "end = time.time()\n", - "easygui.msgbox('simulation finished - duration: '+str(end-start)+' seconds', title=\"SEIRTQ\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta[j],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T[k],T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " aux.append(aux2)\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1318s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1518s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1369s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1237s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1375s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1291s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1499s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1419s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1473s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0681s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0838s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1148s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0827s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0687s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1376s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1204s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1043s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0689s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1228s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1322s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1368s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1183s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1386s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1367s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1112s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1283s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1504s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1181s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1228s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0702s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0745s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0803s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0702s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0531s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0986s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1312s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1300s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1353s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1396s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1112s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1020s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1358s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1252s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1166s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1120s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1086s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0891s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1253s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1189s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0500s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0720s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0660s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0718s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0877s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1064s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0782s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1096s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1135s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0954s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1390s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1355s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1024s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1287s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1393s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1392s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1213s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0968s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1318s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1272s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0726s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0748s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0558s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0450s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0504s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0591s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0861s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0817s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0726s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1222s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1420s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0856s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1275s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1039s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1562s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1643s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1330s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1482s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1465s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1611s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0621s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0679s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0978s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0533s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0601s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0554s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1087s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0992s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1074s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1447s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1276s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1153s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1207s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1344s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1109s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1389s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1432s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0883s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1673s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1260s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1231s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0718s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0709s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0472s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0777s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0644s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0981s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1074s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0926s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0770s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0767s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1324s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1080s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1786s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.3s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.3s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.3s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.3s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.3s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.4s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.4s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.4s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.4s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1145s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1277s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1499s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1407s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1522s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1632s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0557s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0589s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0887s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0943s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0750s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0574s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0608s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0728s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0657s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0700s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0955s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1181s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1343s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1338s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1362s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0726s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1313s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1014s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1586s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1206s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1093s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0695s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0980s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0753s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0699s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0779s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0637s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0622s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0490s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0725s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0740s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0627s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0710s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0894s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1405s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1164s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1116s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1020s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1230s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0967s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1140s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0966s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0963s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0733s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0873s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0738s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0779s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0719s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0651s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0807s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0754s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0973s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0538s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0847s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0768s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1287s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0843s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0773s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1147s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0685s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0974s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1141s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0764s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0819s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0788s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0595s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0829s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0980s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0819s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1033s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0762s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0562s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0645s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1027s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0874s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0567s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0588s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0968s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1076s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0987s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0665s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0811s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0785s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1010s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0667s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1159s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0867s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0835s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0749s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0826s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0666s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0679s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0563s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0668s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0918s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0573s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0467s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0652s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0654s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0862s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1261s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1295s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0884s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1132s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0628s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1033s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0787s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1078s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0702s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0689s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0865s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0838s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0692s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0871s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0561s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0637s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0868s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0750s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0954s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0707s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0869s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0915s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1006s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0794s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0860s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1252s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0779s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0839s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1082s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0997s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0806s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0900s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0710s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0649s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0681s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0764s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0620s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0506s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0532s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0891s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1341s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0855s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1171s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1014s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1242s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1317s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0800s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0754s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0883s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0801s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0796s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0850s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0722s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0754s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1297s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0808s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0794s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1269s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0629s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0724s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1363s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1372s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0810s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1503s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1260s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1077s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1268s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1208s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1122s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1240s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0945s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0904s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0882s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1244s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0981s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.3s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.3s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.3s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.3s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.3s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.4s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.4s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.4s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.4s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0694s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0804s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0636s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0737s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0809s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1197s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1378s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1177s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1167s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0979s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1130s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0983s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1260s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1150s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1119s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0944s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0893s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1019s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1238s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0708s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0701s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1113s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0898s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0635s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1163s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1050s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0786s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1012s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0985s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1085s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1567s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0957s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0984s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1055s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1068s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0913s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1308s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0811s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0841s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1156s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0724s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1139s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0887s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0718s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0942s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0762s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1047s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0869s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0721s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0815s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0684s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1048s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1000s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0910s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1322s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1232s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0951s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1097s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0957s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0989s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0760s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0830s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0982s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0910s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1117s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1137s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1402s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1081s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1045s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1208s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1465s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1216s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1079s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0932s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0866s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1419s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1289s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1059s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1181s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1079s.) 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"[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "'OK'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "# For parallel simulation\n", - "start = time.time()\n", - "def simulate(simulation,k,tsim):\n", - " simulation[k].integr_sci(0,tsim,0.1)\n", - " return simulation[k]\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i][j],k,tsim) for k in range(len(sims[i][j]))))\n", - " simulation.append(aux)\n", - "end = time.time()\n", - "easygui.msgbox('simulation finished - duration: '+str(end-start)+' seconds', title=\"SEIRTQ\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(100*simulation[i][j][k].peak/population)\n", - " aux.append(aux)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(simulation[i][j][k].peak_t)\n", - " aux.append(aux2)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(simulation[i][j][k].prevalence_total[-1]) \n", - " aux.append(aux2)\n", - " prevalence.append(aux)\n", - "prevalence = np.array(prevalence)\n", - " \n", - "# Calculate indicators\n", - "peakvsnoexam = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(simulation[i][j][k].I/np.max(simulation_reference[j].I))\n", - " aux.append(aux2)\n", - " peakvsnoexam.append(aux)\n", - "#peakvsnoexam = np.array(peakvsnoexam)\n", - "# Calculate indicators\n", - "totinfected = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(100*simulation[i][j][k].I_ac[-1]/simulation_reference[j].I_ac[-1])\n", - " aux.append(aux2)\n", - " totinfected.append(aux)\n", - "\n", - "totinfected = np.array(totinfected)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "test = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(100*simulation[i][j][k].peak)\n", - " aux.append(aux2)\n", - " test.append(aux)\n", - "test = np.array(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "peak_reference = []\n", - "for j in range(len(beta)):\n", - " peak_reference.append(simulation_reference[j].peak)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 1: Grid Epidemic plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "if saveplot:\n", - " %matplotlib inline\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.13" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "beta[4]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "int(len(T_T)/2)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,int(len(T_T)/2+1)))\n", - "levels = np.arange(0,105,5)\n", - "n = 4\n", - "j = 4\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference[j].t,simulation_reference[j].I/np.max(simulation_reference[j].I),color = 'black',linestyle='dashed')#label='I_d No exams'\n", - " for k in range(int(len(T_T)/2)+1):\n", - " axs[int(i/n),i%n].plot(simulation[i][j][k*2].t,simulation[i][j][k*2].I/np.max(simulation_reference[j].I),color=colors[k])\n", - " axs[int(i/n),i%n].tick_params(labelsize=15)\n", - " if int(i/n)==4: \n", - " axs[int(i/n),i%n].set_xlabel('Days',size=25)\n", - " if i%n==0:\n", - " axs[int(i/n),i%n].set_ylabel('Active Infected',size=25)\n", - " #axs[int(i/n),i%n].set_title('Exam Rate: '+str(round(examrate[i]/population*100))+'%')\n", - " #axs[int(i/n),i%n].plot([],[],label='Exam Rate: '+str(round(examrate[i]/population*100))+'%')\n", - " axs[int(i/n),i%n].text(1000, 1,'Tested: '+str(round(examrate[i]/population*100))+'%', {'fontsize': 25},horizontalalignment='right',verticalalignment='top')\n", - " #axs[int(i/n),i%n].legend(loc=0,fontsize=15) \n", - "\n", - "# Legend \n", - "for k in range(int(len(T_T)/2)+1):\n", - " axs[0,0].plot([],[],color=colors[k],label=str(int(T_T[k*2])))\n", - "fig.legend(loc='lower center',bbox_to_anchor=(0.5, 0.025),ncol=int(len(T_T)/2)+1,title = 'Results delay [days]',fontsize='15',title_fontsize='25') \n", - "\n", - "if saveplot:\n", - " plt.savefig('plot1.pdf',dpi=100,format='pdf')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 2: Tests vs Delay para distintos beta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total infected normalized by the dynamic with no exams" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "if saveplot:\n", - " %matplotlib inline\n", - "%matplotlib inline\n", - "plt.rcParams[\"figure.figsize\"] = 8,6 # 38.40, 20.56\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot\n", - "levels = np.arange(0,105,5)\n", - "# Colormap:\n", - "# https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html\n", - "# jet , hsv , Greys , YlOrRd\n", - "colors = plt.cm.BuPu(np.linspace(0,1,int(len(levels)+1)))\n", - "\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 2\n", - "\n", - "fig,ax=plt.subplots(5,2)\n", - "for j in range(int(len(beta)/2)): \n", - " cp = ax[int(j/n),j%n].contourf(T_T,examrate*100/population,totinfected[:,2*j,:],levels) \n", - " for l in levelcourves:\n", - " cp2 = ax[int(j/n),j%n].contour(T_T,examrate*100/population,totinfected[:,2*j,:],[0,l,100],colors='white',linestyles='dashed')\n", - " ax[int(j/n),j%n].clabel(cp2, inline=1, fontsize=20,fmt=str(l))\n", - " ax[int(j/n),j%n].set_title(r'$\\beta$ = '+str(round(beta[j*2],2)),fontsize=25)\n", - " ax[int(j/n),j%n].tick_params(labelsize=20)\n", - " if int(j/n)==4: \n", - " ax[int(j/n),j%n].set_xlabel('Results delay [days]',size=25) \n", - " if j%n==0:\n", - " ax[int(j/n),j%n].set_ylabel('Tested [%]',size=25)\n", - " \n", - " cbar = fig.colorbar(cp, ax=ax[int(j/n),j%n]) # increase fontsize\n", - " cbar.ax.tick_params(labelsize=20)\n", - " \n", - "#fig.suptitle('Total infected proportion to the dynamic with no exams')\n", - "#plt.tight_layout()\n", - "plt.subplots_adjust(hspace=0.4)\n", - "\n", - "if saveplot:\n", - " plt.savefig('plot2.pdf',dpi=100,format='pdf')\n", - "plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### COntour plots separados" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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gZNvXXTeKzJxCPv5umVv3BRN2ZNvxUVFcObA/P27bzMaswJryF04ESrad4BxNPi89tMeF1KTEwJbyOpcFCXpU3CFKZaXhx49/5aP/zfS4Dnek7U3GnNCTwX068OSbMykqcW2pxnDNtq8a2J8mcfH87zebNl3xUrat+IYxLYYS5YhiceZ8f4ei2IiKO0TJKoEX7//YZ8/zZrYd2yyOZWt38dmPK9wNyy38KV87sm2HCJf078vMbZv5bf9eG6LyHtpM7hvObD2CbQWb2FW03d+hKDai4g5BBo7rQ1lULKsXe75CUqAsbQrwxazf+WLW7y6X9/V+24GSbVcaw1nT3iExOtqeCjXb9ohAaSZvHtOE5KgUpu/+2t+hKDaj4g5Bbn3qakqKy7jjzCc9ut+bTeTuLm3asU8rfly43mvxVBHs2XZ0RASFcWVklxSTXVJsQ1TeQ7Nt35BekslDv9+OIXgXXFJqR0eVhxgDxx9Lhx6tmP76bJ88z5tN5HfcfjL3XXcSyQmxLt8Trtn2HSOGM/38y4l2uLc/eZ1otu0RgZJtR0oE7eNyKDNllBv9uww1VNwhxvl3nkXmgVzmfeXZ6OtAGZA29sReDD62I698OJ/cAtcySF8PSLMDO7LtJvFxXDVoANtysiitrLAhquAn3LPts9qM5cFjniDW4d1uLMU/qLhDiPY9WnP8uGP4+u35lJW6/wHurrS9lW3HxUVz201jWb91P9NnrnQrJl8SKNn2LUOHEBMRwXNLFtpToRezbV2X3PsIwumthpNXnkNxZXh/gQlVVNwhRJuuLcnYm82M9372+rO8OSDt6kuG07xJEk+8OZNKFzdECddsu2ViIpf178unG9awNSfLhqiUYOe4lB60iG3F/HTPp4IqgY0OTgshFv+ynauHTaKy0n0hBMqANICVO/dTMn0Rqzd5d0pTY6QdKNn2lQP6EyEO+7JtLxLKg9ICJdsGOLftUPLL81ieHdjb3naMy/B3CEGLZtwhQsuOzRARn0jbm9l2aUoEsxdv5JWPXN8z3NcD0uzArh3Anl6xgCu/msauPJuWtNVBaUFNWlQyfVMH8cvBuZQb1xYrUjxHLG4UkV9EJF9ECkRkiYjcIiK1fjCJyFkiMltEcpz3/CIiV7vz3OD7xFOOwhHh4InvHuDeZ6/wdyhH4U62PXpkD66aMISICO//swyFbNshQmllBYv27PR3KA0Sytl2IJFbns+bW18MuyVO/ci7wGSgE/AB8BoQD7wETKlZWERuB74EjnXe+yrQBpgiIi7P31VxhwAjzhpIy/ZNmT9judv3BtKAtD/eehJjhnTHuNFqEK7ZdsfUVGbedB2DWrWxISInmm17RCA1k7eOzWRF9mIOlqb7O5R6CYVmchE5D7gM2Ar0McbcaIz5E9Af+Aq4UkTOr1a+E/AkkAkMNsbcZoy5C+gLbAbuEZHhrjw7+D71lKM4909nsHd7Br/8sMqt+wKpifyay0bQNCWBJ90YkOYpoZBt3zFyOM3i4tmRk+3vUAKGcM+2+yR35/RW5xPtiPF3KOHCec7jU8aYQ99EjDGlwEPOl9W3ZbsOiAFeNMZsq1Y+C/i38+UtrjxYxR3kdOvfkeOGduOLKfPc6t/29nxtd7Ltzh2bMfGcwXwx+3fWbN7n8n3hmm13a9qEs3v34q1Vy0gvKrQhKnQKmIcEUrZ9UfsTOaH5SVTogiu+opXzWNva0lXnThSRqjWIxzmP39ZS/psaZeol+D75lCMYf+lICvOL+f5Dm3aDqgNvZtu333YSBYUlvPyh93cwCoVs+/ZRwyksK+OV5YE9aljxHU2jUzk2ZQCLDs6mwgT2Ijyh0EzupOqNdK7lWhfnMbLa7z2dxw01Cxtj9gIFQDsRiW/owTodLMh5/Ylv+eHzFRTmub4+dSBl2wAvTZ1Hs7REcvJcb+r0JNv2t3jtyrbP6taT55YsJKvYpqbhEMi2w30K2MktRwLwc8YsP0cSUjQTkSXVXk82xkyu9vpr4FLgbhGZaozJBBCRKOCRauXSnMcU57GuKSA5QIKzXL1NaSruICYiMoLKSsO2dXtcvscTaXt7+pc7zePgnyZyf0u/inUlGVz79acs3bfb36EoAYIDB6e2Gsa63N8DflCaryg1kWwra9rIWnZmGGMG11NgKnAlcCqwRkQ+B4qBk4DWwA6gA1DZyECOQpvKg5TmbZvw1tqnGTymt1ef46603cm2b71+DH++7iRE3I3KffwtXrvmbQPM2rGF3FKbMlnNtoOepKh4dhRuYW4QTAELoWZyjDEVwNnAX4F04Grnz0ZgBFDVJHPAeazKtFOonYYy8kOouIOU6/91MclNEtixab/L9wRSE3m3Li2YeM5gRMCdQeTBmG3bJe3/XnA2N/arLwFQfEUgNZMnRe7i1S3PsDrXs42FFM8xxpQZYx43xhxnjIk1xqQaY84FtgHdgQxjzFZn8ar9iXvUrEdEWmM1k+8yxjQ44lTFHYT0Gd6DsRcN55OXf+LArkyvPcdbTeQicNcdp5CbX8xLU3VAmiuM7NiBU7t0p8LY2Oqm2XbQ0zymCc1iWvo7DOVoLgGisRZlqeIn5/G0WsqfXqNMvbgsbhGZKCIviMg8EckVESMi79ZRdorzen0/Lq2ALyKdGqhnqqvvIRRIaZ7E3a/cSPqeLD76n+ubCHh7zrY72fZZp/bj2O5teOG92S5v2QnhOyBNgHvHnsCu3BzeXbWi8UEpjSKQsu2J7U7lr73+HRRzt0OpmbwKEUmu5Vx/4AkgC3is2qU3gRLgdudiLFXl04D7nS9fduW57gxOexDoB+QDu4Be9ZSdjtVUUBtXYg2P/6aO63WxwllvTdxbdSTIOeHiE2nWJo0Hr3iJkqJSl+4JpCbyiAgHV142gqWrd/Dt/LUu3xeMTeR2cUavnhzXvBV3/vi1fftta7Yd9CRExDGmxfEsyfyZ0srQnSsf4PwgIkVYHsoDegNnAkXA2caYQyOHjTFbReQ+4HlgiYh8CJQCE4F2WAu5uLRbkDvivgtL2JuA0UCd8w6MMdOpRbIikgr82RnsFDeeDbDcGDPJzXtCCkeTNL55fyFL56zjwG7vbeHozVHkFRWV3Dzpg4Bfj9wO7Mq27zxxBGszDvD5Rte/6Cihz/iWI4h2xDA3/Xt/h9IgoZhtO5mG1Sx+BRAH7MZau/z/jDG7ahY2xrwgItuAe4GrsFq91wAPGmPecvWhLovbGHNI1OL5MOArsd7c1OpLxCn1ExMfzd/e+xNTn/+eDSt2uCXtQGoiT02J5wAl7D/oXlNjMGbbdg1IM8Afvv+CmIgIbBuXrtm2xwRKM7kD4ew2J7Apbx27i3b4O5ywxRjzBFazuDv3fIm10YjH+PoT8UbncXK9pWqnjYjcLCL3O4997QwsUImOjeKRT+9jyPg+tGib1vANPsQdaUdFRvD8k5fxlxtOdusZnko7FAakAZQnVrL2YDrLD7g3110JbToktCUlKo056d/5OxTFD/hsARbnrifHARuqZ+9ucLLzp3qds4GrjTEh+ZUzMiqChz66m34ju/PUXe8xf4Z7A5MCaRORSycOoWObJjz7duiv7GRXtv3HEcNo3yyF+2Z9S4VdG69otu0xgZJtA5RXruLhVX+ksNymteq9SAg3k/sNX2bcNzmPr7p5XyHwT2AQ1tJxaRzuYx8DzBSRhLpuFpGbnBubLyk1wTWA44p/XMKQ8X148W8f8dOnSxq+oRqB1ETetnUqV1w8nB8XrmfRym0u3xfO2XbT+HhuGDKY6IhI+6SthAQRYv1/kV+eRyWBvS654h18Im4RSQEuwoNBacaYA8aYh40xvxljsp0/c4FTgF+AbsAN9dw/2Rgz2BgzOFr8P2WiTdeWXP3wBQ2WczRJY9pLM/n3rW/yzfsuDTT0Ge6uRX7HHadQVl7Bc++4nm37Q9p2YFe2ffvwYcRERPLUrzbOc9dsOyS4ptMF/LH7/Qg+WHJQCUh8lXFfAcQDn9o1KM0YUw685nw5yo46vU2TVqk89cMDjJk4jA692jRYvjC/hHlfLXf7OYHURN6saSJd2zXllY/mk5Fd4LXn2EGgDEjrmJrKJf2OY+ralWzN8d7sAcV1AqWZPDYihvEth5JdmoWxb7ii19Bmcu/gqz7uqkFpr9hcb9WK+nU2lQcKianxPDPzIYryinn1/qnsqGdjEEcTzwehBVITOcCe8iIuuW8KxcVlLt8Tzk3kAH8YNZSyykqeW2JjS4tm2yHB2ObDiIuIY64OSgtrvC5uERmKtXDLBmPMbJurH+Y81raRecAQFRPFMzMfJiLSwesPfcjCr3+rs2xjpO1t3JX24P4d+XXHHgpdXCgG/DP1yw7s3ETksUVzmbF5PemFgd1CES4ESrYNcFab4Wwv2Mz2woD+yFO8jC8y7qpBafVOAXP2g7cGcpybiledH4i1+EpljfLjsRaFAah16dVA4akfHiAuKZYpkz7m5y+XAtZc+JTmSYw4axBpLVPYs3k/+zKKWL98u8fPCaQm8p7dWvHEPy/itU9+5s3PFnntOVWESrZdnlhJemEBM7fb+MGs2XZI0C2xA23i2jN1x+v+DsUltJnce7gsbhE5FzjX+bKV8zhcRKY4f88wxtxb455k4GKs9VkbWhXmPKy1XN8Crql2/mmgu4j8jLVyG0BfYJzz94eMMT+7+j58zb+/uI/uAzox9Ykv+fH9BYDV1z38zAFcfO/ZNG/X5FDZ4sJSXnpoGj9OW+z1uLzZRO5wCHffeSoHcwr46Nu6WxdqEu4D0kZ27MDtJw7jjz98xb6CfFvqVEKHPUUHmLrjDZZmBtZg1UCkxESxpbSFv8PwGu5k3P2x9hqtThfnD8B2rGXcqnM5Vv9zY1ZKewdL6sdj7aASBewHPgJeNMbM87Ben7B74z4GjO3DSZedwKKvl7F+6RZOv2Y0l/31HNYt3sJ3b82hqELo3LsNJ104hLueugzAbXl7cz1yd5vIzztzAL06t+TB576kwMVmcn81kftb+lU4RLhv7ImkxMSSWWRjdqnZdqMIpGbyJtH7WJARHAvxaLbtXdxZ8nQSMMmdyo0xLwEvuVh2CrVMFTPGvA4ER9tQLfz3nnfI2JvFNX+fyN8/upNvp8zhnFtOYvbHi3jypleRtNRDZbet38sND57D2deMYvn8DWTsa3A/dSCwmsi7dGrOzdeNZsGyLcz8ZYPXnlOFv5vI7cq2L+/fj2Obt+SOH74Kio1EFN8yvOkAOsQ7+Dnjp6AYTa54l+AcCRRkfPjkV0z+6wckpSVw6Z/PZt3izTxx42QkLRURObT2+4x3f2bTql207tiMuMRYr8Ti7VHkxfGwcsMeHn3lW5fvCfcm8jbJSdw7+gRmbd/CF5vW2VKnt9Fs23cIwtWdzmBE0zEqbQXw4ZKn4c5n//2OstJyzrnlZD56+utD5021VbFKikrJzSyg27HtiEt0bbEYd7Jtb0sbYNvuTO749zSXy4d7EznALScMAeDBuT/aV2kINJErFoPS+tAytg1Ttr7o71BcQpvJvY+K24d89epMNi3fxu7N+2ud9hWXEEObTs3YtGoX+3dmNlhfIPVrnzL2GAYM6cx/Xv+RkjLvN9GGShM5wCMLZjFt/Wp25+faU2GINJFrtm1xUfsxZJZksCzrV3+HogQI2lTuY9Yt3kxe5uERw1XN5BGRDk69ZBjN26SxYsFGcg7WP6o4kPq1O7Rrwl23n0KrZsmUlbvePxusC63YJe3kmBiiUiMoqSjnt/11L8gTSIRDth1I0u6a2IHuSb2Znf5tUKxLrtm2b1Bx+4GqbFtEMMYgIpx26XAm3jqerev28PYTXzdQg3t4s4k8OjqSh++fQElpOZP+O4NKFzfECNaFVuxk0unj+HLiFUQ6bPyzCJEmcp23bREpEazPXcXCjNn+DkUJILSp3I8YY4iJjeKmv5/HkPF9yM7I42+X/Jfysvq/WQdSv/btN4yle8cW3PX4J6RneX/ucahk22O7dGZC99489et8yisrG74hjNAm8sMUlC/jxU3L/B2GS2i27Ts07fEx1fu2E5LjuOYvZ3HSxCFs/H0nD1z2Pwrziuu9P5D6tZukJTBuTG/e/XIxi1Zsc/m+cG8iT4yO5pFTx7PuYDovL7Ox3zJEsm3Fol9KLxIiAmgUpRIwaMbtIxwRDjoP683WtYf7Mgtyi/j01dn8Nm89qxdvsV3a3uzXBthXWcyVf33brV2/gnXql53cN+oEWsYncut3X1BmV7atA9IaTSBl2/ERcfy19w2syF7Mu9vt3ptJCXY04/YRZ904nhe/vY+ufdoecT59TxaLf1rjd2m7k21HRUYw7sxjAdh/MI+KCtfkE8xTv+zKtiMdDrq2aMqbv//GigPBsQqWZtu+55RWI4mNiGP2geDYBUybyX2LZtw+IKV5Elc/fAHL529g8+rdbt8fSNIGuOna0Vx07mD2HMhh5Qbvj4b2dxO5nRTHl3PpFx8S5Yiwr9IQaSLXbNsiQhxMaDOKDXmr2VW0zd/hKAGIZtw+4LpHLiI2PoaXHv7E7Xu92aftCSOGduOicwfz0be/uSXtYG4ityvbPrNXT5rHJ2BAlzWtgY4iP8zIZoNIi27KzP32zi7xFppt+x4Vt5fpObgLp109ms9em82uzQe8/jxvZtstmifxt7tPZ93W/bz4/lyX79MmcujVvBlPnnEadwwabkt9viBcmsgDKdsGOC6lGXuKdrI2d6W/Q1ECFG0q9zIdB3Zjz7Z0PnjO/b6qQGsiv/8vZxPhcPDQ81+5tdCKp4RKE3mECP8+4xSyS4p58tf59lWsTeQhyee7P2DGnk90XXKlTlTcXubHaYuZNX0pFeXujR4ONGkDvPHpQhLiotm1P9vle7SJHK4dPIi+LVrxh+++IKek/kGILhMiTeT+JtCy7d5JleSVQ5lxbTtcfxOozeQllZFsL2rm7zC8hjaVe4mktARGXjoKwOvS9jbxcdGUpkSwZPUO5izZ5PJ9/pJ2IDWRd0xN5c4ThvPdlo3M2OL9bU7tQLNt/9AurhX/PO5FBqQO9XcoSoCj4vYSVz88kQdevpbWHZu6dZ8n0vZmtt20SSLvvHoD547r625YfiEQMvXqZEYW8dmGtTw0Lzh2/gonAi3bntB2HBWmnA15q/0diksEarYdDmhTuRcYelp/zr5pPNNfm83e7Qddvi/QpB0dFcE/Hj6XhLgYlq/f5dZztIncIqu4iL/N+d62+rwt7XDJtgNN2s1jmjC2+VB+PjiLggrvLx2sBDeacdtMq07Nue/1W9j0+07efPwrl+8LNGkD3PvHUzm2exv+8dI3bNvd8DajVWgTOfRp2YIPr7yYdknJttTnC8JF2oHIxe3PwFDJ9/s+93coLqHZtn/RjNtGHBEOHpp6JwCP3vImpcVlXnuWt6V90bmDOXX8sUz+eAGzF290+T6VNiRERfHshDOJiYwgv9TGQUbaRG4LgZZtJ0bGc0KzAcxN/4Gcsix/h6MEASpuO0lJ4cP//khJUSn7dni3idzbFEUafli4jjc/W+TyPcE8X9tOHj5pHB2SU7jsi4/IDpJR5Jpt+4/UqD38c829lFXqSHLFNVTcNtC0dRrdRhzD4llrmP/1crfuDcQm8tKUCD75YTmf/LDcrfs8JRDEa1e2PaF3Ly44tg/PLfmZX/a6Ny7AX4TLQisQeNl2rCMGQDNtxS20j7uRtO/Rmmdm/517nrmc2Phot+4NVGl7gjaRW1w2qB+L9+7i+SULbaszlJrIdUDakTx0zNVc1/lP/g7DZTTbDgw0424EPQd34Z+f3UtFeSUPXP4/igtdb+ryl7RjYiIpKbFXBCrtqroqufzLj0mKjqbC2FSvNpGHLMckd+OYlH58tus9f4eiBBmacXvI4FP68p9v/kZBbhH3nv+sR7t+uYMd0r7iomH84fqxREfX/n3Nk2xb+7UtxnXtQkJUFCUV5WQUFfo7HKUGgZhtX9f5dLJLM5mX/oO/Q3EJzbaPRESuERHTwE9FtfKdGig71dVna8btIf1P7s+uzQd46KpXyM5w70PBH8uZ3vWHk5lwen/en/YLMdGRlJaW17jHt9IOBPHalW33bdWK/507gTdX/sajC2fbUieg2bZNBKK0B6b1oWtiL6bueIMy473ZJ4pXWQ48Use1E4FxwDe1XFsBTK/l/CpXH6zidpOUZknkVUby5mNf8d6z31FS5N5IUH9I++9/mcCYE3ry/rRfmP71MvLyi2vcY+Pe0C4QSk3kcVGRPHn2aewvyOeFpcHTrx1OA9ICkQvajiS9ZD+LDs72dyguodn20RhjlmPJ+yhEpOrDYHItl5cbYyY15tkqbhcREW549GLGXTKS2894kqwDuV6Xth089a+L6HtsO954dz5ff7+SzKyCI67rYLTG8dfRo+iUksZlX3xEbqnKsDY02z6a17c+R9PoFlQY7++yp/gWETkOGAbsBryyqbqK2wUiIiO4++UbOOnSkXzx5lyy033zYdCYbDs6KoIXn7ycTh2a8eLkn5g5dy35+SWIQHR0JCOHdiM5KY6o1GjmLd3M/oN5lJS6luVpv7bFqM6duHxAfyYvX8yiPTvtqziEsm0dkHYkDoR2cTmUVlayt9jGfzNeRLNtt7nJeXzdmFq/mbURkZuBpsBBYKExxq3N110Wt4hMBEYD/YF+QBLwnjHmilrKdgK21lPdh8aYS9wJVERGAA9ifZOJAzYCbwAv1PGHYwuxCTE8+OFdHD/2GN76z1dMfcGzgSS+biIfMqgz3bu0ZN+BHHbvzSY/v4TYmCiGD+nKhNP70f+4DofKnjOuL3MWb+K1T35uUN7ar32YDcUZvL9mBU/Zuce2lwmnJvJAzLbHthjGRe3H89zGf5Fblu3vcBSbEZE44AqgAnitjmInO3+q3zcbuNoYs8OV57iTcT+IJex8YBfQy4V7Gt0JDyAi5wCfAMXAh0AmcDbwDDASuNCd+tzhqn9eysBRvXj2vg/4bqrrq4hVxx/92vMXbeKl12dx6/Vjufi84ykqLqVZk0TuvPUk8vNL+GDGEnbvz2bgMR3o16stl505mMrKSl7+aD51zWTyp7QDrYm8PLGSXXm53D/H5hHBOmfbFgJR2pESyWUdTyOvLCdopB3m2XYzEVlS7fVkY0xtfdbVuQhIBb42xtRsUikE/onlxC3Oc32BScBYYKaI9DfGFNAA7oj7Lixhb8LKvGe5cE+jO+FFJBl4FesbzBhjzBLn+YeAn4CJInKJMcblofSu4miSxrtPf8OS2WtZPt+zvZR9LW0R53MNfDR9CSkp8Vw2cSixMZF0aN+UnbszueeZ6eQVWJnXj4vWM+TYjtx9zThGH9+db+avqXVDEZX2Yc7rcwyn9+nBHT98RX5Z8KxFHk7ZdiByaqsTaBLdjPe3N/TZrzSWssoIdhWlNraaDGPMYDfvqWomf6XmBWPMAeDhGqfnisgpwHxgKHAD8FxDD3H509gYM8sYs9EYu1aWcJmJQHNgapW0nfEUY7UCANxq5wM79WnH3z+5l9j4aIoLS4NC2g6HOI+OIzLm19+Zx+czlnHsMe3Yuj2DPz7+CXkFJTichs8rKOHXVdtZumYn7Vul0aVdM7di8DaB0LxenXYpyTx80lgSoqIoLNdpPHWh2faRxDpiuKj9KWzIW8163W87JBGRPsAIrAR3hqv3GWPKOdysPsqVe7w9OK3RnfBYc+EAvq3l2lys5ocRIhJjjGl0SnHsiB48Mu1uiotKadoyhd1b0xtbpVeoLu2+fdpxwrDudO3cnOioCGbNX88vS7eye08WlZWG/70+m+LiMnbszqSkzMrqKp12d4iQV1DC9j1Wlp0QF3PUs7Rf28Ihwn/OPg2Au2d+c+jP0BZCKNvWAWlHM7rFEJKjUnh189P+DkXxHg0NSquPKtEkuFLY2+JudCc80NN5PCrtNcaUi8hWoA/QBVhbs4yI3ITzDzRW6v8z6dCrDf/87F4O7svhwSte4sBuzxf+93a2XcW5Zw7g5mtHExsTRUFhCQnxMfTq2Zo+vdrwypQ5HEjPo7S0nDfeW0Bp8tECrpJPh1ZplFdUsGbz3iOuaxP5Ye45cSRDWrfj7pkz2J2fa1u9oSRtfxOI2TbAmpyveH7DZrYVbvJ3KC6h2bZ7iEgscCVWl+7rHlQxzHncUm8pJ94St22d8ECK85hTx/Wq86m1XXQOJpgMkBLRtM5P8YSUeCZNu4fiwlLuv/S/ZOyr63EN46sm8vPPHshtN4xl3sKNfPPjKn5bvp1xo3px1ql9GTG0G7PmreNAeh4ikB9noOzwl0ARDjWpjx/Wk2H9OrF41Q7SM/MPlVFpHyYpJoaz+/Ti3dXL+XTDGtvqDTVpaxP5kQhCr6RK8soNG/Nt/HejBBoXAmnAV7UMSgNARAZijfuqrHF+PNYYMoB3XXmYV8RtZye8r2jSrR0V5RU8evMbQSHtwQM6cdF5x7Pgl028+d58tu+0mrp/mruOhIQYjj2mHSOHdmP+ok2UJB+9yIoxlrxPHt6Lq88dSll5Ja98NJ/cAmtVNZX2kWRFFTFh2rv27a8dgmgT+dGc2XoMl3Y4nSfWPUhG6QF/h+MSmm17RFUzeX0jD58GuovIz1j94GAltFXdwQ8ZY3525WE+XUnDk054DmfUKXVcrzqf7WFYAOzems6tJz3GmiX1TT+vH19JOzoqgvGjetG8aSIffrb4kLQdDqGsvIJ5CzeSX1BMakp8rSujRTiE5mmJ/Pn6k7n1khOJi4nmjn9/zMbtVjdLsEvbTtokJ3HH2OFEiJBRVEh5ZWXDN7lKiGXb/iQQs+12cS25stMENhesDxppK+4jIr2BE2h4UNo7wDLgeOBG4A9Ad+AjYJQx5l+uPtMfK6e51QkPrAcGAz2ApdUviEgk0Bkox8W+gZqMOn8IA08byEsPT6Os1PN1XHw7gtxBRISDjz9fyuq1ew6dr6y0skxjDOXllaQ2s/6IHSJHDKRqmprIpNtOp1uH5vy2ZidPvDmTg9lWr0UoSNuubDs+KopXLjiHtknJfLRuFbvyPG+JOYoQk7Zm20cSIQ7u7Xk5pZUlfLC9rnU4Ag/Ntt3HGLMWEBfKvY5n/d9H4Q9xu9UJjzVX+3LgNOCDGtdGAfHAXE9GlHc+tj33vHIjW9bsabhwPfh6rnZxSRlT3q+7RaW4pIyisjJynZuJGI4U2YHMPF7/ZCHxcdEsWrGNsnLrC4tK+zACPHXO6XRv0oxrv/5EpV0P/pZ2IGbbF7Q7lY4JXXl9y3Pkldv4b0dR8FJTuYgMFJGj6q6vE15EUkSkl4i0rnHbNCADuEREBlcrHwtUNS285G6MSU0S+ftHd1GQV8y/bn7D42zbH6uiAezZl82efdm139QkmtjoKHKdi6xUJdvJCbEM6NUOgKVrdjJv6eZD0m4MoSZtgHtHncDJnbvxzwWzmLdru231htLKaKDSrouO8QkszlzA8uxf/R2Ky2i2HTy4s1b5ucC5zpetnMfhIjLF+XuGMeZe5++edMKfB7wJvAVcU3XSGJMrIjdiCXy2c7PxTGAC1lSxaVjLoLqMI8LB/e/9iWatUvnzhc+TdcCzqT3+knb990QQGRFBVFQE5dWk3Cw1gfNO6sc54/ry5Jszmb144xH3+Wu3L7uwU9qtk5K4auAA3l29nLdWLbOtXl9IO5z6tQOVdnHZfLDjNRz4drvcxqDSDi7caSrvD1xd41wX5w/AdqBK3O9gifh44HQgCtiP1Qn/ojFmnjtBGmOmi8ho4AHgAiAWa+nVu4Hn3V3NrWPvtvQa2JEXH/iIdcs8y6YCVdoA0dERxERFkldofYi3a5nK+Sf34+LTBjFj7mrbpG0HgTiCfKfJ4ZxP32NL9tFLvwYy2kTuf85oPZr04t/YW7yTSnTLTsU7uCxu55rjk1ws63YnvDFmCjClnusLgDPcqbMutu/J54ZR/yLLw+05A1naAPGx0Tgcwr6MXJqmJnDlhCGcNfpY3v96Cf/9YO4R94VCv7ZdtElOYlC3tny2YQ0bMm3OQLRf21YCUdp9krtxfeeJzE9P5eNdb/k7HJfRbDv48F+q5Qdi4mKYcNfZAB5J26QlBZy0IyIcR035io2OJMLhoGv7Ztx26SjOGn0sL384PySlbfcI8kdOGEeTWM9WsasTlXbIExsRw109LudgyQE+32P7fkeKcgRhJe7WXVpw0R9OIj4p1u173RU2eF/aLZsnM+WV6xg5oMsR5+NjowEYPbgbp47szX/e+IF3vjxykIxK+zDVR5Df9v2XZBbbKKYQG4wWCARitn1FhwmkRTfjne2vUFoZPOMMNNsOTvwxHcxvRERG8I8bXqMwz73VrwJR2k2bJPL0YxeTnBTHgcwjP8jKK6xFQhLiYvjzU9NZsOzImXcq7SOpGkH+8LyZQTeCPNyy7UCUdvfEjpzeehRz039ga4FnOwkqijuElbi3rdtDy/RdDResRiBKOyU5jqf+72LSUhKOWPGsijWb9/Lxd7/x/c/rWLN53xHXVNpH0rtFc24eOoR3Vy/nbTtHkPuAcJN2oFJWuYav9nzE3PQf/B2KW4Rytl1WGcG+Avc/u4OFsBJ3Rbl7y1UGorTjYqN48v8upnXzZO5+/NOjxAxQWFzG8+/OOWrbyWDfnhPsH0H+e+F+rvzyYxbuqXVfAM/Rfm3bCcRs24FQbsr4Yf+X/g5FCSPCqo/bHXwhbU/Ija1kyaod/O2ZL1i2ru7Wg0CTdqCIv4o2yUn07WotRzBv13Zdg1xxm/ZxrXlp0AO0i+vk71DcJpSz7XAgrDJuV/GVtN3JtiMjHSS0TyI9M58X3p/j9rM8JZCkbfcI8mbxCZz43qsUl9so2hAcjKbZ9tE4EO7qeQnxkYlklx30dzhKmKEZdw0CUdrRURH8/aFzee2Ry4iPi3b7Wf5cFS3QpB0dEcEL559F9ybNuHvmjKCTtjaRBwZntRlH54RufLLzbfLLAy+++tBsO/hRcVcjEKUdHxfNY49eyAkDu/LW579QWFTq1rNU2oeJEOHpc09ndIfO/G3O9zqCvAFU2rXTMb4NV3Q8m5XZS1iS5dL2yQGDSjs00KZyJ4E4EC0lOY7H/nUhPTo255H/zeD7n9e5fG+o9GnbORjt/MF9OK1LDybNn8nH61bZVq8v0H7twOG8toMpqijggx3Bs12nElqouAlMaQNcf+NourZvyl+e/pyfl291+T6Vdu1MXbuSPfm5zNm5zdZ6Q3EwmmbbdfPJrneYdeAbbSJX/EbYi9vbS5iC52uPP//eHL6Y9XutU77qQqV9NNcPHsSXu9axJz8v6KTtD1TatdMtsQOxjl1klxkOlqY3fIOieImw7uMORGl37dycSX8/l9iYSAqLSlXajeSmIcfzt7GjuaR3X9vqPIT2a3uFQJR2QkQc9/e+kes63+HvUDxCs+3QImzFHYjSPrZ3W557/FKO6dqaJikJbt2r0j6aCBFO6NaRzzeu5ZnFC2yrF1Bpe4lAlDbAXT0uIDkqOah2/apCpR16hF1TeSCOHAcYOqgz/3jgXPYfzONP/zeN/Qdd/wBTaddOSUIF1379CRXGYGvNISjtQCBQpT2q+fEMajKcL3d/yM5C18eaKIq3CC9xR7ovOF9I+4Rh3Xjkb+eweWcGdz3+CVm5rmc+4Srtoe3b0TE1lZS4WFbs2ceGjAyyiw9vHlOeaK2EVlJRYU+AVYSotAMh2w5Emsc04ZauF7E5f31QLmuq2XZoEl7idhNfDUTbkJXF3KWb+ffk7yhwY552uEr736eezITevYmMcOAQoayigkU7dvLyL7+yeNfuQ9K2HZW21wjUbDslci+/ZS3ih31fYHO7jddRaYcuKu468IW0hwzqzPxNO9i1L5sHnnPv23w4SjsuKpInTj+NEzp3YurKlczZspU2ycmM7tKZk7p1ZXC7tlw341N+2eveDnAuEYKjx0Gl3RDFlUVM3fG6v8NQlCMI28Fp9eELad987Wie+MeFnDS8p9vPCkdpA/Ru0YKxXbvw+Zq1PL9gIfO2befDlb/zh+lf8O6y5cRFRfGfsacxsGVrewL0MToYLXDolNCOZ/vfTbOYlv4OxSM02w5tNOOugbcXVnE4hLtvO4WzT+vHJz8sZ+ai9W7dH67SBhjarh1RERFMXbGS3JISIkQwWDuhPfzLTGLjIpnY61juHXoid8+cwb6CfHuCDdEmcn8TqNKOdkRxX88rSIhIpLC8wN/huI1KGyoqhewC7+/W6C80466Gt6UdGengwQcmcPZp/ZgyfRFPvjkT44a//CntskT/jx5PiYsFoEuTJgBUGEOlMVQmWfU98et8ftmzk2Ft2nN5n372BBui0g6EbDtQuaLjBNrEtefd7a9QWGHTlz9FsREVN5awfbGEaed+bRg1uBsvvDeHVz5yb16xv6VtF65Ku0XC4Xns4jyu3GstRjOgTWvio6Kc9VVSaQwCpBcW8PTiBVRUVnLbwGH0adaiccGqtL1GoGbb/VJ6cXabccw+8B3r8n73dzhuo9l2eBD24vZFf3aEQyhNiWD1pr1cdPcbvP/1ErfuDzdpn3tMb+bfehNn9+4FcGgs747sHHZkZ3Nyt250TEs9YvR4VZnFe3fz8vJfATipU1fPg1Vpe41AlTbAhe1PYG/Rbr7Y/YG/Q3EblXb4ENbi9oW001Ljeen5qxg/tAcA+zJy3bo/3KT98Pix/OeM0/hp02Z25eQccW31/v0s2Lad1slJ3HbiUGIijhyiUZWZ/7B1M+WVlbRLSvEsWJW21whkabeLy+a1Lc/y0ub/UGbK/B2OotRJ2IrbF9Lu1KEpLz51OR1aNyG3wP0P6nCSdlJMDFMvvZjL+vfj9cVLeWzOXJbt2XvoumBl1a/8upgtmZmc0rk7fxg4lCjH4T8jh1jqTi8sQIBmcfE4RA6dd4kQnfYVCASytPun9iYuIp5yU05WafBlrppthxdhKW5fSPvE4d3539NXEhcbzR///TGLV213+d6SJEdYSbtf61b8cP01tE5K4t+z5vDq4sVsy8o+osyh5vLKHP61aDaFZWX8cdAwrjluIC3irf7wCudIvxPbd8Qhwk/bt1iD11wdAegjaYdrth2o9EjsxP29b2ZCm0v8HYpHqLTDj7CaDmYcDp8MQuvetSX/evA8Vm/ay9+e+YL0LNdHpjZG2BB80u7VvBkfX34pAPd8/Q1frl0HQKTDQbP4ePq3aU2H1FQKSktZmLmTDZkZzN6xlccWzeFPg0dw9/Ej6deiFW+s/I29+XmMbNeBG/oNZkduDr+6sxCLSturBGq2nRaVzP29byC7LJMv93zo73AUxSVcFreITARGA/2BfkAS8J4x5opaynYHzgdOBboDLYEsYBHwrDFmlhvP7QTUt7L/h8YYr3xV9nQf7dUZGUz67wxm/bqB0jLX18oOlTna4Hqf9rr0DL5au56zevfkxE4d+XLtOhKjoznnmN5MPK4PfVoeXgDjYFEhn29cy79+ns27q1eQW1rCxJ7HckbXnpzRtSdlFRUYDHvy87jp2+lszDroWrAh2qcNKu36iJRIHjzmWmIj4vjv+scprNA520pw4E7G/SCWsPOBXUCvesr+E7gYWAPMADKBnsAEYIKI/MkY87ybsa4AptdyfpWb9biEu9Lu2L4JD/zlbP71ynds2pHOdwvWunV/qEjbkznaD37/A+1Skjm3zzHszMnhQH4BD4wbw9oD6Tw2ew55UkqPJk25pHdfrus7iD35ubyx8je+3LiObzZv4PyefejVpBnllYb9hfm8uXLpoWbzBlFpe5VAlTbAZR3OoktiD17f8hx7i3f6Oxy3UWmHL+6I+y4sYW/Cyrzry5q/BR43xiyrflJERgM/AE+IyMfGmL213l07y40xk9wo7xGeZNknDu/O3+45k5LSMuJj3bvf303jdtUBni+sUlhWxkM//Mhr55/HH0cMB+CDFSv5+w8zj5jytXjvbl485WzuHDyCeTu3szHrIGWVlXy41sP5tiptrxLI0gZYkjWd4ortLM/+1d+huI1KO7AQkfHA7cBwIA04CPwOPGeMmVGj7AisRHgYEAdsBN4AXjDGuNRE67I1jDGzjDEbjWk4lTHGTKkpbef5OcBsIBoY4eqzfYUny5def+UJ/OvB89i2+yDXPPAuKzfscfl+f0s7EFZDq2J9egb/mTuP4rJyXl706yFpV40IF2DGlg18tWkdcZFRdElNa1zAKm2vEsjSbhXbjPZxOeSWZfPzQZd77RSlVkTkP8CPwGDgC+Ap4GugOTCmRtlzgLnAKOAz4EUsHz4DTHX1mf4YnFY1QdLdT842InIz0BTr28xCY8xKu4LyJNM+7Zz+XHXJCL6Y9TtPTZnps/5sCL5BaK7w5dp1RDkcbDx48FCmXXNEeIUxRDgcxEdFe/4glbZXCWRpN41O5fG+d7Mi+1c+2jnF3+F4hGbbgYOI3AjcB7wF3GSMKa1xPara78nAq0AFMMYYs8R5/iHgJ2CiiFxijGlQ4D4Vt4h0BMYDhVjfOtzhZOdP9fpmA1cbY3Y0Ji5PMu3iJAdfzllFVl4hcxZvcuv+UOnPBvukXcWnq9fUup921VOaxydwsKiQlQf2efYAlbZXCWRpR0kkDx1zHVGOaOakf+/vcDxCpR04iEgM8Ciwg1qkDWDMESv5TMTKwt+ukrazTLGIPAjMBG7FhczbZ/O4nW/yPSAGmGSMyXLx1kKswW6DsPoO0jjcxz4GmCkiCXXdLCI3icgSEVlSVmOnn9KUKLelPeaEnrzx0nWkJsVRUVHplrT9PT/brjqqsFva5YmVtUobIMrh4OpjBzCibQdmbd/CnnwPBKHSDmtu6XopHRO68s62l9hf7HqXlqLUwclYIv4UqBSRM0XkLyLyJxEZXkv5cc7jt7Vcm4vluhFOV9aLT8QtIhHAO8BI4EPgSVfvNcYcMMY8bIz5zRiT7fyZC5wC/AJ0A26o5/7JxpjBxpjBUZGH/e5Jln3ztaN55G/nkFdQjMPhxmpc+L9p3K46qrBT2mlxsTw+4VRSYmJrvd4qIZHr+w7m9kHD2Jh1kJeX/0pRuRtLUiaWq7R9QCBn22e0Hs24lsOYsecTfs9Z6u9wPEKz7YDjeOexGFgGfAU8BjwL/Cwic0SkebXyPZ3HDTUrMsaUY017jgS6NPRgrzeVO6X9LnAh8BFwhSsD3BrCGFMuIq8BQ7E6+p9z9V53pZ2cFMuD909gaN9OfPLDcp59exblFbVnhrURStK2O8tul5LM6xedT9vEJKatW8XCPYen5cRFRnFBzz6c0qkrx7dux/IDe7np2+nklR7VIlU3IbywCqi0XSWn9HfmpTfh232f+TsUj1Bp+4VmIlJ9R6jJxpjJ1V5XbT94H9bU5xOB5UBnrOT0FOBjDg9Qq9o84chNGA5TdT61ocC8Km5nx/x7WNJ+H7jK1eHuLpLuPNbZVF4dEyEeDUK7+dZxDOjdjn9P/o4vZ7s3bVylXTfHtGjO6xeeT5TDweVffszSfUc2X45s14E/DBxCSXkFLyxdyP+WuTltR6XtEwJZ2pESSavYDLYXZrO9cIu/w/EIlbb7VFY4KMpvsMW5ITKMMYPruV714V4OTDDGbHO+/l1EzgPWA6NFZLgxZmFjg6mO18QtItFYGfY5wNvAtcYY19NU1xjmPHrl/8ioyAgKEuDF9+fy6Q/LWbtlv8v3hpKwwX5pD2rbhtcmnkdOSQmXfP4hm7Mzjyrz0/Yt5JaUcLCosNbr9aLS9gmBLO0IcfB439vYkLear/dO83c4SuiR7TwuqyZtAIwxhSLyHXA9MARYyOGMuq5tC6vOZ9dx/RBe6eN2dq5/hiXt13FB2iKSIiK9RKR1jfMDReSoOJ0T3u9yvnzXnsgtIhzCbTeO44nHLiYywkFOXpFK22a2lGWxaM9OLvjs/TqlXGkMv+7dpdKugUrbNa7qdB5dEnuwpyj4VkWrQrPtgGa985hdx/WqAdhVG2RUle9Rs6CIRGI1sZfjQiLqzlrl5wLnOl+2ch6Hi8gU5+8Zxph7nb+/DJwBZAC7gYfl6K0VZxtjZld7fR7wJtZ8uGuqnX8a6C4iP2Ot3AbQl8Mj9B4yxvzs6vtoiNSUeB5+YAKD+nTgw29/w11lqbTr56RuXflh/ybSCwu48ZvpttYNqLR9RKBLe0TTgUxoM47ZB75lWfYv/g7HI1TaAc9MrJmqx4iIo5bk9FjnsWqvjZ+Ay4HTgA9qlB0FxANzjTENfri401TeH7i6xrkuHB4Btx2oEndn57EZ8HA9dc524bnvYEn9eOB0IArYj9UM/6IxZp4LdbhEz26t+MdD55KWHMcj/5vBt/N9t944hLa0Bfjz6FHcOGQw98/5nvfX2LZ2zmFU2grQNq4lf+x+GVvyNzB99/v+DscjVNqBjzFmu4h8ibUHx5+wVj8DQEROwdpkK5vD07+mAY8Dl4jIC9UWYIkF/uUs85Irz3ZZ3M51wie5WHaMq/VWu2cKMKWW869jNbd7FRH4y71nYAzcNGkqG7YdcPnexgobAkvadmfZcVGR/PvUUzi7dy/e+n0ZUz1dW7w+VNo+I9Cz7X4piRSWF/Dm1heosHUsrKIcxW3AAOBpETkTa1pYZ6zW6QrgBmNMDoAxJte50to0YLaITMXagGsC1lSxaVjTpRskrPbjro1mTRPJjCijuKSc+5/9gpz8YnLyXP+Q1Cy7fjqnpfHi+WfTPa0pjy2cy8vLvbChg0rbZwSytAWhbVwWK3OWsiZ3BeXGN/8u7Eaz7eDBGLNLRAZhtSxPwGryzgW+BP7PGPNrjfLTnZttPQBcAMRibdx1N/C8q1Olw1rcJ43uzZ9uO5lv5q7m2Xdms2Ovq4u5hV6WDd4ZhBaV7CAxKpqrvprG/F3b7a3cR8IGlTYEtrRjHTH849gb+TnjJ5Zk/azSVnyGMSYd+KPzx5XyC7DGgHlMWIo7OSmWO+88lfHDevL7hj1M+365W/cHQpZtZz12CzvK4eDk7t34Yvc61hxMZ8z7r1FWafNMwBDPsiFwpB3IwgaIdkTxyLE30CWxB3PSv/N3OB6j0lZcJezEfdwxbZl0/zmkJMXxvw/m8d5Xi4/agao+AkHagZxlt05K5Llzz2JgqzZsnZbF7+n7VdpuEijChsCXdpREMqnPdXRL7MXb215iRfZif4fkEeEo7S7Rro8jUo4k7MS9v7yYvRm53PX4p2zakd7wDU60abxhRnbswNNnn0FMRCR/+O4Lfk93fe67y6i0fUagS9uBg4eOuYreyX15d9srLM2ybVaoT1FpK+4SVuI2kcKu/dnc9PeaU+jqJxCybDvr8UZf9g3HD+bPo09kY9ZBbv3uc7Zkuz5ewCW0P9unBLq0AdrEZbKraDurc1fwS6a7uwQHBiptxRPCStzuolm2q3VWsqMkm882rOHBuT+6t3OXK6i0fUqgSLtLQnu2FNS+6lm7uGwAZuz9xIcR2Us4SluxBxV3HYRalg32S7tf61a0b5XC9I1rmbFlAzO2HLVbXeMJ8aZxUGnXxh3dr6RPcg9e3PQuv+esb/iGICNcpa3Ztj2ouGugWbZrXDmgP38bO5qdudl8tXk95XYPQAOVto8JBGm3jm3OX3vdROu45ny++yf2FB09TqIq2w5WVNpKY1FxV0Oz7IaJj4riX2eexITuvZm5bTN3//SN/dLWpnGfEwjSHtdiGLd2vYx9xem8tHkqvxxcQWHFkX9OwS7tcEWlbS8qbgIny7azHrBf2jGRkUy76lK6pjbhP4vm8tKyX93ehKVBVNo+JxCkfXu3KxjbYig/H1zG9/sW1No8HgrSDtds2+dUCuSHrt5C9525SKBIO5CFXUVBbCnT1q/m9wP7WLjHC1slqrR9TiBIe3Tz4xnbYig7C/fy5e6f2JC/DYCkyARSohIZ0qQfyVGFFFUU8VvWQooqCv0bsIeEq7Q127afsBV3oAjbznrAfmlHR0Tw1zGj+HrHen7Zu4vJy720wIX2Z/ucQJA2wIKMZQxKO5aRzQZyTEo3NuRvo2N8G05vPYoRTQeSEBl3qOygtGEsyPiJpVkL/Rix+6i0FTsJS3EHirTtFDbYL+02yUk8f+5Z9G/Zmn1l+fyyd1fDN7lLGGTZoNKuyT09r2NNzia+2TeXclPOlG2f0i2xI2e3GQfAcSk96J/am7np37O3aBcGw/FNRtI1sRcpUU3ILs1kc0FwjDZXaSt2E3bi1gForjG6c2eePOs0IsTBzd9O57utm2x/hkrbP/hT2u3iWnJ3z+voGN+G+Ig4Zqf/SlFFMZmlOby8+QMeOuY2rux4DpmlOby48f/YmL/m0L0b89YwruWZjGw2jsFNRrK9cAvlxuY1A2wmXKWteJfGp55BRGUj3224SHtIu3a8PvE89ubnMeGTd1TaHpKaUKTSrsYpLU/g2QEPkhSZQF5ZAalRSTiQQ9dX5qznvR1fkF2Wy5StTx+StjjLZJQeYHnWLxRXFNEuvqNf3oM7hLO0Ndv2LmGXcXtCIAobvNOfXRhXxsLsHTw090c+Wvc7JRUVtj4jHIQNgZdlg3+lfVePazih2SAWZPzGTwcWMbHdqfRO7krv5K4syVp1qNxXe2ZRVL6JvcW7D50zGATBYEgv2Y9DHCRFJhMXEUee3av02YRKW/EmKu4GCERp2y1shwjXDhrIdUMGMeGTd9lfkM87q5fb+gxApe0n/CnsTvFtuafndTSNSeWjnd8wJ30x+4rTaRHTlN7JXemR1ImlWasBS9CtYg+yOvfgUfUY58TDxKhkoh0xrC5YRl55rk/fi6uotBVvo+Kug3AQNkCPZs349xkn079la37YtsmtLU7dQqXtF/wp7S4J7Xm8733WgiqbPmBx1u8UV1h/N/uK0ymrLKdzQjsMps452lWZNkDzmFac2XoiZZWlLM5c4Ku34RYqbcUXqLhrEIjCBu9I+08jh3PL0CHklJZw+/df8tVmL4zSVWH7DX+PHM8szeGNrdPYXriHNbnWOIkqEW8r2E1xRQn9UnszvGkaOwuza62jSto9k45lZLNx9E7uy08HZrA6d7mP3oXrqLQVX6HirkYgSttbi6mUJ1bSpmkyX25axz9/nk1WsRfEo9L2G/6WNkB2WS4/7F9AuTk8TqKqvzq3PJ/5GUs5pdVIUqOasJOtR2TXANGOGNrGdeDE5ifRMb4bKVGpfLrrXeakf+ePt1MvKm3Fl6i4CUxhg/3SjouK5E8jRzBtyyrWZWbw51nfUuGNpvEwETYEnrQDQdjVqS7tKqrkXGb2EiER9Eo6lt9zlh4hbYAIieDM1hNpFduW7YVbeH3rs+wp8sKKfY1Epa34mrAWd7gIG2BYh/Y8etrJdExJZV9ZPusyM1TajSDQhA2BJ+36aBeXzarspZzd5iK6JvYkOSqV3LLsI8oUVRQybdfbJEQkBuxiK+EsbcV/hK24A1Ha3hB2UkwMfx09iov7HceW7Ewumj6VX4N8BTRQadck2KQNkF2Wxaa8tbSJa4+jjiUl9lWbFhZohLu0Ndv2H2En7kAUNnivL/vSoX254Lg+vPTbLzy7ZCElFV4QrGbZfiMYhV1FSWUxOWXZdE86hgFpQ5l14Bv/BOYBKm2Vtj8JK3GbiMbXEQzCbhIfR4sWiazK2M9ry5cwe/sW1hxMt/05mmX7l2CWdtVAtF8z5zG4yQhaxLTGgQPj/C+QUWmrtP1NWIm7sQSDtM89pjcPjB9DVnERJ019k9LKiqCXtg5AO5pgkfagtD6c2qof72x76QghV/1eUJ5HfnkuvZP7qrSDgGCRtlRAZH7oruit4naBYBB266Qk/nH6eMZ27MLSfbv586zvvLOYimbZfiVYhA0woc14ru50LruKthMXkUBhRf5RZXYVbaesspQoR3SdZQIFlXZwSDsccEncIjIRGA30B/oBScB7xpgr6rlnBPAgMAyIAzYCbwAvGFPLHJH6n38MMAkYAyQD24GpwGPGGK99ugaDsAG6NmnCJ1dehgj8fd5M3lm9POilrVn20QSLtCMlglu6Xsr4lsNZlvUL725/hdLKo/8+q5rL39j6AjsKt/ghUtdRaau0AwlXM+4HsYSdD+wCetVXWETOAT4BioEPgUzgbOAZYCRwoasBishQ4CcgCpgG7ATGAQ8D40VkvDHG9k/5YJB2bGQk+bGlrC/N4K1Vv/HBmpXsyvPC+s1hlGWDSrux3N/7cgakDeWbvZ/yzd5P62z+rjqv0g5sVNqBh6vivgtL2JuwMu9ZdRUUkWTgVaACGGOMWeI8/xCWgCeKyCXGmKkNPVREIoA3gXjgHGPMF87zDuAj4AJnbI+5+D4aJBiEHelwcP3gQVxz/EDOnvYO+wryeeKX+bY/BwgraauwG0+7uGx+OjCDFdmLWZq10N/hNBqVtko7EHGp994YM8sYs9EYl9pfJwLNgalV0nbWUYyVuQPc6mJ8o4HewNwqaTvrqgT+7Hx5i4hIbTe7Q1mi/XOyvSHt/q1b88lVl3Lf6BNZvHc35ZWVtj8DsITt46ZxlfaRBJO0B6X14brOowHYVrBJpR0CqLQDF28MThvnPH5by7W5QCEwQkRiXGjirrMuY8wWEdkA9AC6AJs9jDcosmwBHjl5PJf068v+gnxu+e5zvt2y0fbnAGGVZYNKu7FUH4T20/4ZlJvA3CPbHVTaKu1Axhvi7uk8bqh5wRhTLiJbgT5Ysl3raV1ONmKJuwceiDsYhF1FWWIlETEO3li5lGcWL6CgzAsfjipsvxNMwo52RHFjl4s4qeWIQ4PQVNqK4n28Ie4U5zGnjutV51N9UZeI3ATcBBCVlAYEj7BHderEPWNG8ufZ37E64wB/me2lXZF8LGxQaddGMElbEP7T9490TOja4CC0YCLcpa2ZdnAQ8vO4jTGTgckAsW3am0BfWxzguFYtuW/ciYxo24EdudkkR8d45TmAZtkBQjBJG6BtXBZz078nb28ua3NX+jucRhPuwgaVdjDhDXFXZcEpdVyvOp/t47psxVvSfvSsk7m493EcLCrk7/Nm8v6aFZR5YwBamAkbAlPawSTslKgk7ut5CUuyfmZJ5gJ+zfTSTAYfo9JWaduJiFwBvON8eaMx5rVq18ZQz6ws4HFjzF8beoY3xL0eGIzV77y0+gURiQQ6A+WAK5M3q/by61HH9e7OY1194LbjDWGnxcWSVVRMeWIl23KyeG7JQl5dvpj8slLbn6XCDhyCSdoD0/rwp+6XExsRx4rsxf4OxzZU2iptOxGR9sCLWGue1Ne+OweYXct5l74Ne0PcPwGXA6cBH9S4NgprTvZcFxdN+Ql4wFnX/1W/ICJdsIS+Hde+BDQKbwg7ISqK648fzPVDBnHnj1/zw7bNvLTsV9ufA2g/dgARTMKOkkiu6nQeZ7UZw+6iHbyw8dGA3mrTVVTYFipt+3BOS34TOAh8CtxbT/HZxphJnj7LG+KeBjwOXCIiL1RbgCUW+JezzEvVbxCReKADUGiM2VHt0hyskeejRGRCjQVYHneWednF+eUe4a0FVC7pdxy3jRhG8/gEvtq0ng2ZB21/DhCWwgaVtl2MbdGWs9qMYdaBb/hi94c6ajyEUGnbzh1YU5jHcHgqs1dwda3yc4FznS9bOY/DRWSK8/cMY8y9AMaYXBG5EUvgs0VkKtaSpxOwpndNw1oGtTpDsNr952C9aZx1VYjItViZ9zQRmQbsAMZjNccvwFpG1Xa8ObXrjUvOZ0TbDizavZMbvvmMFQf2eedB2iweMASTsAWhc0I7Sit/Z01uNv9e8xf2Fu/yd1i2oNK2UGnbi4j0xlrB8zljzFwRaUjc3UTkdqy9N/YB84wxLi/M4WrG3R+4usa5Ls4fsJqrDzULGGOmi8horGbuC4BYrOVS7waedydDNsb8IiLHA48Ap2BtcLId+AfWJiO22sJbwh7Srh1LcndTWlnBGyuXMnn5Ymbv2OqVZ6mwA4tgknaz6DTu7XkJXRN78ejaP5NRsl+lHWKotO3FOXbrHayk8n4Xb7vc+VO9nk+wBrNlNXSzS+J2tsVPcjGgqnsWAGe4WHY21uJgdV1fgxsbk3iCt4Tdq3kz7ht3IqM7dOb+Od/z/pqV/LjN40Xe6kebxQOKYBI2wNjmQ7mx60QE4eOdU8go2e/vkGxBhX0YlbZbNBORJdVeT3ZOL67Jw8AA4AQXdqtMB/4KfA1sw0pqBwP/xkpyW4nIKOey3nUS8vO4G8Jbwm6bnMxdJ4xgwjG9ySkp5p8LZvHJ+tVeeZYKO/AIJmkLwkPHXMmAtKFsylvHu9tf5mBpur/DsgWV9mHCStqVEJnf6C0sMowxg+sr4Ny98n7gKWNMgwv0G2NWA9VFkA98KyI/A8uxds88G/i8vnrCVtze7MMGeHLCaRzXvCUvL/uVl5f9Sm6pF0Snwg44gknYVbSNy2Jv0S62FWxi1oFvQmIFNFBpVyespO0jnE3kb2NNR36oMXU5x4a9j9W9PAoV95F4S9gpsbFcPXAAUzb8RmZxEQ/O/ZGckmL2FeR75Xnh2I8NKm27iI+I5brOE1mbO4v1edl8s+9Tf4dkGyrsI1Fpe41EDq8xUlzHJpWvisirWIPW7mygvqpmroSGHhxe4nZpE1P3SI2N5drBA7lq0ACSomPYVpzFpxvWsD7TSx8emmUHHMEkbIBjU7pzV/crSI1uQnbpJtbneakLxw+otA+jwvY6JcDrdVwbiNXvPR9rITFX9rkd5jw2uC5JeInbZu46YQRXDxpIYnQ0X29ezwtLFrJOhW0rKmz7iHZEcUXHCZzdZhwHivfyzPpH2Fa4yd9h2YZK+zAqbe/jHIh2Q23XRGQSlrjfqrHk6eCqtU1qlL8CuBgoBT5q6NkqbjeJi4qkqKyc8sRKOjZPY/aOLTy/dBEbVNi2EsjChuCTNsBZrXtxdptxzDnwHZ/vnkqZ8cKSun5AhX0kKu2AZpqIlANLgF1Yo8qPx1rLpBy42RizraFKVNwu0iQ+jhuOH8yl/fty7ifvsTk7kzt+/IpKby3aFqbChsCWdrAJO0IctI9rTblZzeLM+ewv3s32Qq+vEOwzVNpHotIOeF4CTsIaPd4Maxr0bmAK8KwxZoUrlai4G6B5QgI3HD+Iy/r3Izoigi82raOkwpKqV6Stwg5Ygk3a7eJacV/Py2kW05JHVt9FYUWBSjuEUWkHDnWtfWKMeZzDy3V7jIq7HmIiI/n2+qtJjIpm+sa1/Pe3RWzJbnBRG89QYQcswSbs+Ig4Lm5/Ome2GU1RRRHv73iVwooCf4dlGyrso1Fphxcq7moIMLpLZ07o0ZFJ83+inFIemPMDv6fvZ3tutnceqsIOWIJN2ABJkQm8OPABEiOTWHRwDl/u+Yj88lx/h2UbKu0jUWGHJypuIDYyknP79Oba4wfSNa0pe/Pz+O/SRaQXFfLV5vUNV+AJKuyAJRiF3TymCTGOLUA2c9K/Y03OCnYVbfN3WLahwj4alXb4EvbiPq5VS16feB5N4uJZeWAfd/z4FTM2b6C8st6lYj3DD7IGFbarBKOwm0anckvXMxmQNoTH1t7PvuLdfL+v3kWXgg6V9tGotMObsBR3r+bNSIuLY17mdtYWpzNn5zbeX7OCxXt3e+eBKmx/h1AvwSjsaEcUE9qMZ2K7k3GIg+/3fUFmaWgJToV9NCpsBcJM3IkxMUy59AJOaNeR1en7mTftHYrLy7lr5gwvPVCFHcgEo7DBmuL13ID7aBXblmVZvzJ993sq7TBApa1UEVbi7pSWStfUJjy2cC7vr3FpupxnqLD9HUKDBKO0O8S3ptKsBWDuge/ZX7yHDflr/ByVvaiwa0elrVQnrMS9NSeLE9971Tv916DCVmF7hTZxLbipyxn0Sz2eFzf+H+vzVjEv40d/h2UrKuzaUWErtRFW4s4vLSXJbmn7SdagwnaHYBR2WlQyF3c4k5NaDqesspSv9nzM1oKN/g7LdlTataPS9hyphCgvbcwYCISVuG1Fha3C9iIOhP/0u4u06CbMS/+R7/ZND6n52KDCrgsVttIQKm53CfPmcFBhe4tIiWB08yFszPuOSir5cMcbZJTsJ6M09D7IVdq1o9JWXEHF7QqaXQMqbG8hCCOaDeDqTmfRPKYlr24+wMqcpazL+93fodmOCrtuVNqKq6i460OFDaiwvcmxKd25ofPZdEzoyu6iHby06T+syfXijAc/ocKuGxW24i4q7pqorA8R6MIOVllX0T4uh1u7XkBsRBzvbHuJxZkLMHhpm1g/otKuG5W24gkq7ipU2IdQYXuPZtFpXNDuVGbuf4viSsOrW54huzSTclPm79BsR4VdNypspTGEt7j9KGsILGEHuqwhuIWdGBnPBe1O5czWowDYWrCQNbkryCjZ7+fIvINKu25U2kpjCU9xq7APocL2LoJwTtvxXNjuFGIj4vg1cx5f75lGdlmmv0PzCirs+lFpK3YQXuJ2GJ3O5USF7V0iJYJyU0HbuCwGp3Via8EGPt/9IXuLd/o7NK+gwq4fFbZiJ+Elbj+gwnafYBZ2UmQCZ7UZy+mtRvLU+oc5WAqvb3mOshDswwYVdkOosBVvoOL2AiprzwhmYbeKbc6ZrcdwcsthxETEsiJ7MSIOgJCUtgq7YVTairfwmrhF5BrgzQaKVRpjIlyoaxvQsY7L+40xrdyLzn4CTdagwvYV3RNLefS4+xEcLM36mR/3f8W+Yi/t7R4AqLTrR4WteBtvZtzLgUfquHYiMA74xo36coBnaznv16XkA03YKmvvEyWRnNh8MANS2/DhzjcoqoC3t73EloIN5JZl+zs8r6HCrh8VtuIrvCZuY8xyLHkfhYgsdP462Y0qs40xkxoXlT0EmqxBhe0LUqOSOa3ViZzReiRJUSnsKdpJrCOO4soilmf/6u/wvIYKu2FU2oov8Xkft4gcBwwDdgNf+/r5nqKy9pxglnUV/VN7c3/vm4lyRPF7zm/MPvAtG/JW+zssr6LCbhgVtuIP/DE47Sbn8XVjTIUb98WIyBVAB6AAWAnMdbMOtwhEWYMK2xc4EI5v0peUqGzW5K4gv2w589N/ZF7Gj6SX7PN3eF5Fhd0wKuzARip0P27bEJE44AqgAnjNzdtbAe/UOLdVRK41xsyxI74qAlHYKmvfEBcRy/gWwzmn7SiaxbRgdc4y1uSuoLiyiE93v+vv8LyKCrthVNhKIODrjPsiIBX42hjjzkoUbwLzgNVAHtAFuB0re/9GRIYbY2rdUklEbnKWI6Jpap0PUFk3jmAXNsCZrcdweceziIuIY3P+Oqbvfp/fs5f6OyyvorJ2DRW2Ekj4WtxVzeSvuHOTMabm6PRVwC0ikg/cA0wCzqvj3sk4B8HFdG53xNZLKuvGEQqy7pPcndLK1RRVFBIh+/g9eymz079lZ+FWf4fmVVTYrqPSVgINn4lbRPoAI4BdwAybqn0ZS9yjXL1BZd14gl3YURLJCc0Hc37bUbSL78inu95l1oFv+C1rEb9lLfJ3eF5Fhe06KmwlUPFlxu3poLT6SHceE1wp7IiotOmxjUdl7XsE4aL2p3NG6xNIdk7nem/7ZJZm/uzv0LyOCtt1VNhKoOMTcYtILHAl1qC0122sepjzuMXGOr2KCtv3tIptxr7iDNrGZdEvtT3bCzeHxXQuUGG7gwpbCRZ8lXFfCKQBX9U1KE1EooCuQJkxZnO1872BHcaYghrlOwEvOl8G9HBflbXviXZEMbLZIM5pM4J28Z2YtOpOssvglc1PUuG9GYQBgcraPVTYSrDhK3FXNZPXt1JaW2AtsB3oVO38xcA9IjLXeS0PS/BnArFY/eVP2hxvo1FZ+4fUqGTOaTuek1sOIyEykb1Fu/l01zsUVRQChLS0VdjuocJWghWvi9uZMZ+A54PSZgE9gQHASKz+7GxgPta87neMMabOu32Iyto/OBCSohJJitxJapSDM1qPYlXOb8xL/5FN+Wv9HZ7XUWG7j0pbCWa8Lm5jzFpAXCi3rbZyzsVVbF1gxU6CTdYQOsLuGN+GUc2PZ2yLQewv3sN/Nz1GdlkmD6z8A8WVwff34i4qbPdRYSt2IiKPA4OBHkAzoAirZXg68KIx5mAt94wAHsQaoxUHbATeAF5wdeC27sftASpr/zK6+RAuaj+ONnHtqTDlrM39nUUHZx+6HsrSVlm7j8o6MOkUdZTTgpG7gN+AH4ADWC3Cw7DWFrlJRIZVH9clIucAnwDFwIdAJnA28AxWi/KFrjxUxe0iKmv/kRgZz/CmA9ic/yOllSV0jI+kuKKIj3a8ybLsX8gvD433WR8qbPdRYQcmISLsKpKNMcU1T4rIo8D9wN+APzjPJQOvYs2uGmOMWeI8/xDwEzBRRC4xxkxt6KEq7npQWfuPaEcUg9OO49RW/TkmuR+Rjkhe3byflTlL+OnADH46YNcaPoGLytozVNiBSYgJG4DapO3kIyxxd692biLQHHi7StpVdYjIg8BM4FZAxe0OwShqCB1ZV9EkOoUXBz5EXEQcOaVZzE3/nsWZC9hVtM3fofkEFbZnqLADk1AUtguc7TyurHZunPP4bS3l5wKFwAgRiTHG1LvEZ9iLW2Xtf7omdmBUs8HERxbx+e4PgGzmpn/Hhrw1bMxbgyEgJg14FZW1Z6isA5dwEraI3AskAilYg9VOwJL2Y9WK9XQeN9S83xhTLiJbgT5Ym2jVOx0mLMUdjLIOJVEDdEloz4nNBzOi6bG0iG1FeWU5v2UtPHT9qz0f+zE636Cy9hwVdmDjb2k7KiEmr9FLXDcTkSXVXk92blpVG/cCLau9/ha4xhiTXu1civOYU0cdVedTGwosrMQd4TBBJe1QknW0I4rjUnqwLGstbeIyOaP1GYxuPoYNeauZuf8rlmf/SmFFQcMVhQAqbM9RYQc2/ha2zWQYYwa7UtAY0wpARFpibab1GLBMRM4yxvxmd2BhJe5AJ5REDdZo8EFpxzKkSV8GpfUmJiKW5zc8ysb8TH7c/xXf7v0spKduVUdl3ThU2IFNiAnbY4wx+4HPROQ3rCbxt4FjnZerMuqU2u6tdj67oeeouP1MqMnagVCJYViTFO7t9U8iJIKc0ix+zZzPyuwlbC2wunfyy3P9HKlvUGF7jso68FFh144xZruIrAH6i0gzY0wGsJ7Di7UsrV5eRCKBzkA5LmyapeL2A6Em647xbRjatB8jm/VhY94apu9+nz1FuXy/73NW5yxjR+HWsBhgVoXKunGosAMfFbZLtHEeq1ZD+wm4HDgN+KBG2VFAPDC3oRHloOL2CaEm6ioubn8GJ7U8nmYxLag0lWwr2MS+4t0AVFLJjL2f+DlC36Gybjwq7MBHhX0YEekB7DfG5NQ47wD+CbQAfjbGZDkvTQMeBy4RkReqLcASC/zLWeYlV56t4vYSoSbraEcU/VJ70SupC7MOvAVAx/gE9hXv5od9X/B7zm/kldc1WDI0UVk3HpV18KDSPoozgP8TkfnAVuAg1sjy0VhTuvYBN1YVNsbkisiNWAKfLSJTsZY8nYA1VWwa1jKoDaLitpFQk3ViZDzHpx3H6Ba96Z3cl2hHDIXlBSzJnE5eeQ7v73jV3yH6HJW1PaiwgwcVdp38CHTDmrM9AGsaVwHWoLR3gOeNMZnVbzDGTBeR0cADwAVYW1NvAu52lnepT1HF3QhCTdSC0DmhHZmlOSRG7mBQWm+u6XwVWaUHWZgxm5U5S9mUt45KQndP69pQWduDyjq4UGHXjzFmFXC7B/ctwMrWPUbF7QahJmqApMgE+qX2YmBaHwal9SI5KoXPdr3HTwd2sCpnGf9Z+wA7w2Sp0eqorO1DhR1cqLADHxV3A4SarB0ISVGJ5JTl0SWhiMf6PkuUI4r88jzW5q5kTe4K1uVay+uWVBaHlbRV1vaiwg4uVNjBg4q7BqEmaoCm0an0S+1Fv9ReDEjtyb7iXTy/8VFKK+GjnW+yt2gXOwq3hNWUrSpU1vaisg4+VNjBR9iLOxRFHeOIpqSyFIA/97yQ4c3GAJBTlsXa3JWsyjm8At+ig3P8EaJfUVnbi8o6OFFhBy9hJ+5QFHWEOOiR1Jl+Kb04vkk32sd35oHf/0BRRSGrc5ezt3gX63JXsbd4p79D9QsqavtRWQcnKuvQIKzEHeUIndHQDhxUUslprTpzdafbiI2Io9JUsqNwCzP3f02ERACwInuxnyP1Dypr+1FZBy8q7NAirMQdzLSKbc5xKd05NqUH/VK6MX3PByzJXMC+4j0szlzA+rzf2ZC3hqKKQn+H6hdU1N5DhR28qLBDExV3gBLtiKK0soy4iFj+O/B+0qKbAlY/9ca8tWSXWvP6M0r289HON/0Zqt9QWXsPlXXworIOfVTcAULT6FSOS+nBsSk96J/alR2F23hj63MArMxewr7i3WzMW8P+kr1+jtR/qKi9h4o6+FFhH0bKDdE5odM1WhMVt5+Ij4ijsMLai/qRPtfSN9Xar72gPI9N+etYnbPsUNlpu972S4z+RkXtXVTWwY/KOjxRcfuIlKhE+iT34LiUHgxI60pqVBP+svImKkwFK7OXsil/LRvy1rKnaEdYzqeuQmXtXVTWoYEKO7xRcXuJpMgEiipKKDflXNZhCBe2vxqA4ooiNuevY2HGbCIkkgpTwS+Zc/0crf9QUXsflXVooLJWqlBx20TLmKZ0T+pEj6RODEjtSpu49ry8+QnW5q5kc/46vtg9lQ15a9hZuJVKKv0drt9QUfsGlXXooMJWaqLi9oC0qGS6JXUkoySLsspVtI5tz/3H/AOA0spStuZvYMbeTzhQbA0k2120g91FO/wZst9QUfsGFXVoobJW6sOr4haRbUDHOi7vN8a0cqOudsA/gNOApsBeYDrwiDEmq3GR1o8D4bx2p9AtsQM9kzqQFt0EgLkHvufjXavYX7ybqTteZ3vBFvYW76TChO5oxoZQUfsOlXVoobJWXMUXGXcO8Gwt5/NdrUBEugI/Ay2Az4F1wBDgT8BpIjLSGNPof/XRjig6J7Sje2InuiV1pLIyg093vwvAGa1HUF5Zxqb8tewo2ML2wi3sKtwGQCWVLMj4qbGPD0pU1L5FZR1aqKwVT/CFuLONMZMaWcf/sKR9hzHmhaqTIvI0cBfwKHCLOxVGiIMWMU3ZW5wOwP29L2Vg2vBDS4VmlWayKif7UPl/rb6XMlPWyLcR/KiofYuKOvRQWSuNJeD7uJ3Z9inANuC/NS7/HbgJuFJE7jHGFNRXV2JkAtd3vpA+yW1pG98RYyq5b8UNGAxb8zdxsCSDHYVb2F64mdyy7CPuDVdpq6h9i4o6dFFhK3bhC3HHiMgVQAegAFgJzDXG5Y7gsc7j98aYI4ZjG2PyRGQBltiHATPrq6hlbFNObjmMXUXbmJ/+IzsKt+AQBxWmgnkZP7j1pkIRlbTvUVGHNiprxRv4QtytgHdqnNsqItcaY1zZDLqn87ihjusbscTdgwbEvbdoF39ecWNYT8eqjora96ioQx+VteJtvC3uN4F5wGogD+gC3I7VvP2NiAw3xqxooI4U5zGnjutV51NruygiNzmfR2Kr+LCVtkraP6iowwOVteJLvCpuY8wjNU6tAm4RkXzgHmAScJ6XY5gMTAZocUzTsFhLVCXtP1TU4YPKWvEX/hqc9jKWuEe5ULYqo06p43rV+exGxhSUqKT9h0o6vFBRK4GCv8Sd7jwmuFB2vfPYo47r3Z3HuvrAQwaVtH9RUYcfKmslEPGXuIc5j1tcKDvLeTxFRBzVR5aLSBIwEigEFtkbon9RSfsXlXT4orIOfqTCEJ0TulN4vSZuEekN7Kg5t1pEOgEvOl++W+18FNAVKDPGbK46b4zZLCLfY40cvw144XBtPIKVtb/S0BzuQEYl7X9U1OGLiloJNryZcV8M3CMic4HtWKPKuwJnArHADODJauXbAmudZTvVqOsPWEuePi8i453lhmLN8d4APOC1d2EzKmn/o5JWVNZKMONNcc/CmoM9AKs5OwFrANl8rHnd7xhjXBrl7cy6B3N4k5EzsDYZeQ4fbDLiKSpp/6OSVqpQWSuhgtfE7VxcxZUFVqrKbwOknus7gWsbH5l3UEkHBipqpQoVtRKqBPxa5YGGCjpwUEkr1VFRK+GCirsOVNCBhUpaqYmKWglXwlrcKufARCWt1IaKWlEswkrcMY5ylXWAoZJW6kJFrSi1E1biVvyHClppCBW1oriGiluxFRW04goqaSUUEJGJwGigP9APSALeM8ZcUUvZTsDWeqr70BhziSvPVXErHqOSVlxFRa2EKA9iCTsf2AX0cuGeFcD0Ws6vcvWhKm6lQVTQijuopJUw4i4sYW/Cyrxn1V8cgOXGmEmNeaiKWwFUzopnqKSVcMYYc0jUInWuH2Y7Ku4wQwWteIpKWlFsoY2I3Aw0BQ4CC40xK92pQMUdoqiglcagklYUr3Gy8+cQIjIbuNoYs8OVClTcQYzKWWksKmglFJHKSiJyixpbTTMRWVLt9WRjzORG1FcI/BNrYNoW57m+wCSsnS5nikh/V7aoVnEHOCpnxS5U0oriFhnGmMF2VWaMOQA8XOP0XBE5BWvXzKHADVi7XtaLijtAUEErdqGCVpTgwRhTLiKvYYl7FCruwELlrNiJClpRQoZ05zHBlcIqbptROSt2o4JWlJBnmPO4pd5STlTcbqJiVryFClpRQhcRGYi1+EpljfPjsRZyAXjXlbpU3DVQMSveRgWtKKGBiJwLnOt82cp5HC4iU5y/Zxhj7nX+/jTQXUR+xlptDaxR5eOcvz9kjPnZleeGlbhjpEzFrPgElbOihAX9gatrnOvi/AHYDlSJ+x3gPOB44HQgCtgPfAS8aIyZ5+pDw0rcimInKmdFCW+ca45PcrHs68DrdjxXxa0o9aByVhQl0FBxK2GNillRlGBDxa2ENCpmRVFCDRW3EtSomBUlOOgSafwdQsig4lYCGhWzogQfKmnvouJW/IZKWVGCH5W071FxK15DxawooYMKOnBQcSseoVJWlNBEBR34eE3cItIUa5WYM4HjgLZAKfA78CbwZs01W+upaxvQsY7L+40xreq4pniIillRQpuQFnR5JZKV5+8ovIY3M+4LgZeAvcAsYAfQEjgfeA04XUQuNMa4+q8nB3i2lvP5jQ81fFAhK0r4ENJyDmO8Ke4NwATg6+qZtYjcD/wKXIAl8U9crC/bubycUgcqZUUJT1TQ4YXXxG2M+amO8/tE5GXgUWAMros7bFEhK4qiclaq8NfgtDLnsdyNe2JE5AqgA1AArATmGmMq7A7OF6iMFUWpjopZcRWfi1tEIoGrnC+/dePWVljbolVnq4hca4yZY0twjUBFrChKfaiYFbvwR8b9GHAsMMMY852L97wJzANWA3lYe53eDtwEfCMiw40xK2q7UURucpajWZsol4NUESuK4g4qZsVX+FTcInIHcA+wDrjS1fuMMY/UOLUKuEVE8p31TcKaelbbvZOByQDH9I02KmRFUTxBxawECj4Tt4jcDjwHrAHGG2Mybaj2ZSxxj7KhLkVRwhyVsxIM+ETcInIn8AxWpjzeGHPApqrTnccEm+pTFCXEUTkrwY7XxS0if8Hq114OnGyMybCx+mHO4xYb61QUJUhRKSvhgFfFLSIPAf8AlgKn1Nc8LiJRQFegzBizudr53sAOY0xBjfKdgBedL9+1OXRFUQIElbGiHIk31yq/GkvaFVgjwu8QkZrFthljpjh/bwusBbYDnaqVuRi4R0TmOq/lYQn+TCAWmAE86ZU3oSiKV1AZK4rneDPj7uw8RgB31lFmDjClgXpmAT2BAcBIrP7sbGA+1rzud9xY71xRFC+iQlYU7+PNJU8nYU3TcrX8NuColNy5uIrfF1hRlHBFZawogYXux60oYYDKV1FCBxW3ogQJKl9FcZGKciozs/wdhddQcSuKn1ARK4riCSpuRbERlbGiKN5Gxa0oqHAVRQkeVNxKUKPCVRQl3FBxK35BhasoiuIZKm7FI1S8iqIo/kHFHUaobBVFUYIfFXeAopJVFEVRakPF7QEqVUVRFMVfhJW4Y0SlqyiKotiHiLTD2gnzNKApsBeYDjxijPHK8m1hJW5FURRFsQsR6Qr8DLQAPgfWAUOAPwGnichIY8xBu5/rsLtCRVEURQkT/ocl7TuMMecaY/5qjBkHPIO1HfWj3nioiltRFEVR3MSZbZ8CbAP+W+Py34EC4EoRSbD72SpuRVEURXGfsc7j98aYyuoXjDF5wAIgHhhm94NV3IqiKIriPj2dxw11XN/oPPaw+8FhNTht+cqy/LS2u9b7Ow7lKJoBGf4OQjkK/XsJTELp76WjNyrNrcz87vvCd5o1sppYEVlS7fVkY8zkaq9TnMecOu6vOp/ayDiOIqzEDaw3xgz2dxDKkYjIEv17CTz07yUw0b+XhjHGnObvGLyJNpUriqIoivtUZdQpdVyvOp9t94NV3IqiKIriPlXdrnX1YXd3HuvqA/eYcBP35IaLKH5A/14CE/17CUz07yUwmOU8niIiR7hURJKAkUAhsMjuB4sxugSooiiKoriLiHyHNZf7DmPMC9XOPw3cBbxijLnF9uequBVFURTFfWpZ8nQtMBRrjvcGYIQ3ljxVcSuKoiiKh4hIe47eZOQzvLjJSMj3cYtIOxF5Q0T2iEiJiGwTkWdFJM3fsYUjItJURG4Qkc9EZJOIFIlIjojMF5Hra/YVKf5FRK4QEeP8ucHf8YQzIjLe+f/NPudn2R4R+U5EzvB3bOGMMWanMeZaY0xrY0y0MaajMeZOb0kbQnwet792blHq5ULgJaxvpbOAHUBL4HzgNeB0EbnQaFOQ33FmEi8C+UCin8MJa0TkP8B9wC7gC6wFWJoDg4AxwAy/Baf4nJAWN0fu3FLbwIFHAdsHDij1sgGYAHxdfX1fEbkf+BW4AEvin/gnPAVARAR4EzgIfArc69+IwhcRuRFL2m8BNxljSmtcj/JLYIrfCNlmSX/u3KLUjTHmJ2PMl7Usyr8PeNn5cozPA1NqcgcwDrgW6/8VxQ+ISAxWgrGDWqQNYIwp83lgil8JWXHjx51bFI+p+gAq92sUYY6I9AYeA54zxsz1dzxhzslYTeKfApUicqaI/EVE/iQiw/0cm+InQrmp3JWdW07BWvVmpk8iUupERCKBq5wvv/VnLOGM8+/hHawM734/h6PA8c5jMbAMOLb6RRGZC0w0xqT7OjDFf4Ryxu23nVsUj3gM60NphjHmO38HE8Y8DAwArjHGFPk7GIUWzuN9gAFOBJKAvsD3wCjgY/+EpviLUBa3EiSIyB3APVij/q/0czhhi4gMxcqynzLGLPR3PApw+DO6HJhgjJlvjMk3xvwOnIc1yny0NpuHF6Esbr/t3KK4jojcDjwHrAHGGmMy/RxSWOJsIn8bq2vpIT+Hoxwm23lcZozZVv2CMaYQqGqdGuLDmBQ/E8ri9tvOLYpriMidwAvAKixp7/NvRGFNItb/K72B4mqLrhisWRgArzrPPeuvIMOQqs+x7DquVy3yEef9UJRAIZQHpx2xc0uNOcNe3blFaRgR+QtWv/Zy4GRjTIZ/Iwp7SoDX67g2EKvfez6WSLQZ3XfMxOrbPqbm55iTqsFqW30bluJPQlbcxpjNIvI91sjx27AyuyoeARKwdm7ROao+RkQewlrbdylwijaP+x/nQLRalzQVkUlY4n7LGPOaL+MKd4wx20XkS6xFi/4EPFN1TUROAU7FysZ1JkYYEbLidvIHrCVPnxeR8Ry9c8sDfowtLBGRq7GkXQHMA+6wFuk6gm3GmCk+Dk1RApXbsL44PS0iZ2JNC+sMnIv1/9ENxpi6Zs8oIUhIi9uZdQ/m8M4tZ2Ctkf0cXty5RamXzs5jBHBnHWXmAFN8EYyiBDrGmF0iMghrqt4ErClgucCXwP8ZY371Z3yK79FtPRVFURQliAjlUeWKoiiKEnKouBVFURQliFBxK4qiKEoQoeJWFEVRlCBCxa0oiqIoQYSKW1EURVGCCBW3oiiKogQRKm5FURRFCSJU3IqiKIoSRKi4FUVRFCWI+H9tYy2LCLnRygAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot pero separados\n", - "levels = np.arange(0,105,5)\n", - "# Colormap:\n", - "# https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html\n", - "# jet , hsv , Greys , YlOrRd\n", - "colors = plt.cm.BuPu(np.linspace(0,1,int(len(levels)+1)))\n", - "\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 2\n", - "\n", - "#fig,ax=plt.subplots(5,2)\n", - "for j in range(int(len(beta)/2)): \n", - " cp = plt.contourf(T_T,examrate*100/population,totinfected[:,2*j,:],levels) \n", - " for l in levelcourves:\n", - " cp2 = plt.contour(T_T,examrate*100/population,totinfected[:,2*j,:],[0,l,100],colors='white',linestyles='dashed')\n", - " plt.clabel(cp2, inline=1, fontsize=20,fmt=str(l))\n", - " plt.title(r'$\\beta$ = '+str(round(beta[j*2],2)),fontsize=25)\n", - " plt.tick_params(labelsize=20)\n", - " #if int(j/n)==4: \n", - " # ax[int(j/n),j%n].set_xlabel('Results delay [days]',size=25) \n", - " #if j%n==0:\n", - " # ax[int(j/n),j%n].set_ylabel('Tested [%]',size=25)\n", - " \n", - " cbar = plt.colorbar(cp) # increase fontsize\n", - " cbar.ax.tick_params(labelsize=20)\n", - " plt.savefig('plot_'+str(j)+'.pdf',dpi=100,format='pdf')\n", - " plt.show()\n", - " #ax[int(j/n),j%n].saveplot('plot_'+str(j)+'.pdf',dpi=100,format='pdf')\n", - " \n", - "#fig.suptitle('Total infected proportion to the dynamic with no exams')\n", - "#plt.tight_layout()\n", - "#plt.subplots_adjust(hspace=0.4)\n", - "\n", - "#if saveplot:\n", - "# plt.savefig('plot2.pdf',dpi=100,format='pdf')\n", - "#plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 3: Test vs Beta para distintos delay" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = True" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "if saveplot:\n", - " %matplotlib inline\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":12: UserWarning: No contour levels were found within the data range.\n", - " cp2 = ax[int(k/n),k%n].contour(beta,examrate*100/population,totinfected[:,:,k*2],[0,l,100],colors='white',linestyles='dashed')\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot\n", - "levels = np.arange(0,105,5)\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 2\n", - "\n", - "fig,ax=plt.subplots(4,2)\n", - "for k in range(round(len(T_T)/2)): \n", - " cp = ax[int(k/n),k%n].contourf(beta,examrate*100/population,totinfected[:,:,k*2],levels) \n", - " ax[int(k/n),k%n].set_title('Results delay: '+str(int(T_T[k*2]))+' days',fontsize=25)\n", - " for l in levelcourves:\n", - " cp2 = ax[int(k/n),k%n].contour(beta,examrate*100/population,totinfected[:,:,k*2],[0,l,100],colors='white',linestyles='dashed') \n", - " if np.min(totinfected[:,:,k*2]):12: UserWarning: No contour levels were found within the data range.\n", - " cp2 = ax[int(i/n),i%n].contour(T_T,beta,totinfected[i,:,:],[0,l,100],colors='white',linestyles='dashed')\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "data": { - "image/png": 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AOfdKB15Gn+1ERGKnrEtA1pnZUEJFio2EFrMa4vwW+mwnIhI7ZZ0Ha3bhQo57CBWeZBDaJq4jhZiR3Gor56Ifjznnwr4AnAH8zDm30sxygF8QupjgDOfcBgAz+w9wC/BNQp/vRNqkrXYk3iJVkbujWlS1d8uPPNk5V+acuwA4h9A/ch8Qqkg8Bfg+sNHMburCuAAmxjiuRzrzBxBQO6O+H9nGcZHH9jnnKmJ5YTPrBfyZUDuvL4fvPgU4DFgcOTEZ9itCVwxcFd5WQUTEj5R1SWJmvyB0wg/gHufc77rwch3NuubPaVU4sx4ilGF3OOdc+Kr1MwntQfufqMP/BRQBZ5rZ4FheX0TEI8o7jzjnFgCzwz9+voNP12c7EZHYKesS42eErpZ/CFhnZn2ib4S6hgAQdX/31l6sBfpsJyISO2WdR5xzmwjlBIS6h3REJLcGhLd/a00k62LKOQAzG07oAoE84P7w3dMIFbG8Eik6CYtsz3RtrK8v6U0f/iXeCsNfc82sd2dfxDk32zn3Tefc2YRa+F4DrCRUWf5wuHK+M+OCD9t3dUSkrVirH2bMrAehD1VBtCrq+2PbOC7y2JoOvPYPgbHAN5xzu8P3RUJ2c/SBzrkyQkHcF9AHNhHxK2VdEpjZL4Gvh3/8hnPu1118ybVAU/j7WLKu0DlXEuNr30lo39RfOucimRrJui3Oucj7Rlpubml2jIiIHynvvBW5cm9CB5+nz3YiIrFT1iXGuPDXOwldgd389q2oYyP3/aIDr6/PdiIisVPWBVNHP9et7sBr/wHoD9zpnKsO39fi5zpgY7PHRdqkwhOJtw/CX7sBl8XjBZ1zNc65lwjtEQ3QEzg76pCmqO9b3KAuXKEZOXF3VSeGEdmn81xrfRO8qQR3+6oNQGTf7UtbOiA8KTkn/OObLR3TwnNOJHQl3GzgHy0c0lKlZlvVmyIifqCsS7Dw9jr3hH/8hnPul119TRfaezbyd9da1hlwSfjHWLNuBPBTYBPwoxYOUdaJSFAp77wVObHX0ZbJ+mwnIhI7ZV0A6bOdiEiHKOs8YmaH8+E2QlvaOrYFs4FIUUhrWTcGmBj+MdasuxK4AXjcOdfSc5rnmnJOOkSFJxJXzrmNwMzwjz8xs/5tHW9mA6O+z2ynBW911PfRwRW9Z3VOG8//e/jrZ83spFjHFfZU+OthwKdaOD4D+G5br+lnzjkHPBr+8UYzG9vCYXcBfQjtz/p4e68Z1ZqyCfh8+D0iIiF7spllRj1nUvg99hNqXyki4jvKusQKF51Eb6/T5aKTKP8Ofz3fzE5r4fGP8uFC36MtPN6SPwL9CF0lUBN1fyTrjjCzAZE7zWwQH1693tEPnSIiSaO8Swwz69bGidHIMRcCU8I/zuzI6+uznYhI7JR1ieGcO6+trRIIddCKHBu5/8sdfBt9thMRiYGyLjFi+ExnQOScZhPwSkde3zlXCTwb/vHOVv7evhn+uh94ob3XDF+A8CBQAnyl2cORHJvS7P7Tw1/z2nt9EVDhiSTG/wMqgCOBeWZ2jZn1jDxoZiPN7FYzmwH8POp5owjtB/ddMzup2Umr44HHwj9WAgf2jg638I1URn46+nnN/JpQ66+ewLtmdnf4A0LkPXLM7DIzexSYFf1E59x84KXwj38xs9vDbbows8MIhdwZQFVrfyhmdp+ZufBtbGvHtcXMBphZbuTGh/8f7hV9v4X2S+3o+/+KUHuzXsCrZnZK+HndzexOPqz0f6jZHm+tuRs4Ffi5c25ts8cWE/o7Gwn8OPweAwl9wIPQPnJNiIj4l7KuBV3NOjP7BR8WnXy1o9vrmNltUe9/XguH/JvQn48Bz4YX9jCzDDP7KB9+4H3NOTcjhve7mtDVHf9xzr0d/Vh4C4L5hP4u/mhmvcysF6Gs6wnMdc7taf6aIiI+o7xrQRfzbjSw1MzuMLPx0ScszWy0md0LvEgoq0qA33bi/fXZTkQkdsq6FsTjPGZX6LOdiEhcKeta0MWsG2NmC5p/rgvn0OnAa8B14WP/5pxb38L7PxJ5/1be4/uE/myHAy+b2RHh5/U2s+8DXwgf92PnXGkMY/4RoUKdrzvnml8c8BahQqLTzOwuC10wMRr4WfjxF2N4fRFwzummW4dvwH2AI3xBVQuPnwXsihwDNBDa37kq6j4H/D3qOWObPdYAFAO1UffVAje08H7fjTqmhlBr4XzgyWbHjQDmRh3bBJQC5c3ee2ML7zEIWBZ1TF34uZHX+WL4PR1wW1t/ZsDYTv655zcbZ2u3Rzrz/sAp4b+nyHH7wr9n5Oc3gB4xjHNU+LnrWzseuD785xb5O2sMf1/c2T8f3XTTTbd43pR1yc06Qh98Is9tJLRg1tbtnhZe47ao1zivlfcZS6iKP3JcJaEPVpGflwADYhhvn/DfQRGQ28oxZ4T/rhxQz4eZWg1M8fq/cd10000355R3HuRd8z+bWkIdQSqa3Z8HnNTe31lr748+2+mmm266Hbgp67w5j9nZv4/wMbdFvf95rRwzFn2200033XTDOWVdsrOuhT+bGkKf62qa3f8wkNnKazwSQx5eHs63yOuVhf8eol/fYhjvyeHnzWzteEJdUCKvG52nm4Ecr/8b1y0YN3U8kYRwzn1AqHryHuB9Qv8Y5hA6AbWWUCXkzYT2iI7YAVxN6IqueYRCsA+hfwzXEGoBdaxz7pkW3vKnwP8R2tetntDJsTHAsGbj2klor7mbCFVD7iJ0FVh3QgH0cnhMU1v4nYqBM4EfAOsIBVcD8DpwsXPuz+3+wficc24xMInQ38FGIItQqM0Gbgcuc87VxvBSfwL6Al9o7Xjn3LPAFYQmFU2ETrS+AJzpQvv7iYj4mrIu7jKafT+0ndsh3b1iEc6Y44H7gVV8eOJwMaG/y9NdbFcJ/JjQVev3OOeKWnmvucC5wNuEPojXATOAc51zCzozfhGRZFPexd1OQu3/HyT0OxYRauufQehk7MvA54BJzrmlnX0TfbYTEYmdsi6Y9NlORCR2yrq4202ok8x/Cf1Z7CP051kfHsvDwNnOuc845xo6+ybOuemEsu7vhP48ehIqrnmLUMHPZ5xzrq3XMLNuhLZObQDuaO1459xvgU8Cywl1FCsmtF3d2S7UxUakXdbOf48iIiIiIiIiIiIiIiIiIiIiIi1SxxMRERERERERERERERERERER6RQVnoiIiIiIiIiIiIiIiIiIiIhIp6jwREREREREREREREREREREREQ6RYUnIiIiIiIiIiIiIiIiIiIiItIpKjwRERERERERERERERERERERkU7J9HoAfjNoYIY7bLT+WETEf5atqC9yzg3u6utcdH5PV1zSFOt7vuGcu7Sr7yn+kzOwmxsxqpvXw5CA62Fej0BSTbyyDpR3Av0GZrohI7t7PQzPdbcGr4cg4nvJntPos53ES+8B3d3AkdleDyMmPaze6yFIAmieIa1Zu1JZJ/GTndPT9R3R2+thHKJHhv4N7AzNCfxJmd5x8co6SJ28U4VFM4eNzuTd14Z6PQwRkUMMGFmwNR6vU1zSFPO/cwNGFuTG4z3Ff0aM6sZjrwzzehiSIsZnOq+HICkiXlkHyjuBISO784sXjvJ6GL4xNqvY6yGI+Foy5zP6bCfxMnBkNv/3v9O9HkaHjO++x+shSJxpjiEtOWXMdmWdxE3fEb35+GO+XGNlTHaR10MIJM0H/EmZ3jHxyjpInbxT4YmIiIiIdElew4eXCasIRUTEn/LrBwE6kSTSmsh8RnMZkcTKqxsCaMEplWiOISLpbGt1aP1XBSgdo/mAPynTpasyvB6AiIiIiKSOvAY7qBBFRET8Jb9+0IGTSSJyKM1jRJIjsuAkqUPzCxFJZ5ECFOkYzQf8SZkunaXCExERERGJu0gBihZvRET8SSeSRFqnOYxIcuTVDdGCU4pRgauIpLOt1bkqQOkEzQf8SXkunaHCExERERFJKBWhiIj4kxaHRNqmuYtIcmjBKfVojiEi6UzFJ52juYD/KMulo1R4IiIiIiJJowIUERH/0eKQSOs0dxFJHhWgpB7NL0QkXan7SedoHuA/Ol8gHaHCExERERFJOnVBERHxH51QEmmd5i0iyaNFp9Si+YWIpDMVn3ScClH9SVkusQhE4YmZXWpm681sk5nd28LjXzWzNWa2wsxmmNmYqMc+ZWYbw7dPJXfkIiIisVHWSTpTEYpI+lDeBYMWiERap/mKtEdZFx9adEo9ml+IpA5lXceo+0nnaB7gP8pxaY/vC0/MrBvwIHAZcAxwk5kd0+ywpcBk59zxwDPAL8LPHQj8ADgNmAL8wMwGJGvsIiIisVDWiXxIBSgiqUt5Fzw6qSTSMs1XpDXKuvhTAUrq0fxCJNiUdZ2n4pOO0zzAf1RIKm3xfeEJofDZ5JzLc87VAU8C10Qf4Jx71zlXFf5xHjAq/P0lwFvOuRLnXCnwFnBpksYtIiISK2WdSDNa0BFJScq7ANJJJZHWaa4iLVDWJYgWnlKL5hcigaas6wJ1P+kczQH8RzkuLcn0egAxGAlsj/q5gFA1ZGs+C7zWxnNHNn+CmX0e+DzAqJHdujJWERHfq3U6QepDCc86ODjvhinvJCAi/16Nz3Qej0SCRnnnS0n9bJc7IqsrY5VmIieVxmYVezwSEX/xcq6irPOlpGZdzvCeXRlrIOXVDWF89z1eD0PiRPMLaY+yzpeSfh6zz7BenR2rb22tzmVMdpHXwwiUSPGJ5gH+kV8/SBkeJ6mSd0HoeBIzM7sFmAz8siPPc8495Jyb7JybnDsopf5IREQkxXQ26+DgvBswUHknwaIOKCLpJR6f7foPDMJ1FsGjK5RFWqZ5inRUPLKuz8DuiRmcz6n7SerR3EIkNcXrPGb2gNQstFT3k87RPMBflOESLQirTjuA0VE/jwrfdxAzuwj4DnC1c662I88VERHxmLJOJEZa1BEJNOVdCtHJJZFDaZ4iKOuSSgtPqUXFrSKBoayLMxWgdI7mAP6hDJeIIBSeLASOMLNxZtYduBF4KfoAMzsJ+BuhAIvusfQGMM3MBpjZAGBa+D4RERE/UdaJdIC6n4gElvIuxejkksihNEdJe8o6D6gAJbVobiHie8q6BFHxScdpDuAvynDxfeGJc64BuJtQ+KwFnnbOrTaz+83s6vBhvwT6AP8zs2Vm9lL4uSXAjwgF4ULg/vB9IiISJxZyu5nNN7MKM6s0s0Vm9gUzazFnzOxKM5tpZuXh58w3s08le+x+oawT6RwVoEgyKe+6TnmXulSAInKwoM5PlHVdp6zzlhaeUofmFZIoyrqu8yLr+mX2p5t1S8Bv4z/qftI5mgP4hzLcH7zKu0Bseu2cmw5Mb3bf96O+v6iN5z4MPJy40YmIpL3HgE8Ae4AngCrgYuAvwJnAJ6MPNrO7gT8CxeHn1gE3AI+Y2XHOuXuSN3T/UNaJdF5egzE+03k9DEl9yrs4UN6ltsgJprFZxR6PRMR7AZ2fKOviQFnnrcjC0/jue9o5UvxO8wpJEGVdHCQ763K6D+QbR/+Ep7Y9TF7lho4ON5C2VucyJrvI62EEiuYA/qEM9wVP8i4QhSciIuJPZnYdofDaAkxxzhWF7+8OPAvcamYvOOeeC98/FvgVUAJMds7lh++/n1CV+9fM7Fnn3Nxk/y4iEmyRK4sDuMAjAaC8E+kYnWQSCQlS8YmyTlKNFp9SR379IM0pJC6UdcG1q2YvPTOy+cpRP2BO0bu8uOMJqhorvR5WwkU6n6gApWPy6oYo/31CGe4NL/PO91vtiIiIr10X/vrrSHgBOOfqgO+Ff7w76vjPAD2AP0XCK3x8KfDT8I9fSNhoRSTlafsdSRDlnUgnqMWuSKC23VHWSUrKqxui9vspQHMKiRNlXUBVNVRz15Kf8sKOtzlt0FSuHfkJr4eUVNp6p+OU//6hDPeEZ3mnwhMREemKYeGveS08FrnvnHAlJcAF4a+vt3D8a82OERHptAAt8kgwKO9EOim/fpBONEnaC8i8RFknKU2LT8Gn+YTEgbIuwGqaavl3/vPcs+znvLrrGQAGdR/M4B7D2nlmathanasClE5Q/vuDzgsknWd5p8ITERHpiki15LgWHhsf/poZ9f1R4a+HbMbpnNsFVAKjzKxXPAcpIulJ3U8kjpR3Il2kE02S7gIwJ1HWScrT1c/Bp/mEdJGyLgXkV+1g9T5HQXUO1426mW9N/BmXDfsImZbp9dCSQsUnHaf89w9leNJ4lncqPBERka54Nfz1q2Y2MHKnmWUBP4w6bkD4a//w1/JWXq+82XEiIl2mAhSJA+WdSJxowUjSmc/nI8o6SRtagAo+zSWkk5R1KeZ3G55jedlCLh9xPfdOfIAj+hzj9ZCSQt1POkfZ7w/K8KTwLO/SowRQREQOqHOZHQj37blmtijqjoeccw9F/fwkcCtwCbDGzF4EaoCLgOHANuAwoKnrIxcR6ZrIYs/4TOfxSCQZlHci/hb5/+fYrGKPRyKSXPEsPlHWiXRNZAFqfPc9Ho9EOkNzifSgrJO2lNXv4ydrH+eEnMXcNeFjfOnI7/DIlj+xuHSu10NLiq3VuYzJLmr/QDlA2e8PyvBDpUreqfBERETaUuScm9zag865RjO7CvgqcAvwKUIBNhO4HngmfGhkJlcO5BKqjGxpVtFeZaWISJepAEVaoLwT8Uh+/SCdbBJJDmWdSCvy6oZoASrANJeQKMq6NLW8bB13L3mAK4efz8ryJQD0y8xhX0OZtwNLgkjnExWgdIyy3x+U4Z3m27zTVjsiItIlzrl659zPnXPHOed6OudynHPXAvnAEYRCcEv48PXhr0c2fx0zGw70Bgqcc1VJGLqIpDmft7sXn1HeiSROZPsdtdwV8ZayTtKZtt8JNs0jJFbKutRV11TPczveJK8ym0zL5EtHfocvHfFdhvYc4fXQkkLb73Scst8flOGJ4VXeqfBEREQS5UagO/BE1H3vhL9e2sLxlzU7RkQk4fIaTAUo0lXKO5E40kknEV9S1kna0CJUsGkOIV2grEshW6v68r/t7zAiezT3Hv0AVwz/KFmW5fWwkkLFJx2n3PcHZXjSJDTvVHgiIiJdYmb9WrjvROCXQCnws6iH/gXUAneb2dio4wcA3w7/+NdEjVVEpDUqQJH2KO9EkksFKCLJp6wT+ZAKUIJLcwhpi7IuPTgcM/bM5a4lP2JW0WIuHX4t35r4MwZ0T4+iDHU/6Tjlvj8ow+PHq7zL7NxwRUREDnjLzKqBVcB+YCJwBVANXOWc2xk50Dm3xcy+DvwBWGRmTwF1wA3AKODXzrm5yf4FREQiIsUn4zOdxyMRH1LeiXggctJJ+z6LJIWyLmBqm3RqN9Hy6oYwvvser4chnZBfP0jzB2mJsi6NlNdX8IeNj/LunnlcM2IKZXWhfxMMw5H6530ixSdjsos8HklwKPf9QRkeF57knT6diIhIVz1DqD3XLUA2sAN4CHjAOVfQ/GDn3B/NLB+4B/gkoe5ba4DvOuf+naxBi4i0Ja/BVHwizSnvRDykAhSRpFDWBZAWlRIvcgW0FqKCR/MHaYGyLg2tLN/AyvINQH+O6tPAV4+6jxm7X2Vu8cy0KUDRPCF2yn1/UPFJl3mSdyo8ERGRLnHO/ZJQe66OPOdl4OXEjEhEJD7U/USiKe9E/EELSCKJo6wLNhWgJJ4WooJL8weJUNZJSf0QyutLuWnM55g88Cye3PZP9tTu8npYCad5Qsep+4n3lN+d51XeZXTlySIiIiIiqS6vwQ4UoYiIiD9o72cRkZZtrc49sLgkiZFXN+RAEYoEi+YOIrK3toRvrHiQP218jJHZh3HvxAe4ZNi1GOlx3kfzhI5R3vuD8js4VHgiIiIiIhIDFaCIiPiPClBERFqmhaXE02JUMGnuICIAM/bM5a4lP2Zl+WJG9xqbFlvuRNMcIXYqOPUH5XcwqPBERERERKQDVIAiIuI/OgklItIyFaAklhajgkvzBhEpq9/Hj9b8hx+veQyAwT2G8ZFRt9Ajo6fHI0sOzRE6RnnvD8pvf8v0egAiIpJctS5LkyQRkTjIazDGZ6bXFTFBorwTSU/aA1rSibJOOiKysDQmu8jjkaSmyP8Xx3ff4/FIpCM0b/A/ZZ0kQ4NroKA6h0n9T+XcwZdwYs4Untn+b1aUL/Z6aEmxtTpX84MYKe/9Ib9+UMpld6rknTqeiIiIiIh0krqfiIj4kzqgiIi0TFc2J5Y6oAST5gwiAvBG4Sy+teLXVDdWcfvhX+ULh99Dbvf0+Ddd3U86RlnvPX3m9ycVnoiIiIiIdJEKUERE/Ekno0REDqXFpcTTglTwaM4gIgAbKvL50tJf8K8tz3J4n6M5a/CFXg8pqTRHiJ2KTf1B2e0v2mpHRERERCROtP2OiIg/qZW+iMihtP1OYqkdfzBpziAija6Jl3a+w+yixQzI2g3AuN5Hkt0tmzX7lns8uuTQ9juxy6sboqz3WCpuvRNUKjwREREREYmjSOcTFaCIeKvWZXk9BPEhLSaJiBxKBSiJpQKUYNIiloiU1JVTUtcT6Mnt46/g+JzJLC9byLMFj1Fal/qZqflB7JT13tNnfX/QVjsiIiIiIgmg7XdEvKfWt9IatdMXETmU2usnluYkwaP5gohE/GjNo/wn/wWO7nsc3z3mF0wbejWZlh7X9mtuEDtlvfeU295S4YmIiIiISAKpAEXEeypAkdZoQUlE5FAqQEkczUmCSfMFEWlwjTy34y3uXvJj1u5bwVUjP84pA870elhJo7lB7JT13lNmeyc9yvFERERERDyW12DafkfEY2p/K61RW14RkUOpxX7iaE4STJoviEhRXSn3rX6ESf0+YF/9IgCO6HMMe2sLKasv8Xh0ibe1Olfzghjl1Q1RzntIme0NdTwREREREUkSdT8R8QddgSSt0RXNIiKH0lXOiaP5SDBpriAiq/dtZHt1f3ZWD+SWsXfw3WN+yYVDrqCbdfN6aAmneUHslPPeU2YnlwpPRERERESSTAUoIv6gAhRpjQpQREQOpYWmxNB8JJg0VxARgCaa+PaKP7Bh/2quHfUJvj3x50zqd6LXw0oKzQtio5z3nvI6eVR4IiIiIiLiERWgiPhD5ESQTgZJc1pUEhE5lBaaEkPzkGDSXEFEdtcW8/3VD3P/6gdxOL4w4euM6TXe62EljeYEsVHOe0tZnRyZXg9ARESSq7YpU5NBERGfiRSfjM90Ho8kdSjvpLMiJ4O0F7NE0/7Q4kfKOvFa5L+/MdlFHo8kdWgeElyaKySGsk6CZGnZGu5esp7JA4+joCoPgGP6nUBexQZqmqo9Hl1iaU4QG+W8t/yc1amSd+p4IiIiIiLiE+p+IuIf6oAiLdFVzSIih1IHlPjTHCS4NE8QSW8NrpF5xcsoqM6hvH4knx3/Zb4/6decOeh8jNQ/56P5QGyU895SVieOCk9ERERERHxE2++I+IsKUKQlKkARETmUFpviS3OQ4NI8QUQA9jdU8u2Vv2VP7S5uGvM5vnn0TziyzzFeDyvhVJAaG+W8t5TViaHCExER6RIzu83MXDu3xqjjx7Zz7JNe/j4iIn4RKUBREYr3lHUCOikkLdPJKkkVyjqJFy02xZ/mIMGleYL/KO8k2TZXbOOe5X/kF+v+Qc9u2XzxiHsZkJUe/y5oPhAbZby3UjGnvcy6zMT8SiIikkaWAT9s5bFzgAuA11p4bDnwQgv3r4rLqKRNdS6T/PpBvtzPUEQOFSk+GZ/pPB5J2lqGsk7Cok8KaV9mifDzXtEiMVqGsk7iKLLYNCa7yOORpI7IHETzj+DR+RdfWYbyTjwwt3gpi0pWMqn/EZTWh/49mDLwbFaWL6G6scrj0SWO5gOxUcZ7KwU/zy/Do6xT4YmIiHSJc24ZoSA7hJnNDX/7UAsPL3PO3ZeYUUmsUnBSJZLSorufqAgleZR10hqdHJLmoq+W0vxKgkRZJ4miBaf40/wjmHT+xR+Ud8FU39TN6yHERb1rYFnZWiCHYT1zuXnMHVQ1VvLqzv8xp+hdmmjyeogJo/lAbJTx3kqVQlEvs05b7YiISEKY2XHA6cAO4FWPhyPtiLR/TcXWciKpStvweE9ZJxFqgS8t0dxKUoGyTuJFW/DEn+YfwaS5gT8p7/yvsLIvhZV9vR5G3BTWFPG15T9nZ/V2Pn7YZ/jmxJ9ydN/jvB5WwmkuEBvlu3dSOaeTkXXqeCIiIony+fDXfzrnGlt4fISZ3QEMAoqBuc65FUkbnbRKV+GIBIu6oHhKWScH0dVJ0hLNrSTglHUSV7riOf40/wgezQ18SXkXEJHik2G993s8kq7LryzgmyseZMrA4/ncuGv49Lj/xw9W/R81TdVeDy2hNBeIjfLdOymc0wnPOhWeiIhI3JlZNnAL0Aj8o5XDLg7fop83E/iUc25bQgcoMVGreJHgiRShqAAl8ZR10hadIJKWpPDJK0lRyjpJJC06xZ/mH8GTKi39g055F0ypVICyoGQFS0rXMKb3CGqbqjGMC4ZcztzimVQ1Vno9vITRXCA2ynfvpFJOJyvrArHVjpldambrzWyTmd3bwuNTzWyJmTWY2Q3NHvuFma02s7Vm9gczUz9yEZHE+xiQA7zunNve7LEq4EfAKcCA8O1c4F3gPGCGmfVO2kh9wu9Zp614RIIlsg2PtuJJKGVdJ/g97+It0gJfbXIlmuZUEiDKuk5It6zrKm3BE3+afwSL5gW+oLzrID9lXapswdPgGthcsY2C6hx6djuBq0feyPcn/Yapgy8mIxhLuZ2meUBslO3eSKGcTkrW+b7jiZl1Ax4kVGFTACw0s5ecc2uiDtsG3Abc0+y5ZwJnAceH75pN6A9qZmJHLSLiX/VN3Siozon18FwzWxT180POuYdieF6kZdffmj/gnNsDfL/Z3e+b2TRC/06fBnwO+H2sgwy6oGWdrtYVCZZ07YKShLxT1nVQ0PIu3nSVkjSnOZV0lbLOf9I967pCVz0nhuYfwZFKV1XHk85j+o9fs66wsm9KdD8B2FiRz1eWPcAXD7+Gj46+jdMHncf/tj/ClsqNXg8tYTQPiF1e3RDlugcSndOp8tkuCGVyU4BNzrk851wd8CRwTfQBzrn88B5DTc2e64CeQHegB5AF7E78kEVEUkaRc25y1K3dD2tmNgk4k9AHj+mxvpFzroEPW3xN7dRogyuQWacuKCLBog4obepQ3inrOi2QeRdvukpJmtN8SpJEWZccyrouUgeUxND8Ixg0J+gyncdMDt9mXap0PwHYVrWTe1f+hV+s+wd9Mvty65gvpHznE1D3k1gp173ho5z27Wc733c8AUYC0S1fCghV1rTLOTfXzN4FdgEG/Mk5tzb+QxQRkSiRysl/OucaO/jcveGv6daiMvBZFz3h0hU6Iv4WXXySbl1Q4khZ1zmBz7t40hXI0pw6oIjPKOs6R1kXJ7ryOTE0/wgGdT9JKuVdx/k+6yLFJ6nQAWVu8VKWlq5hcI+BOJrIsiwmDzyL+cXv03RIXU9q0Bwgdsp1bwQwp5OWdSldHmdmE4CJwChCYXiBmZ3TwnGfN7NFZraoqDg1/6EWEUkGM+sJ3Ao0Av/sxEucHv6aF7dBpbhYsy587IG8Ky9pSNiYfFT5KyLtUBeUjlPWeaMzn+0qSuqSPcxO0ZVK0pzmUuI1ZZ03OpN11aU1yR5m0qkDSmJo/uF/mg8knvIu+Tp7HrO+vKpT75cqHVBqmmrZXr2LguocTh5wBp8YczvfOPrHHN77KK+HllCaA8ROmZ58QcnoZGddEApPdgCjo34eFb4vFtcB85xzFc65CuA14IzmBznnHoq0o8kdFIQ/EhER3/ooMAB4zTm3vaUDzOxkMzvkH1szuxD4SvjHxxI3RF9KeNbBwXnXf2Dim55pKx6R4FDxSYco6zovqZ/t+gzs3uUBJ5MWgKQ5zaPEQ8q6zktq1mUP6NnlAQeFFp8SQ/MP/9NcIKGUd52T9POYWf17dWnAqVB8EvHsjhX8fN3fye7Wmy8f9X0+OfZO+mXleD2shNIcIDbKdGlFUrMuCFUWC4EjzGycmXUHbgReivG524BzzSzTzLKAc4G0bVEpIpIEkZZdbe0p9xtgu5n9z8x+G77NAN4mtLfn95xzcxI9UJ9J+azTwomI/6n7ScyUdZ2X8nkXD5GTRTphJBGaR4kHlHWdp6xLMC0+JYbmHf6meUDCKO86J5BZlyrdTwDmFS/jziU/5unt0zkxZwo3H/b59p+UApT/sdH5BGkmqVmX+Mudu8g512BmdwNvAN2Ah51zq83sfmCRc+4lMzsVeJ5Qxc5VZvZD59wk4BngAmAl4IDXnXMve/ObiIikNjObCJxNaF/P6W0c+h9C1e2nApcBWcBu4GlC+3rOSvBQfSedsi76ZEnA9kEUSRuR4pPxmc7jkfiPsq5r0inv4kX7NUu0AO4jLQGkrOsaZV3ybK3OZUx2kdfDSCmad/hb5HyK5gLxobzrvKBnXaT4ZFjv/cl827ira6rniW2v8s7ueYzMDm1F1C8zhxHZo1m3f6XHo0ucSPGJ5gDty6sbokxPc15kne8LTwCcc9Np9gfinPt+1PcLCbXzav68RuCOjrxXrdPJdhGRznDOrQXavVTeOfdPOreXXEpLZtb5hU6aiPhbXoNpPtyMsq7r0jHv4kELQRKh+ZMkmrKu65R1yaPFp8TQvMPfNBeID+Vd16RC1qVKAcru2mJ21wLkcPeEy7lw6BUsL1vI8wWPU1y31+vhJYzmALFRpqc3L7IuCFvteCbSbjz6JiIiIvEVaR+v1rEi/qM5sIi/qGWuRGjuJCIAPbv15Jh+J3g9DM9p+53E0JzD3zQPEImPVNl+B+Cvm9/kP/kvcHTf4/j2Mb/g8uHXk2XdvR5WQin/Y6NzCZIsgeh44ictnXjXlaAiIiLxoSt3RPxJHQFF/EVXLUmE5k4i6a1vZn/unPANtlZu5rVdz7N631Kvh+QpXf0cf5pz+JvmASLxkSrdTxpcA8/teIv39i7kzsOv4LLhH6Fnt2yeK3jM66EllPI/dtp+RxJNHU/iQJ1RRERE4ktX8Yr4k+a5Iv6iq5YkQvMmkfS0s7qIP218jD6ZffnChHv4+lE/4sg+x3g9LM+pA0r8ab7hb5oHiMRHYWXflOiAUlxXxo/XPs53V/6Ot3e/AsDQHsMZ2mO4xyNLLOV/bJTpkkjqeJIgzU/K6+pQEfGL+qZuKTGBlvQQffJEV/CI+ENQup8o7ySd6GpkAV31nI6UdQIwY89cZu6dz3mDT+Pjh13MgO6hBZcMutFEo8ej85augI4vzTf8LVXnAco68UJhZd/Adz8BWL1vY/i7HO44/GYm9juOd/e8zuu7nqe2qcbTsSXS1upcZX87lOn+kyp5p8KTJIkuRPH7SXoRERE/StWTKCJBFZQCFJF0opNHApoziaSjRtd0oADFORiRDecOmcapA8/m9V3Ps7J8MY70nbOpACW+NN/wt/z6QZoDiMRBqmy/E/Gr9U9yy5hqLhp6JacOPIuXdzzNgpJZKTs/UPbHRlvvSLxpqx0PaDseERGRztM2PCL+onmtiP9oCx4Btd0XSUeNrokmmiiozmFjRTU9Mnpw++Ff4RtH/4STB5xORpqfClb7/fjSXMO/dN5EJH5SZfud8voKHtz0ON9Y/ktK6oq5ZewdnJ17odfDSjhtv9M+nT+QeFLHEw+pC4qIiEjn6WpeEf9Q9xMRf9IVyaL5kkj6mle8jAXFKzhn8GRuOuxiPj3u/7Gw/wc8mv9nr4fmKV0BHV+aa/ib5gEi8ZMqHVA2VuTztWW/48zck9hZNRuACX0mUtdUy7aqPI9HlzjK//ap+4nEgwpPfKL5laI6aS8iIhKb5lfx6ISKiHdUgCLiT9FXL+lEUnrSwpNIemqiiff2LuD9vQs5deBx9MjYBUD/rAGclXsB7+99i4qGfR6P0htagIovFaD4m+YBIvGTCgUoDscHRUuAXkAvvnLkxxjf50iWly3klZ3/o7Bmh9dDTJit1bnK/jYoz6WrVHjiUypEERER6ZzoQhSdVBHxhgpQRPxLJ5LSmxaeRNKTw7GgZEX4pxxOG3gclw3/CBcOvZL5xe/xzp7XKKrd7ekYvaIClPjSPMPf8usHaQ4gEieFlX0DXXwS7Tur/srVIy7k2pEX8K2Jp7CgZBbTdz1HaV1qZqOyv33Kc+ksFZ4EhApRREREOk5FKCLeUgGKiH+pC0p6UwGKSHp7dscK5pXczzUjLuS8IedxVu6FLC2dx7/z/4wjPedtkUUo0EJUPKhdv39pDiASP6nQ/QSgprGWp7dP57Vd73H9qEu4fPg5rN+/mkUlqZ2HKkBpnwpQpKNUeBJQ0YUoOpEvIiLSPhWhiHhHBSgi/qaTSelLi08i6WtH9W7+vPm/PLHtFa4YcR5De2QeKDoZmX0YO6q3eTxC72ghKj40v/A3zQFE4idVClD2N1TySP5zvLzzHUrryhmRDVMHX0yfzH68s3s6NU3VXg8xIZT77VNBqcRKhScpQCfyRUREOkZFKCLe0LxVxN/UBSV9afFJJH2V1u/jsa0vhX/K4dQBfbl34gPkVWzgtV3PsW7/Sk/H5yUtRMWHClD8TXMAkfhJlQKU4royAAqqcxjeczRnD76QcwZfzFuFLzFr71vUu3pvB5ggW6tzlfltUJ5LLDK8HoDET16DHbiJiIhIbPLrBx1UiCIiiac5q4j/5dUNOagQRdKD5kUisqysmr9ufpKc7gO564h7ueeo+5nU7ySvh+WprdW5B23FI52juYW/aQ4gftbYFKzzB4WVfQ8UoQTdrzc8x9eW/YztVVu4btTNfG/Sb5jU70Svh5Uwyvz2Kc+lLSo8SVE6mS8iItIxkZMsOtEikjyas4r4X+Skkk4spRfNi0TSV71r4I3CWdyx6H4e3PQ4vTP78ulxd9OrWx+vh+Y5LUbFh+YV/qbsF78qq8ymrDLb62F0SKoUoORVbufbK//Gd1f+jrK6YqobqwDIsu4YqXlOR5nfPmW5tERb7aS46BP5amkuIhCqEA/aJF0k2bQVj0hyJaL4RHknEn9qrZue1ILfv5R1kkgNrpG3d8/h3T3zGNNrJHVNFQDcMuYOVpUvZXnZQhzpea5RW/DEh+YV/uWn7FfWSXOR/x5yeld7PJLYpcoWPKv3beSry38HwKhsuHbkTYzrcwQv73yatftWdPp1DSPDMmh0jXEaafwo89umLI+fVMk7FZ6kERWhiIiIdJyKUERERA4WfWWTTjClDz8tQolI8jS6JvIqtwM59M/qy5jeh3PaoKnsqi7gjcIXWFI6TwUoWozqEi1a+ZeyX/xMBSjeKqjOYWFpAZP6n8gXJ3yTjfvX8MKOJ9hWldfh1xracwS3jvkCW6vyeGnHk9Q0+e/vdGt1rvK+DcpyidBWO2lKbc1FREQ6Ti3nRUREDqZ2+elH8yGR9FVev5+7Fj/Ar9b/E4fjtnF3851jfsGQHsO9Hpqn1I4/PjSn8C9lv/hZULfgSQWzixbzhcU/5qHNTzG050i+fvSPmDr44g6/TkNTPTuqt3HKgDN44Pi/cv2oW+mZ4b+/U+V9+5Tloo4naU5dUERERDpOXVBEREQOpi4o6UdXQYukpyYcHxQtYU7RUk4bdAJXDZ9CSV3oCuChPUdQVLvbl63yk0EdUOJDV037V379IOW++FbQOqCkSveTBtfIa4Xv8+7e+Vw38mLWlC8DoH/WAGoaq6ltqmn3NYrq9vD8jsdZUjqPEwdMYcrAczhn8EXM2vs2r+58xncdUJT37VOWpy91PJEDIl1Q1AlFRDrLzC40s+fNrNDMas1sp5m9YWaXt3DsmWY23cxKzKzazFaY2ZfNrJsXYxfprMiVP7r6RyQ9KOtE2qernNKL5kCpR1knsXA45hUv4zurHiK/qjcZdOOLh3+T70/6DVMHX0yWZXk9RM/oiuj40HzCn1Lp/IfyLjUFrQNKYWXflOiAUtNYyxPbXmF5eT0F1TncdNjn+N6kX3HawKkY7a85VjdWsW7/Sl7c8QT/zn+QOUXvcvqgc/nJ8X/mIyNvoUdGzyT8Fh2jvG+fctx7yc46FZ5Ii6KLUFSIIiKxMLNfAG8Dk4GXgF8DrwKDgfOaHXsN8D4wFXge+BPQHfgt8GTSBi0SZ6l0AkZEDqWsE+mYyIKRTjalPs2BUoeyTjprW3Vf/rDpCUrrivjo6Nv4wbG/5YIhl9M9o4fXQ/OMFqTiQ3MJfwp67ivvUp8KULz1aP5blNQVc8vYO7jn6B9xeO+jYnpedWMVa/Yt58UdT7K8bCGZlsm5Qy7hlAFnJHjEnaesb5ty3DteZJ222pGYaEseEWmLmd0OfB34N/B551xds8ezor7vB/wdaATOc84tCt//PeAd4AYzu9E5pw9uElhqPS+SepR1Il2jVrvpQS34g01ZJ121vGwdy8vWManfBG4ZcxHXjbqZPbWFrCpf4vXQPKWW/PGRVzdE8wifCWruK+/Si7bg8caGiny+uuy3nJM7mU+Pu5ovH/V9Hs3/CwtLZrf73CzL4vRB53LqwLMpry/jvb1vMKf43fBj3Tl90FQWlc6hurEq0b9GzJT17VOOJ5dXWaeOJ9Jh6oYiItHMrAfwE2AbLQQYgHOuPurHGwhVVD4ZCbDwMTXAd8M/3pm4EYskj67+FUkNyrpgqm3K1JXGPqSrnVKf5j/BpKyTeFq9bxPfWvlXvrbsZ6wuXwrAhUOu4NJh15HdrZfHo/OO5iVdp3mE/wQt95V36StI3U8gdTqgzCpaxBcW/4j/5L/AyrLF7R6fZVlcPfJGrhl5EwDPFjzKu7unH3j8uJyTuWH0p3jg+L/w0VGfole33gkbe2co69umDE8OL7NOHU+ky9QNRSTtXUwolH4HNJnZFcCxQA2wwDk3t9nxF4S/vt7Ca70PVAFnmlkP51xtYoYsklzRJ2GCeDWQiCjrgk5XH/mPOqCkvqBeBZ3GlHUSd3mV24H+AIzIHs2UQedw/pDLmLnndWbufd1XVyon09bqXM1JukjzCP8JUO4r79JY0LqfQKgAJejdT+qa6nlux1tAD6AHo7LLWjwuJ2sglwy7ljNzz2dH9Tbe3v0yy8sWHng80zK5YMjlVDVWklexgZMGnM7Zgy9idtEMXt35DFWNFUn5fWKhcxCtU4YnhWdZp8ITiavmHVBUiCKSFk4Nf60BlhIKsAPM7H3gBufc3vBdkQ0dNzR/Iedcg5ltASYB44G1CRmxiIe0DY9IICnrUoRO/viPTjqlNs17AkVZJwn1wLonGdd7Np8aexGXj7ie84ZcylPbH2ZJ6Tyvh+YJzUniQ237/SUgxSfKOwlcAUqqbL8TUVCdc0jxyaDug/nY6E9zdL/j2FtbyONbH2JH9VYADMPhOD5nMqN7jeO9vW/wxq4XGNVrDCfmnMbJA07n7NwLmbH7VabveoYG1+DBb9Uy5X3rlOEJ5VnWaasdSShtxyMSeLlmtijq9vkWjon0R/s64IBzgL7A8cCbwFTgf1HH9w9/LW/lPSP353Rl4CJ+F7R2tCIprr28U9almEj7W7XA9Q+1zk9tmvP4grJOPLelsoD7Vj/CV5Y9wMaKNeytKQSgb2b/tN2CR/ORrtMcwl88Pteh85jSIWWV2YHagidVtt+BUPFJQXUOAKOyx3LTYZ/j6H7Hsap8KY/l/+1A0QmAwx3odlJeX8bq8qVUNlawfv9qXtr5JP/e8iAfFL3DkJ7D6JPZz6PfqG3K+pYpwzvNt5/t1PFEkiKvwdT9RMQnmhozqK7oEevhRc65ye0cEylibACuds7lh39eaWbXAeuBc83sjBZaeImkPV0JLJIYcc47ZV0K0xVI/qIOKKlLc574U9ZJUOWHC1AARmXD1SM/zgk5p/Lenjd4d89rVDVWejtAD2g+0nWaQ6QmnceUZFAHFO/0zjyJW8d+kqE9B7F+/2oe3/q3FucBR/U9jtG9xlHZsJ/BPYZR0VDB7pqdVDdWsW7/SrZUbiS7Wy/K6ks8+C1io6xvnbqfpM5nO3U8kaRR9xORlFUW/ro0KsAAcM5VAW+Ef5wS/hqpjuxPyyL3l7XyuEhKilwVpCuCRXypLPxVWZfCdMWxv+jKp9SluY5vlYW/KuskqQqqc3hi22zW7VvJpcOv475jf88Vwz9Kr269vR6aJzQf6TrNH6QdZeGvyjs5hDqgJNcZg07i7gm3MKxnLi/vfJdfr3+SqsZKjEPXEc3gjcIX2FG9jY+Ovo3bxt3FKQPOOHBsbVONr4tOoinrW6b8jquy8NekZ50KTyTpVIAiknLWh7+WtfJ4afhrZNYeOf7I5geaWSYwjlAlZl6cxicSOCpAEfEdZV0a0TY8/qIClNSkuY4vKevEM9uqdnL/mkf5v6U/Ye2+5UwbdjWXDf+I18PylOYiXaP5g7RBeSftClLxCQSzAOXqERfwf0d8kgHd+/Hqrpk8mv8CxXVlFFTn4Dh094RV5UuZvutZHtz0M3634X6qGiq58bDPcv6QywBaLFbxO2X9oZTfceNZ1qnwRDyj4hORlDGD0D5xx5hZS7lybPjrlvDXd8JfL23h2KlAL2COc642rqMUCSAtyoj4hrIuTelEkH/oBFRq0jzHV5R14rltVTv50Zr/8OWlP+Wt3S8DcHjvo7hh1CcZ1H2wx6PzhuYiXaP5g7RAeScxCVr3EwhOAUoGGQzrmUtWRiaPbX2J/257BYc7UDxSUJ1DQXVOq8/fUrmR/259iP0N+5g27Gpyuw9psVglKJTzh1J2d5lnWafCE/FUpPuJilBEgss5txV4GTgM+L/ox8xsGnAJocrK18N3PwMUATea2eSoY3sCPw7/+JfEjlokWFSAIuItZZ1o0cc/tICUejTP8QdlnfjJ9updrNkXWnjqkzWRs3Iv5PuTfsNnxn2JMb0O93p4ntBcpGs0d5AI5Z10lApQ4q+JJh7Ke5pPL/gWrxfOoqYxtJbdvHgkUoDSvJuJYeyu3cWiktlkd+vNqYPOTtrYE0U5fyhld+d5mXWZXRm4SDw1Lz4ZnxncCkWRNHQXcBLwGzO7AlhKqP3WtUAj8DnnXDmAc26fmd1OKMxmmtmTQAlwNXBU+P6nkv4biARA9KLM2KxiD0cikpaUdXLQiaAx2UUejkQiJ6HGd9/j8UgkXiLzHM1xPKWsC5j6pm4UVvZlWO/9Xg8lYV7dNZM5xUu5Yvi5XDbsbE4acBpLSxfw8Jbfez00T0TmIpqHdJzmDhJFeScdFik+yeld7fFIYhcpPvHrPGFfQ0Wbjx/b/whWlW9ke3V/AEZllwEc6I7S4BoAaGwKfTUs0J1PQDnfnLK7SzzJOhWeiG+pEEUkOJxzBWZ2CvB9QmE0FdhHqKryAefcgmbHv2Bm5wLfAa4HegKbgK8Cf3DO6f/wIu3Q4oxIcinrpDmdEPKH6KugdDIqNWiO4x1lXXD5fWGpq0rrynls60s8U/AGFw05g96Zod8zgwxOGXgmS0vnHVh8Sheah3SeFrFEeSddoQKU5BjbexR3TbiFuUVLeW7Hm1Q0VFFQnUMGGYzILqFXt970zxoAQCNNwKEdU4JMOX8wZXfHeZV1KjyRwIguRFERioj/OOf2Av8vfIvl+A+AyxM6KJE0oC4oIsmjrJOWqAuKf+hkVGpRAYo3lHXBFsSFpY6oaazllV0zwz/lcMnQsXxy7J1cM/JGZu55nQ+K3qG6scrLISadFqY6T/OG9Ka8k65SAUpi1TTWsHF/PpcMO5uc7v14b+8C1u/bQk1TLXtqB3Px0LM4IedUSuuK2bB/tdfDTRjl/MHy6oYotzvAi6xT4YkEkrqhiIiIHEoLNCIi3lIRij9oISm1aH4j0nFBWljqijd257Or5g/ceNj5XDPyJqYNu5YPimYwfeez1Ls6r4eXVFqY6jzNG0T8r6kxg+qKHmT3qfV6KIdQAUpiFNYU8fe8pykeNY3Lhk3lpJyJbK7YTml9OYN7DOS4/kdS2VDNnOJ32V61BQhttdPNMmlw9R6PPv62Vucq48OU2/6mwhNJCeqGIiIi8iF1QRER8Z4WgLynbXhSiwpQRDousrAE/l5c6ooV5etZsXI943qP4rqRFzOp34m8tONJALK79VIHFImZFrJE/K+6ogeAClDipLCyr6/nB/sbKvl3/vO8unMmHxt9GSfkHM3RmePIysgiv3IHzxa8yZLS1dQ05TAqu4yTB5zONSNv4rVdzzG/+H2awlvwpApl/MHU/cSfVHgiKUdFKCLtaDKo0D//IulCizSStpR34hPqguIPWkxKHZrbRFHWSQcE4ermrthSWcBvNvyLTMukwfVnfO9qvnfMr9lSuZG3d7/ClsoNXg8xqTT/6DwVrvqMsk5aoAKU+AnC/KCorpQ/b/4vfTN7M7jHQPbVV1Dv6imvrzhwTEF1Dr0zazi3vpRPjLmdC4dewas7n2FZ2QIcqbVOqAKUD6XU5/wUybsMrwcQCzO71MzWm9kmM7u3hcenmtkSM2swsxuaPXaYmb1pZmvNbI2ZjU3awMVzeQ124CYi4mfKOkm0/PpBB24iIl5Q1oVOEEUvBEny5dUNOWhBSYJL8xrxK7/nXWFl34M6oaSaBtcAwM6aHF7dNYvD+xzJV4/6AV858gcc2/9kjPQ7R6j5R+dp3iDSMr9kXXVFjwNFKH5TVpl9oAglCIIwP9jfUEle5XaK6koPKjqJWL9/C19d9lseWPs3Gl0jnxn/JT497v95MNLkUL5/SHntH74vnTGzbsCDwMVAAbDQzF5yzq2JOmwbcBtwTwsv8SjwE+fcW2bWB1Kst5LETJ1QRMSvlHWSbNqKR0SSTVl3MF2F7D1dzZw61AFF/CRIeReEK5y7oqaxlqe2T+eFHW9z4dAz+MjI87nj8K/xy3XfY1tVntfD84SukO68lLqiWqSL/Jh16oASP6kwP1hQsoJFJSs5Z/BkemaEMi/LujOq19iU7IC2tTpX2R6m7Xe85/vCE2AKsMk5lwdgZk8C1wAHQsw5lx9+7KCAMrNjgEzn3Fvh4w4tgZO0pCIUEfEZZZ14pvmVwlq0EZEEUda1QotA3tNiUmpQAYr4RODyLvrq5iAvMrWmtqmO6bve4/Vdszgx52j21IaKTi4ffj21jTV8UPQONU3BWAyMF809Ok9zBhHAx1mnApT4CXoBShOO9/YuDP+Uw61jTucjo25ldflSXtzxFLtqtns6vnhTtn9IWe2tmLbaMbMeZnaVmX3WzC42s2T2hxoJRP8LUBC+LxZHAmVm9pyZLTWzX4arMUUOiN6OR1vyiIhHlHXiG9qSR0QSRFnXjkibXLXK9Y7a86YGzWPEY4HOuyC02e+sJppYUraGguocCqpzGN1rHNeO+gT3H/cHrh5xI/2zBng9xKTTvKPzNGeQNOf7rNMWPPGTKnODp7Yv5tH8FxjX+0junfhTPnHY7QzISr3PDMr2DymnvdFu4YmZ5QLLgReAh4DXgRIz+7uZDYs67stmts7MdpnZAjP7iZmNT9TAY5QJnEOondepwHhC7b0OYmafN7NFZraotCTQHZslDlSEIiIBE1PWwcF5V17SkLwRSmCpCEVEfKJTWVddWpO8EcaZThZ5K7KYpBNVwaY5jARQh89j1pdXJWQgqbLI1JbvrfoHX1v2M9buW8GFQ6/gh8f+jikDz/F6WJ7QvKPzNF8Q6bBOfbZr3F/ZqTdTAUr8BH1eUNdUz/M73uKOxffx8s53mTzwLD4x5navh5UwyvYQ5XTyxdLx5LuEqhAN2AAUAT2AzwDzzGy8mX0d+HX4uKHAZOBeYJ2Z/biLY9wBjI76eVT4vlgUAMucc3nOuQZCxTMnNz/IOfeQc26yc27ygIExNYGRNKFuKCKSJAnPOjg47/oPDMJue+InKkIRkS5KetZlD+jZlfH6grqgeE8nqoJP8xdJsqSex8zq36ur421TpAAl6ItNrcmr3M6P1vyHOxffx2u7ZrGlciMAI7MP4+i+x3k8uuTTnKPzNFeQNJP0z3bd+vbuynhVgBInqTAnqGio4pH85/ji4h/y4KbnAeiXlcO0YdfQPcOf/410hbI9RJ/rkyeWKotLAQd8yjk3ERgGXE6oCGU08DihIhMD3gI+D3wPWEqoevFbZvb7LoxxIXCEmY0zs+7AjcBLHXhujpkNDv98AVH7zIl0lIpQRCRBlHUSKCpCEZFOUNZ1kU4YeUsnqoJPcxdJkpTNu1RYbGrNntpi/rnlGZaW1VBQncO5gy/hriPu5VsTf8ZpA6eSael14YbmHJ2juYKkkcBmnQpQ4iMV5gRFdaUUVO+moDqH4/ufwlUjPsYPJv2Gc3Ivplvq7eyrXA9TTideLIUno4EG4DEAF/I6oXZYecAUIAeYA1zqnPuHc+4nzrnJwM1ANXC3mZ3emQGGqx7vBt4A1gJPO+dWm9n9ZnY1gJmdamYFwEeBv5nZ6vBzGwm17JphZisJFcf8vTPjEGlORSgiEi/KOgkyLeKISCyUdfGjLije0jY8wae5iyRSOuRdKiw2tec3G57j9xv+jcNxy9g7uO/Y3zF18MVeDyvpNN/oHM0TJNWlQtapACU+UmVO8OT2xdy74lfsrtnFxw67je8c80tOHtCpJW1fU66HKKcTK5Zy7Qyg1jnnou90zhWZ2S+AvxHqiPKnFo55wszGAD8FbgfmdWaQzrnpwPRm930/6vuFhNp5tfTct4DjO/O+IrFqXnwyPtO1cqSISMuUdRJ00Qs4Y7OKPRyJiPiVsi7+ok8ajcku8nAk6Slysmp89z0ej0Q6IzJ30bxF4i1d8i6y0DSs936PRxJ/Da6RmXsXMHPvAk7ofzQfGz2VPpn9ADCMgd1zKa7b6/Eokycy39Bco2M0T5BUlipZFyk+ye5T6/FIDhUpPsnpXe3xSNoXXXwS1HnB+v1b+MaKP3FSzjF8ZtyVTOp3IktKO7Wk7XvK9RDldGLEUniyAxhnZuOdc3nNHnuBUOEJwIJWnv93QoUnZ3dqhCIBFF2IoiIUERFJN1rIERFJPp088o5OWAWb5i0iXZMKi01tWV6+juXl6zAMRw6XD5vAZ8f/HyvLFzNj93S2VG7weohJo7lG52ieIOJ/0d1P/FaEEqQCFAh+YerSsjV8aelaenTrTm53GJU9hutG3cLLO58iv3KT18OLK+V6SF7dEGV0HMVSePIuMB54APh49APOub1mVgt0B3a29GTnXLGZ7QNGdnGsIoEUKUJRAYr4hTVCZkUsO61JKqt1WTr5IQmnLijiJeWdpCt1QfFOdLteza+CJ4gFKMo68ZugLza1xRE6rzeneA853d/g8uFnc0LOqWyp3MQ7u19lednCA8ekOi1UdY4WtjpHWSfJ5tcuKCpASR6Ho6axloLqHIb1HM2wniP52lE/ZEXZIl7e+TSFNTu8HmJcba3OTftM98M6SarkXSy/wW+AOuAGM3vdzKY0e3wicLNzrqalJ5tZJtCb2IpcRFJWXoMdsiWPiIgfRPY11N6Gkkj59YMOKkQREZHE0x7O3tHcKrg0ZxHpusLKvgd1QkklZfX7+O+2V/jswu/zt81P0rtbH64eeeOBx430OfcXmWdorhE7zQ9EgqO6osdBnVD8oqwy+0ARShAEfU6wqHQVdyz6IY9vfZkj+h7Dtyb+jBtGfdLrYcWd8jxEOd117RaDOOfWmtkdwD+Ai4GLzWw3MB9YBCwGZrTxEjcA3WilI4pIulEHFBHxM12pK4mmLigiIsmnK5O944crp6RzgtgBRcRvUnkbntqmOl4vnMWbhbMZ1GMAPTIcWZbFNyf+lBVli3lv7xuU15d6Pcyk0VyjYzQ/EAkOdUCJjyB3QKlpquWZgtd5o3AWHxk1jW5WfuCxXt16U9VY6eHo4kt5HqIuZZ0XUxcS59y/zWwd8HNgKjAMuAa4OnKMme3gw0KUxeHvTwT+DDjg7XgOXCToorufqAhFRPxIRSiSaFrQERFJLm3D4x3Nq4JL8xWR+AjyglNbmnDsrS0BcsjJ6seO6m1cOPQKLhh6GYtL5vLOnunsqN7m9TCTRgtWHaMCFJHgUAFKfAR5PrC/oZJ/5z8f/imHy4Ydzq1j72TG7ld4d89r1DX567+NrtD2O8rozop5syDn3Hzn3HnAGOA24EFCXU+qAQNGAdcC9wOvAruBN4AcQoUnc8xsaNxGLpJCtA2PpBozu8XMXPj2uWaPnRf1WEu3n3k1bmmdtuORRIq0tFdbewkSZZ3/ZaRRq/vOUCtd72hOFUzpOk9R3km8Bb3lflvK6vfxozX/4c7F9zF91/uckHMq9058gFHZY+L2Hn0y+3Hu4EsY1nNk3F4zETTP6BjNC7ylrAuAJoOKmK6jTzi/b8ETlG14UmE+sKK8gg37V3HliI9y36Tfcv6QS8myLK+HFTfK8pBU+fyerKzr8L/UzrntwKPhG2aWAUwETgZOCX89EegT9bQM4KHw8YXA0sjNOfdcR8cgkqq0DY+kAjMbDfwJqODgLGjuPWBmC/fPTsCwJI50xa4kkq4qliBQ1gXD8OxRfPnI77GgeDZLy+ZT3Vjl9ZB8SVcme0dXUAVPus1TlHeSSEG+4rk9e2qLeXjLszy5bTqnDzqBguqtAFw89CpqGquZXzKr01dF52QNZOrgaXxk1C0UVOUzc+8bLCzx7//VNM+IneYF3lDWBUyk+KRPg7fjwL8dUCBYXVCCPB/YUb2b+1Y/wpF9ZvKZcZfxkVG3clLO6fxmw31eDy2ulOUhQc7pZGZdl0sEnXNNwOrw7T8AZmbAkXxYiHIKcBLQDxgevl0ONMVjDCKpRtvwSFCF//3/F1AMPAfc08bhM51z9yVjXJI4zat9gzjxEn9Kt4UdCQ5lXXCU1VfQq1tfbhrzOW4Y/UlWlS/lrd0vs71qi9dD8yVtw+OdIJ/ASlfpME9R3gVDY5NRVpkdiIWl1kRf7RzERae2VDVW886eeYQagsPR/Y7nyL7HcOWIjzGv+D3e3/smxXV7O/SahTU7eHzr3xjf50iO638Kt4y5g4+Nvo2FJR/wzp7pFNXujv8vEgdatIqd5gXJo6wLMBWgxEQFKMmxoSKfe1f+hUn9jmBUdui/yUzLYvLAM1lYMptG1+jxCONDWR6SVzckUBmd7KxLSNGHc84B68O3/0buN7MJfFiIEilGEZE2qAhFAuZLwAXAeeGvkmZ0gkTiLbqtfSov7kigKOsCoqxuH19c8lMO73MY5w2ewrmDT2FO0bsADMgaRN+s/myryvN4lP6kIhRvaB4VPClegKK8C5AgLSy1JciLTrH45ooHOarvOK4Yfh7nDrmE84Zcyv+2P8Lsohkxv0aDqyevcgN5lRtYWjqfsb0ncFTfYzkxZwpn5V5AfuUm3tkzneVlCxP4m3SeFq1ip3lBUijrgk4FKDEJ0jwhyHOB1fs2snofQA5Xjziam8d8nkuHXcfrhc+zoHg2TagAJVUEbOudpGZdUruNOOc2AZuAp5P5viKpQkUo4mdmNhH4GfB759z7ZtZeiE0ws7sJdcMqBGY55zYmepySHNqORxIhxRd3JACUdcG0uWIbmyu28Uj+czjnaCKHq0dcxMXDrqawZgcLimezqOQDSuv1b0tLdFIp+bTQFDypNkdR3gVXZGEJgrG41JogLzq1Z/3+Lazfv4V/5z/PpcPOYVPFOgBGZY9hVK+xLC6ZQ72rj+m1iuv2Uly3l8Wlc5k27BrOG3wJY3tP4Jh+JxwoPMm0LBpifL1k0vwidpoXJIayLsWoACUmKkBJnpd2rmN71YN8auyl3Dzm80wbdg2v73qehSWzcaTG2t7W6lzluM95kXXa5kYkoFSEIn5iZpmEtlvbBnw7xqfdHL5Fv86zwO3OudL4jlC8pCIUibdUW9yRYFDWBV+jazrw/SP577OxYj+XDDuFq0d+nCtHfJQ1+5bzt82/8nCE/qYuKMmnhabgSYU5ivIudQRpcak1qbwNT3FdGY9vezn8Uw7XjDyTi4ZeyTUjb2JO0TvM2vs2ZfUlMb1WTtZApgw8m96ZfXlnz3Rm7H7lwGOfHHsnuT2G8kbhC77sgqIClNhpXhA/yroUVhG15OlxEYoKUOIjyAUoS8vWsHTZGiYPOJZPjr2UqYMvZkHJLK+HFVfKcf/yKutUeCKSAiJFKCpAkQTINbNFUT8/5Jx7qIXjvk9o+7SznXPtzVj3AvcCrwL5QE9gMvBT4HpgmJlNdS5qdUhShopQJJ5SYXFHfCOWvFPWpZCqxmre2j2Ht3bPYWiPQZw7ZAq53T/867hqxMfYVLGO9ftW0YT+mprTyaXk0kJT8OTXD/Lj/ESf7dKUuqAEw4ObXmPmnk18dPRZXDT0Ki4ceiU/WfN19tbubvF4w3A4JvQ5mkuHXcfgHsNYUjqPl3Y8eeBK6oHdc5nU/0QyLYvPjPsSNY3VzC95n5l7Xqekzl8ZrrlF7DQvaJOyTj7kky4oKkCJjyDPAxaVrmJR6Sr6ZfahXxb07taHOyd8g3f2TGdp6fyU6ICiHE86357HVOGJSApRFxSJSRNkVlj7x4UUOecmt3WAmZ1GqGLy1865ue29oHNuNbA66q4K4HUzmwMsA84CrgJejHWQEkwqQpF4iRSggIpQJEoc805Zl9p21xbz9PbXwj/l0D+rD2flXsC0YddQXl/K4pK5LCiZxY7qbZ6O04/UBSW5NHcKlqQUyOqznXRQkBaYWhPkhaf2rN63kdWrNzKkx8ucPuiEVotOAByOPpl9+ejo2xjWcyQfFL3D27tfxuHIIIMmmpg84EwyLYs3C19kR/U2jut/CpMHnMm5gy9ha1UeL+14kk0Va5P4G7ZPc4vYpU0BirJOukoFKO0K0vwgyPOAfQ0V7GvIYWyvkXTP6MGnx/0/Lhl2LdN3PcuKskUqQEl3KXIeM6O9A0QkmPIa7MBNJFHC7boeBTYA3+vKaznn9gH/Df84tYtDk4DJqxty4CbSFfn1gw4qRBHpKmVd+imvr+C2Bd/lZ2sfIr9yE1MHT+PeiQ9wYs4Ur4fma1urcw/cJPE0bwqOoMxNlHfppawy+8AtqAor+x64pZo9tcW8tPMdCqpzKKjOOegxI3Seb1T2GD5x2O0M6zmSdftW8sz2Rw90MWmiiUzL4tRBZ7Ondhdzi2eyrGwBz+94jH9u+QOv7XqOxqYGjul3fLJ/tQ7RvCI2mg/ETlmX5ioyD96KxyPVFT0O3PwmSHODIM8B8qt2cPeSn/Hr9Q/TzbrxufFf5utH/5gs6+710OJGGe4dr7PO+39lRSThtBWPJFAf4Mjw9zVmLRY6/d3M/g783jn35XZeb2/4a+/4DE+CSFfzSjxoGx6JI2VdGmpwDcwvWc78kuX0zfwfZ+WezKb9KwGYOvhijut/CgtLPmB52UJqm2o8Hq3/6Cqn5Embq51TQADmJsq7NBWkq5xbE+Srn2MRKT4ZlV2Gw5HdrRfXjbqZCX0msrh0Dm8WvkQTjQe23wE4qu8khvQYzqKSD2h0jQBUNOynomIdeRXrmV8yi/qmOoCDnudHmle0T/OBmCnrxDcdUMC/XVCCtE1fUOcATThmFy1mTtESzhk8meP7D6HehXJ5ZPYYdlRv9XiE8aEM94SnWafCE5E0oq14JAFqgX+28tjJhPaQmw2sB9pt6QWcHv6a1/WhSSpQEYp0lbbhkThQ1qW5/Q2VvF44C+gB9GB3TSbnDRnCrWO/wMebPs3KssUsKJnNmn3LvR6q76hdfvJowSk4QnOT7V4PoyXKuzSXSgUoELwFqPYYRkF1DsN6DuaKEVcwoc9Etldt4alt/zpQBBtdPHJk30kAnDjgNPpk9mNTxVqWlS1kb20hDkdp3Ye57Oeik2havGqf5gPtUtbJh6K7n2gbnlYFZX4Q5AKU9/Yu5L29ADmcnJPNN47+Mduq8nhl5/9Yv3+V10OMC2V4UnmadSo8EUlTKkKReHDOVQOfa+kxM7uPUIj92zn3j6j7JzvnFrVw/C3Ax4E64OmEDFgCTSdQpKsCcKWx+JCyTpqbsWcuM/bM5ai+4zh38BSmDj6Jflk5BwpPBvcYyt7a3R6P0n9UhJIcmi9JZynvJCJIVzm3JagLUK1xOHpm9ODWMVdz+qATWVCygmcL3qC2qeaQjiWGsbJ8CUtK5zIyewxn5V7AlSM+xlF9j+XZgsfYVbPd911O2qLFq/ZpPtAyZZ20yiddUFSA0nVBz//l5bX8ZfMT3DT6Eu4+4lts2L+GV3Y+zZbKjV4PLS6U4Ynnddap8EREVIQiyfaMmTUAi4ACoCdwKjAFaADucM7lezc88Tt1QZGuUgGKJIGyLg2s37+F9fu38PCWZ+if1ZfiuhyO7tvId475BWV1JSwtW8Dy0oVsrdoc2IWdRNHJpsTTfEmSRHmX4oKyyNSWoC9ARfTuls2nxl7H6YNOZHvVLv6e9zQldeVADhDahifC4dhUsRaArVV5zCl+l9MHnctNh32Oz47/Er9d/0MqGyuS/0vEmeYT7VMBSlwo63wgq1u35LyRClDaFZS5QVDzv9E18fbuOczcs4Bpw87i46Mv4e4jvs33Vt5NVWOl18OLG2W478Qt61R4IiIHURGKJMFfgIuAs4BcwIAdwCPA75xz6lMvMdOiinSFtuGRBFLWpZEG10hxXRkAWyqz+cumJzl/yCTOG3wpFw29ktK6Yh7L/ysbKtZ4O1AfUheU5NCikySQ8i5NBGWRqS1B3oYngwzunPAJzhh0IqvLN/LEtlcpqSs/qGtJQXUOcHABCoS6nwDMK36Pcb2P4PRB53Jm7gW8tfulZP4KCaXFq/ZpLtAlyjofOHpgLj+99Br+tWIJc3cmYctCFaC0Kyhzg6AWoDS4Bqbveo8Zu0PdViNFJ9ePupW1+1akzFa/W6tzld/+ELesU+GJiLRKRSjSFc65+4D7Wrj/58DPkz0eSX0qQpGuUBcU6QxlnTRX21R3YCueXt2yOXXgsVww5FiK6/YCcGz/kzmm3/EsK1vIpv1raaLJ4xH7h4pQEk9zJeks5Z1EaBsebzTRxMw98+mR0Z1/5z9PQXUhQIsd1Qqqc8jAGJFdesgx26u2cPqgcxnQfSBAoLfbaYkKUNqnApTWKev8bU9lJacMG8m0cUewtngvj6xcwjPrVtHoEvxvmApQ2qUClMSqbapjRfl6IIf+WX04Pmcy5w25lG1VW3iz8AVWlC0OfJYrv5MnGVmnwhMRiYmKUEQkSKIXVkAnVSR26oIiIvFS1VjNe3sX8t7eheF7cjh5wDimDDyHcwZfTEXDflaULWJp6XzW7V/p6Vj9RieeEk8LTyLSValQhBKkLiiLSlexqHRVi49FCkiO6TeBDfu30OAaD3RAMYyR2aV0s240uNDCaVZGdzIt88DPqUbziPZpHiBBs2d/BVP/+g+uOvooPnXqSdxx4qk8vTb0GapnZiY1DQn+96wiainVwyKUSAEK+K8IRQUoiVdeX8Edi+7n3MFT+Njoi/jc+K+wq7qAf275Pbtrdno9vC5TfqcGFZ6ISIepCEVEgkZX+EpnqAuKiMTbSztn8Hrh+5yUcwxn5J7ElIGnM77PkfxkzTcA6JvZn/0N5R6P0j/UBSXxNEcSkXgIymJTW4K8EOVwdM/I4pYxV9PgGnly26us3bcZF/5fQXUOg3sMZGK/4wHYXbMzZYtOomke0T4VoEiQ1DY08Myq1TyzajW5vXvhLFR0Muvm25m/czsPr1jCkt1JWHxXF5Q2BWVOENTcb3CNzNgzl3f3zOfM3JO4asTplNaFzlsO7jGMkrq9NLpGj0fZNSpACTYVnohIl6gIRUSCRgss0lHqgiIi8VTXVM/8kuXML1lOlmWS22MA3QyyLIvvTfoVRbV7WFA8i0Wlc6ho2Of1cH1Di0eJp8UnEekqdUHxTjfrxqKSVVw94gK+cPiNzC9ewZp9myioLqR7RhafOOxKTsg5gbzK7awqX3rgeRP7Hc+++nJ2VG/1cPSJp0WstmkOIEFTVFlFJhlk98jixdVr+djxx3LlhKNZvqeQf61YzKub11PflOBtTVWA0qagzAmCWoDSRBOzixYzu2gxkM3o7Dq+cPg9ZGV0Z8buV5lT9C71rs7rYXaJsjuYVHgiInGjIpRgsCbIqvB6FCL+oC15pKNUhBIcyjsJgnrXwK6avUAO3TOyeGzrK1wydDLXj76Va0fdxJry5Uzf9RwF1fleD9VXVISSWCrSDQ5lnfhZUK54bkuQFqOqG2t4bsebbK7YxjUjL+Qjoy7m8sapNLhG+mT2AmBXzV4ezX+RleWVQA4A3554M8OzR7Fx/xpm7nmdleVLcKTuOT0tYrXNjwUoyjppy/7aWn42833+8MFcrp00kU9OPonfXXQF2/aVsWT3ruQMQtvwtCsIc4KgFp5GbK/uz583P8Mth13MDaM/ybRh1/DununM3juDmib//rnHIl2yO1XyToUnIpIQKkIRkSDSQot0hLbiEZF4qmuqZ/qu95i+6z1GZw/nvCFTuGDIZDIsA4AhPYbTq1tv8qs2eTxSf1ERSmL5cQFKRIIlKFc8tyVIi1HLy9exvHwdw3oO5tzBpzKq1zD21Vewu6aYJaWrKKjefdDxX1/+Oy4aeiZXjTiH2w//KkW1e3i+4DFWlC/26DdIjnRZxOos5b8ETVV9Pf9dtoInlq3glJEjWVIeKjr5wVnn069HTx5esZjVRUn471ldUNoUhAIUCFbhabTlZetYXraOif0O55bDLuSakTdRXLuXpWXzvR5aXCi7g0GFJyKScCpCEZEgUhGKxEpdUEQk3rZX7+I/W1/k8a0v0YQDcvj46Ms4e/CF7K7ZyYKS2Swsnk1pvf7NiaYTUYmjeZGIPzU1Zvh2caklQVlwaktQFqMKa/by1Pbp7R5X2VjNiztn8PLOd5ky6HhuGDWVRtcIQJ/MvvTI6Elx3d5ED9czmju0TQUoEjQOWLRjB5mEivfrqpu45OgjuP6oSSzYVcAjK5bwxpaNNLoEr1GoAKVNQZkPBCXzm1u7bzPfWbWZ8b1HU9+0CoDzh1xKv6wBvLP7VfYHfEvfrdW5ym0fU+GJiCRVpAhFBSgiEiTakkdipS4oIhJPTVGt7v+8eTrLynZxybCTuWrEx7hqxMdYUjqPf235o4cj9Cd1QUksLUKJ+JNfF5dakkpdUIK2GNWaJpqYV7yMecXLwvfkcOfh07ho6FWsKFvEzD2vs7lyvZdDTCgVoLQtr26Icl8C6Zfvz+Iv8+Zzw3GTuPWUk/jzJVfz16UL+Nm895MzAJ8VoIC/5gkqQEmsvMrtQH8AemSM5oIhU5k6+GJm753B27tfYX9DubcD7ALltn+p8EREPKEuKCISZLrqV9qjLigiEm/VjTXM2DOXGXvmMqTHIM4dfCp9MqsOPP6Rkbewet9SNuxfg0Pz6widkEocFaCI+JNfF5daE5RFp9YEaRuejnpq+3z21ffksuFncuKAKWyrzOOdPdNZXDrX66EljOYNrVPuS1BV1NXxyOKlPLpkGeePH8e66tD/v08aOpzrj5rEwysWk1dWmuBB+KMABfxZqBqUuUBQC1AA/rHlf0zf9R43jL6E84ZcytmDL+SZ7Y8yt3im10PrEuW2/6jwREQ8pyIUEQkyFaFIe1SEIiLxtqe2mP8VvB7+KYdhPXM5bdBUzh96GaV1xSwuncvikrkUVOd7OUxfUReUxNFcSMS/glSEoi4o/lNSV87j217ifwWvcd7g07hu1FSOz5l8oPCkd7c+VDZWeDzKxNBCVutUgCJB1eQcMzbnAZBJBscePpSPHn0st0w6kXe35vHoqmW8t30LTYnchqciaknWJ11Q/DQ/CMpcIKh5v7NmD3/Y+B/+t/11bhh1KYU1OwHom9kPMHVAkbhQ4YmI+Iq24hGRINPCi7RHW/GISCIU1hRx24Jvc+rA47l0+EmcP+RSLhp6JQ9u/Bnr9q8kgwyaaPJ6mL6hk1KJo8UoEf/y4wJTa4Ky8NSaVOuCUtdUz5u7Z/PW7g/I7taDgd1hWM+R3Dvxp6woW8LsorfZsH+118NMCBWutk6ZL0H3xPIVvLlxEzefeAI3nngc/7riIyzbvYtrn3s8OQPwSRcUv84PgtAFJagFKLtq9vLHTf8J/5TDV4+8ltMGncsHRTN4u/AV9jWUeTm8LtFnfe+p8EREfEldUEQk6FSEIm1RFxQRibd618Cc4iXMKV5Cn8xenD7oRDZVrAXg0uHXcVz/U1hcOoclpfMoqdNJGNBiUiJpHiTiX0HqggLBWHhqS1AXpVricFQ11lBVnUNlYyYv7XiXi4edzkkDprC7Ziez985gTvG71DX5/7+rztBiVstUgCJBVlxVxR/mzOUv8+Zz8RET6Nk3tGSaYca3zziXlzetY/mewsQOwmcFKOCv+UEQ5gFBLzj977YPqGvqw3lDpnFW7oXMKXqHt3a/zL76Mq+H1mnKbO+o8EREfE9dUEQk6LT4Im1REYpIYtQ3dUupxZ6OqGio4u3dc4A+AKzdV87Rfeu4ZuRNXDPyJvIqNrCgZBYfFL3j7UB9REUoiaMFKRH/8utVzi1RFxR/Ka0r59GtL/DEtlc4M/dkrhlxJleN/BjzS94HoGdGNjVNwft7ioUWs1qmvJcgq29qYvr6DUBoG54jBg3ixonH87kTJrN8TyGPrVrGS5vWUduYwOIQbcPTpiAUoEAwC04La/byp02P8UzB61w/6hLOH3Ix3TN68MS2f3g9tC7bWp2rvE4yFZ6ISGCoC4qIpAIVoUhbVIQikhipttjTUbOKFjGraBFDegzi7NyTOX/ISRzd97gDhScn5JzKxv1rqWqs8Hik/qAFpcTQgpSIf/n1KufWBGXxqTVBXJRqTb1r4L29C3hv7wIGdc+huK47o7Kr+H9Hfocm18isvW+zpHQeDa7e66HGneYLLYs+5yESN02QWWE09EnOmsDG4mLO/stDXDtpIjeffCK/vOBSvnPmudzwwpNsKk3CuRqfdUHx09wgKIWoQcz6wpoiHtz0OP/b/jqNrpHsbjC61zhOHziVt3a/TFl9iddD7BTldXKp8EREAkldUDrPGiFLawoivqAiFGlLpAhFBSido7yT1qRzEcqe2mKe2/EWz+14i+4ZWdQ15XB8/0w+N/7LNLoG1u5byeLSuawsW0xtU43Xw/WcuqAkhgpQ4kdZJ4kQpCKUoCw+tSbV5iTFdWUA7KwewBuFC7ly+FncOvYLXDvyJmYXzWB20YxAt+1vjRa0EktZJ9EyKz68MDXRRSgVdXU8tnQ5jy1dzmmjR3HFcUeRVxZaeL/miInsr6tl5rYtNLkEjsNnBSjgr7lBEApRg1iAsqc2ch4yh2P7H8tZgy/gjNzzmVs8k7cKX1IBSoKkSt6p8EREAk1dUEQkVTS/KkiLMRKhLigiiZNqCz4dUdcUuvJ4RXkDX132AOfkTubcISdxbP+TqGuq4++bf8O6/Ss9HqV/+P0kVRCpAFfE//x4pXNrgrD41JYgLky1pgnH9F3vMX3XexzX/yg+OuosLhl2LSV1Rcwrfg/DcKTeOTwVrIokV6QIJRldUOZvL2D+9gIyMDIwbj9hMscOHsr2feU8vno5T61bSWlNAvNH2/C0KQhzgKDm/OuFs1hcuprrR13CRUPP54xB5zFzz2u8tPMpr4fWafpsn1gqPBGRlKEiFBFJJVqMkZaoC4pI4qRzEcqWygK2VBbwn60vclTfcZydewrbq7YAMGXgORzVdxKLS+eybt8qmmj0eLTe0qJSYqgLioi/+fVK55aoC4q/rCxfz8ry9QzrmUtJXTlDesDUwdM4ZeCZvLfnDZaVzafRpd7cQotaIsmTzC4oETc8+gQXTzicT0w+gXvPmMpXTj2TH8+ZyX9WL0v8m/usC4qf5gUqQEmMvbUl/HXzEzxb8AbXj5pGfdOHf+c5WQPVAUUOkuH1AGJhZpea2Xoz22Rm97bw+FQzW2JmDWZ2QwuP9zOzAjP7U3JGLCJey2uwgwpRRPxOWSdtyasbon2S5SD59YMO3ESCJCh5V1jZ98AtnTgc6/bn8Y8t/2N9RSYF1Tk0uiEc2/9k7pzwDX583J/46OjbGNt7gtdD9YWt1bkHFaJI12nOI6kgKFnXWdUVPQ4qRPGzssrsgwpRgiaV5iKFNUXUNdVTUJ3D1qoGenXrxW3j7uKHk37PJcOupU9mP6+HmBCaK0iq8mvWZVbYQYUoidLQ1MRrGzZy63+f4dKHH+GpFStZV7IXgMP69efaIybSPaNbYgdRkXlwJxSP+HFeEMl/P88BgnjOIVSA8iT/3DKTguocju9/Cj+Y9Fs+Pvoz5GQN9Hp4naasji/fF56YWTfgQeAy4BjgJjM7ptlh24DbgP+28jI/At5P1BhFxL8iBSgqQkksM/u5mc0ws+1mVm1mJWa21Mx+YGYtroqa2ZlmNj18bLWZrTCzL4f/3U8rXmRdbVOmJlQBFFmM0YKMRFMBSnIo67ouqJ/tgnhCKJ5e3DmDTy34Ng+s/RvLyzdy+qBzuW7kzQce75eV493gfCJyokpzq/jRfMcbyrquC2rWdUZkoclvi00tCcICVFtSbS4yu2gxdy7+KT9a/SA7qrdx5YiP8qmxX/R6WAmleYK/KO+6JghZl6wCFIBNxSXcP+Ndlm7cRWZFBtcdeQy/u+gKPrj183zl1DMZ0qt3YgcQKUDxuAjFr/OCIOR/UDN+fkkxb+2ew+mDzuX7k37DDaM+GejzA6mW015lnfflcO2bAmxyzuUBmNmTwDXAmsgBzrn88GNNzZ9sZqcAQ4HXgclJGK+I+FSk+ETb8CTEV4AlwFvAHqA3cDpwH/B5MzvdObc9crCZXQM8C9QATwElwFXAb4GzgI8mc/A+4FnWqV18cGkrHmkuuvhEW/EkhLKu6wL/2S7V2t/HqsE1sKBkBQtKVpDdrScDuvcjA8ju1ov7Jv2OwpoCFpXMYXHpXMrrS70erqfUrje+tAVP0inrui7wWdcZQdyKx89t+NsSxBb9LXE4lpStYUnZGkZmD6VHRncA+mb257ZxdzFr79usKFtEE4f83yTQNE/wDeVd1wQm67zYhufPM+ezPL+QW089kf93yhl88aTTeH7DGr4x843Ev7m24WlVEPI/aBlfXFfGQ3lP8fyOt7hh1CVcOPRCjup3LD9Z8w2vh9ZpKZbTnmRdEApPRgLbo34uAE6L5YlmlgH8GrgFuCj+QxORIIrufqIilLjp55yraX6nmf0E+DbwLeCL4fv6AX8HGoHznHOLwvd/D3gHuMHMbnTOPZmswfuAL7KueVVvikyw0oKKUKS5SBGKClDiSlnXdb7Iu3hJ1yKU6sYaqqtrgByyu/Xk0fwXuWjoSVw36mauGXkTG/ev4fkd/2VH9Vavh+opFffGl+Y6SaOs67rkZl2T/7q7+nGxqSXRVz/7eRGqNUFbnGrLjurd4e9yOKrvOAZ2H8xnx/8fJXVFzN77NvOK32N/wz5PxxhvKbawFUTKu64J5Oe6SBFKogtQHDA7fyuz87cyJieHm086gaasD99z2tgJzCrYSnVDfeIGoQKUVqkAJf721pbwl81P8FzBm+T2GABApmVx8bCr+KDoHfbVl3k7wE5IkZz2JOt8v9VOF30RmO6cK2jrIDP7vJktMrNFpSWpVUUtIm3TNjzx0VKAhT0d/npE1H03AIOBJyMBFvUa3w3/eGfcB5m6Yso6ODjvqktb+yv7kNrGB5O245FokW14tBVP1ynrPNfhz3b15VVJGlrqtcCPVXVjDa/sepcvL/sNdy3+IU9vf40B3XOpb6oDYEKfozlv8KUM6J7ecwnNp+JL85zEUdZ5rsNZ11hR4Yv2+i3xa8v9lgShDX9rUm0Osn7/Fu5YdD8PrP0bRbW7uXrkjdx37O/J7tbL66ElhOYI3lDeeapT5zEbqyrjNoDINjzJ2Ipna1kZP333PX725vtkVmQwKXcID112LQs+9QV+eu7FnDBkWGIHoG14WhWE7A9avu+uLWb1vk0UVOcwsPupXDrsOn446ffcOuZORmWP9Xp4nRLknPYq6/z3qeRQO4DRUT+PCt8XizOAc8zsi0AfoLuZVTjn7o0+yDn3EPAQwDHHd1f7A5E0pC4oCXNV+OuKqPsuCH99vYXj3weqgDPNrIdzzj/l0ImV8KyDg/NuyDGDOvQfurqhBFPzRRldIZze1AUlYZR1sUvqZ7s+Rw73ZFKXrp1Qdtbs4ant03lq+/TwPTlcM/IkLhp6JdePvpVtlXksL1vI8vJF7K7Z6elYvaIuKPGlbXiSSlkXu6RmXY8xoz/MuuhFJY+vcG4uKFvxqAuKPzThDmzxNzJ7KMf0O5zqxlBB8cdHf4aiuj3ML36PioZg/57RUuTK6lSgvItN0s9j9hwxOiGf7ZLVBSVifX4RH/vvk3zsuGO59uhj+MQxJ7CueC93vfkym8tKEvvm6oLSoiBkfxDzfUnZGr64+D6uGHEeFw89gymDzmbj/jU8tPk31DT588+5LSmW0wnNuiAUniwEjjCzcYTC60bgE7E80Tl3c+R7M7sNmNxSgImIRIsUoagApePM7B5CHxr6E9qj82xCAfazqMOOCn/d0Pz5zrkGM9sCTALGA2sTOmD/CFzWaeEkmNSmXoCDup+oCKXjlHVdEri866p0LUKJeHDTazxXsIjTBp3A1NxjuWrkxzl90Lncv+ZrAAzIGkRpfXr+O6S5VPxofhN/yrou8UfWqQily4KwENWaVJp/7KjeHd6KJ4dM68aQnsM5e/CFXDH8BpaWzmPW3rfJr9rk9TDjRvOD5FLedZo/si6OorufJLoIZcmOnSzZsZMfvzOTK44+iiuOPYqdFaF/q887bBz1jY3M2bGNhI3CJ3MEP84H/L4NT9DyfXdtMQ9veZYnt03noqFnclLOqANFJxP6HM22qi3UNfnj7z5WQSxASXbW+b7wJPwL3Q28AXQDHnbOrTaz+4FFzrmXzOxU4HlgAHCVmf3QOTfJw2GLSApQF5ROuQcYGvXz68Btzrm9Uff1D38tb+U1IvfnxHdo/hX0rFM3lGDSIo2AuqB0krKuk4Ked10VtJNE8bKrZi8v7HibF3a8zcDu/RncYyAAGXTjmxN/QnVjNUtK57KoZC67ara382qpSYtM8aMuKHGjrOskX2adTxaYWuLHRaeW+H0hqi2pNP9ocI18c8WDjMoexqXDzuGCoacxZdA5PLH1H8wpftfr4cVdEBe3Akh51wleZJ01QVZF6Pv6Pl3+FdqUrC4oFXV1PLViJU+tWBl6XzK46+TTOHX4KLbvK+eptSt5Zv0qCisrEjgIdUFpSRCKT4PUBaWqsZqXds7gpZ0AORzRp447J3yDhqYGPiiawft736KsPsEdf+IsYNvvJDXrfF94AuCcmw5Mb3bf96O+X0ionVdbr/EI8Eh771XnMsmvH6ST7yJykFQqQslogh77m2I9PNfMFkX9/FC4zWGLnHPDAMxsKHAmoarJpWZ2pXNuSWfHnA6SmXWJpkKU4NGWPJKKXVASlXfKuq5Jpbzriub7NAfhZFE8lNSVU1JXTuiq5Uz+teVFLhx6PBcOvZJpw65hV3UBL+z4L2v2Lfd6qJ5REUp8pEsBirLOn3yddQEoQvHLolNLgrAQ1ZYgLVK1paC6kH9s+R+PbXuJcwefytbKxQAc338yE/oczayit9lbW+jxKOMn3QtQdB7Tn7zMumQXoEDytuL51BPPMu2ICdxw0rHcc9rZfOXUM/nz0vn8esEHiX1jn8wP/DgX8HvxaRCzfWNFd76/6o98fPQ5XDj0Si4YejlLSxcwfdcz7K3d7fXwPJMqn+0CUXjiheiT75A6J+BFpOtSqQglBkXOuckdfZJzbjfwvJktIdSe61Hg2PDDkerI/i09N+r+so6+r/iTFlCCR91Q0lsqFqHEoMN5p6yTeEqlq5Fj1eAamLFnLjP2zKV/Vh9OH3QSFw45gZrG0Am9kdljOKrvJJaWzk/77Xg0f+o8zWkOoqyTg/lkkam5oHVBAf8uRrUmVeYdNY21vFE4G8gAcjh90ASmDrmY84dextp9K5m99y1WlS+liZgXcXxN51ZiovOYaSQrqhFIqnRBqWts5JV163ll3XpG9+/PR487lqWluwAYnN2LT0w6gafWrkz5Lih+nAsEpQAFgpHt6/dv4f41Wxjc4xWuHH4eFw09ndcLnwegV7feVDdW4RK34VQq8O1nOxWexKh5IQqk1Yl4EWlFpAglDQpQOsU5t9XM1gAnmlmuc64IWE9oL7kjgcXRx5tZJjAOaADykj1eSTx1QwkeLdikN23F0z5lncRb0E4YxUN5fQVvFM7ijcJZ4XtyuHDocVw78iauG3UzmyvWs7hkLsvK5rO/YV9CxpBpmTS6Rl+e3NJCU3ykSxeURFDWpQEVoXSJilD84ZmC13l79xwuGnomlw8/k9sP/yprypfzl82/8Hpocafi1MRQ3gVXsopQktkFZXt5Ob+ZHep0kkkGZxxxGF859Sy+dMoZzNi6mf+uWcH72/Npcgkah0/mBn7rghKEzA9SF5S9tSX8K/85Ht/2MnVN9YzKho8f9hlGZY/lzcIXWVjyAU00ej3MlJLorFPhSReoGEVEItKsC0pHjQh/jcwQ3gFuBi4Fnmh27FSgF/C+c84fs0lJKBWiBIu25ElfadoFpSOUdZIQqbQY1FH/zp/NG4XrOCv3FC4YchIfO+w2Lh9xPd9Z8UWaaCKDjLhewXx4n6P51Ni72FtbyMKS2SwrW0hFgopcukJFKF2nApROU9alC58sNDWnIpTEC9JCVWvK6vfxTMHrPFfwJqcOPI4B3UNZ3j2jBx8d/SnmFs0kr3KDx6OMHxWgJITyLuBSdSue6Us3sDLvn3z8+OO4/vhJTBt3BFvLy5j21CPUNiY4r9UFpUVB6YIShFyva6oHoKA6h7cKV/CJMUO5ZewdXDr8Ot4sfJEFJbNodCpAiaOEZZ0KT+JMxSgikm5FKGZ2JLDbOVfe7P4M4EfAEGCOc640/NAzwM+BG83sj865ReHjewI/Dh/zl6QMXnxHiynBom4o6Skdu6Ao68QP0rEIpbCmiGcL3uDZgjc4rNdwhvcccqDY5JsTf0px7R4Wl85jZfli6pq6dvKxrL6ERSUfMLb3BK4bdQsfHX0be2p3sax0IYtL51BYsyMev1Jcad7UNZrHHExZJy3ywUJTS/y4+NQSvy9ItSYV5hxNNDG/ZHn4pxyO6juO4/tP5vRB57Kjaiuzit5mYckHXZ4/+IUKUGKnvEsfqbgVz/bycn41aza//2AOF004nAkjcw8UnXzxpCms2LubDwq2Jq6Ho0+KU/3aBcWveR+kAhSAeSXLmVeynMkDjuWWMdP4xJjb6ZeVwxuFL3g9tMDwMutUeJIEzYtR0ukkvUi6S5MilMuBB8xsNrAFKAaGAucC44FC4PbIwc65fWZ2O6Ewm2lmTwIlwNXAUeH7n0rqbyC+pG4owaLFm/STZl1QlHUB1NhklFVm+/bkT1ekwoJQR22r2sW2ql1ADlmWyYKS9Zw7+CSOyzmFuqZaVpUv5d09r5FfualTr7+7Zicv7HiC/t0HMLTHcA7rNY7xfY7knMEXMW3Y1VQ07GNJ6TyeLfhPfH+xOFERSteoCwqgrJO2+GShqSVBKEJRFxTvrd+/hc8s/C7nDJ7M1SPO4sbDPss1I2/ix2u+zr76Mq+HFzcqQImJ8i4NpVoXlPqmJl7bsBE2bCSTDHplZfHp409hcK/e5JeX8uSalfxv/SqKq6sSNgY/FKf6tQAF/Jn3QTuPsKh0FYtKV3FyzjFUNa4CYEKfiQzvOYq5xTNpcPUej9DXPMs6FZ54QIUoIukpUoSSggUobwMTgLOBk4AcoBLYAPwH+INzriT6Cc65F8zsXOA7wPVAT2AT8NXw8Sn3hyRdpwWV4NCWPOknDbqgKOsCzO8nf7oqaCeP4qHeNfDv/Od5NP8Fju47nrMHn8I5uSeytHQ+AP2ychjWYwQbK9biOnC9XxONlNYVUVpXxIb9q7E9xhmDzuOioVcxoPsgemT0PHDsYb3GM6TncFaULfLd1dJadOq8NC9AUdYFUO+sLM47bByzC7bS0BS/7cfaFIAiFL8sQLUkqPOSVJhv1DbV8fbuOby9ew5H9hnLSQOOOVB0csGQyymu28PKsiVx3crPK5oLtEl5l8ZSsQsKQFV9Pef+9R9MO2ICN51yPPeeMZWvTjmLu998mTfzO1eYHzMfzAv8WISqLijxs6RsDZAB5PDRUVOYOmQalwy7hrd2v8yconeoVwFKSzzLOhWe+IAKUUTSS6p1QXHOrQLu7sTzPiBUeSnSYSpCCRZ1Q0kfqdoFRVmXOoK62BOr6EUhCMZJpK5wONbu38za/Zv5Z94zmEGjy+HTQ6dy9ciPs6++jGVlC1laOp/NFes6WITSBA6O6nssA7oP4oOid3iz8MUDj58/5DJOHnA6boxjZ/U2lpbOZ0npPIrr9ibiV+0UzZc6Lx3nLsq6YBqU3YtHrrie8toa3s7fzGt5G5i1PZ/axsb2nxwPPlhsaokfF6Ba4vdFqdYEabGqNRsq8tlQkQ/kkEEGZ+aez9CeIyivK2V+yfvMLX6PotrdXg+zyzQXOJTyTiJSrQtKXWMjr6xbzyvr1nP4wIHceMJxLNm9E4CLxh7OcYOH8dz61WzdV5awMagLyqH8nvVBy/TfbnyRt/as4ZNjpnHD6E8ybdjVvLzzaeYVv+f10HzFy6xT4YkPNS9EgdQ6cS8iH0q1IhQRL2hLnmBJx4WcdJUGXVAk4KKLUMC/J4K6IhWuTo5VpFAE4PFtc1m/v4Kzck/i9EFTmTr4Ykrqirh/9VdpdLEtxnbP6MH5Qy7j+JzJFNXu5uWdT1HdWHXgsbG9J7C3tpA15csZkT2aacOu4coRH2NffRlzi2fy7p7XDhzvB7ryufPSvAuK+FxB2T4+/9wLXHLkEVw4YTzXHzWJBbsK+NgLTwKQmZGR9p1QglCEEtTC2FSZZzTRxBcXP8Dkgcdy5YhTuWjoVUwbdg1Pb/sXs4re9np4caO5gASZJbCeMhW7oGwuKeEn74YW4jPJ4KShw7nzpNP4v8lnsHBXAc+uX80rm9ZTUV+XmAH4YE7gt/z3e9YHqQBlVflGvrFiI8f0m8CtYy6ie0bo77qbdSPLulPT5L8/33SiwpOAUFcUkdSnIhSR+NAVPcGhLXnSQ6p2QZHU4/cTQV2VKotDsahrqmdO8RLmFC+hR0Z3ThlwLMOzB8dcdAJwUs5pXD78egprdvDSjlDRiWE4HKOzxzKwey4z97zO8zseJydrIEN6Dmdk9mgm9JnIyOzD6JHR01eFJxGaJ3WeClDEj5xzvLM5j3c255GZkcHph40mw0LnFrIzs5hz6+dZuKuA1/I2MiN/M/vqkrTw4oMFp5b4bRGqJUGdjwR9ntFEEwtKVrCgZAUDu/fn/CGnk1exCoAJfSZyYs6pzC2eyY7qbR6PtOtUgCJBlYwCkWQVoSSrC0rEb2fM4b/zV3DNMRO57rhj+Nl5l/Dxicdx3XP/Tfh7qwvKofzcBSVIeb5m3ya+tTK0ldSobJgy8ByuHfkJZu55nZl7X/fl5/F0oMKTgGqpKwroZL5IqlARikh8qBtKsKgbSupTFxQJilTvhhKkk0ldVdtUx5ziJeGfcg7cPyq7rNXnHN33OKYNuwaA6bueZfW+pQAHtuo5pv8JOJrYUb0VgLL6EsrqS9iwfzXzit+nZ0Y2ZfUlLb+4j6gIpXNUgCJ+1dDUxOz80L9LmWTQN7s7L65ey7SjJjBt3BHUNzYyZ8c2frVgNiv3JnELERWhdFrQi1CCOscoqSvn2YI3wj/lcHzOkZyZez7nDrmEbZV5zCmeyeKSOYG/oloFKBJkKkLpuN0VFTy0YCEPLVjI8cOG0bt7FgC9MrN45rqbeHXzep5dv5rCyop2XqmTfDAf8Fv2+z3ng5TnBdU5LCzZy6T+a7h8xPWcP/Qy3t/7Ju/ufo3KxgT9Ny0tUuFJitE2PSKpJ7oIJR6swdG9PEn7PYv4jApRgkNFKKktGV1QlHcST6lciBJdhALBOKkUDwXVOQe+jy5C6ZeVw/Wjb2VQj8G8UfgCK8oWHfS8DDI4tv/JGBmM7jWO2qZaSuqK2FW9nQbXQHVjVSCvrFIRSsf5Ya6irJO2lFRX8+N3ZvKTd2Zy/PBhXHLkEVxy1BE0hrfeOXnocI4bPIzXt2xkd6IWmZrzwaJTS/y2ENUSvy9OtSRVCl2n73qP9/cuZOrgU7l8+OnceNhnuGDIZfxozT1eDy0u/D4HUNZJe5JZhJKsrXggsUUoKwoLQ+9HBsNy+lBaU83XTzuHr005m1nb83l63Sre2rKJuqYE/X/PB/MBdUGJXVAKUPKrdnDf6kcY0+tNPjn2Qi4eejUT+kzkdxvu93poMUmVvFPhSRpQMYqIiEjLVIgSDH5Y2JHEURcUCaIgLv7EKlUWiTpiR/UAHI5B3XP4+OgLGNJjOEtL5/P6rucPdDmJ6JeVQ3HtHvbXl3PaoHOZOngae2p3MXPP63xQ9I5Hv0F8+X0Byo/UBUX8zAHLdxWyfFchv3hvFhBaaLrwtMO56+TT+eE5F7KkcCev5W3gtbyNFOwvT87AfLDo1BIVoSRG0OcXFQ1VTN/1HtN3vceEPocxsHsOECpIveuIb7GmfBkLSmaxv2GftwPtInVBkaBLdBFKsrqgwIdFKIneimdbWTmfeuJZRvXvx/XHTuK64ybx4LSruOzpf7O2eC89umVS25jAnPZ4Kx6/5b6fMz4oWb61aic/WvMfRmW/RXa3ngBkd+vFJcOu5d09r1FeX+rxCFObCk/SlLbqEREROZQKUfxPRSipKzQ/3e71MEQ6zM8nhroqKCeWuipSXHL9qGmcmXsOK8s38J+t79BEE4YdVHxSVl/Ck9sfZl99GYZxZN9JTBt2DR8b/WnG9z6K/2z9i1e/RkJoAapjVIAiQfLbGXN4aek6ph05gUuOPoLvnHkenzn+FM74z98A6Ne9B/vqkrQA4/MiFD8sRLUmiPOQoFw53ZpNFduAbUAOA7v3p5t149pRn+CqkR9jZdkS5hbPZO2+FYcUrwaJ8l9SQaK7lKTaVjwF5fv4/Qdz+cMHczlp5Ag27iiGPvDTcy/m6IG5PL1uFS9uXEtZbU1c3/ezx5/ClOGjeG7DGt7O30Sjc54Xofgl99UFpWsKqgvD3+UwZeDxnDfkUqYOnsa84pm8tfsVSuuUcYmgwhM5SPOCFBWiiIhIOlMhir+pCEVE/CZdtuTx88mlzsjAOG3QiVwy7ByqG2v5e97T7KjeDeQcOCZ6O5599aHvHY71+1dRUlfEbWPv4pSBp7OqfAlLy+YndfzJoC4oHaM5igTF5pIS/jJvAX+Zt4DR/fszsn8/ADLMeOvGT7OnqoJXN23g1c3r2Z7GnVD8djV0a/y8QNWSVJhblNSVc8/yPzAqeygXDj2TC4ecyokDpvDHjT9lw/7VXg+vy1SAIqkgmVvxJPI9IDldUBywZMfO8PtlsGjLDo4YMIgfnnMh3z7zXN7aspnHVy9j7s74XDh06fgjOGXYSC4eN4HGpibm7dzOs+tX81reBmqzvclcv+W+n/M9CAUoAAtKVvDFxT/kI6OmceGQ8zkz93zmF8/iqW0P00ST18NLKSo8kTapEEVERORDKkTxr+gFHtAij4j4QxCvQo5F9EIR+P8kU3tOzJnIbWM/wr76Cp7aPp0d1bsP6XRSUJ0DHFyAErG3tpDlZQsZ1Wssh/Uez9Ky+Yc8P5WoCKVj1AVFgmJ7eTnby8vJJIPu3brx8ILFXHbMkdx7xlTuPWMqy/cU8psFs3lve37yBqUilE4J4vwj6EUoBdW7+Xf+8zy+9SVOHjCJXdVrALhqxMcZ0mMY7+99i40VazweZecp+yVVJLMIJRW6oAD8b+Uq/rdyFUcPzuX6Y4/lmklHs6tiP3N3bseA0f36s21f54pTD+vXn2Nyh7C2aA+/WzSHKcNHc8bI0fzqgsv+P3t3HR7XeaZ//PuOmGWZmdmOOXbsxImTOA1D0zZNU0jbLXfbLWy6Zdp2+ytsYYtJSmnTMIMDThxjzIxiBosZZ97fH2dky4okC2Y0M9L9ua65JM2cOfOK5jlwn+fl51ffwP6iAj75yrNOBzZ1QQnq+h4Kdfxsczl/zHiEJ/I2ccfEjUyIiTsXOkkITwz56fKChYIn0icKooiIDA1JEcnMip9Pdn06bbY10MMJWQqiBC9daSwiwUbdUIJTpCuC906+gVFRyfwj5zk2l7wN8I7QSHuQpLBxBNb7eHsIxWAobynD4iE5IoUIE0mrbRnk7yQwdCV073UOyYoEsxa3mz/vP8Cf9x9gYmIiN8ydww0L5px7Z5w1YiRXTZnOyxlnKKwbpPd9hVD6JZhPUnUnVK6e7kqbdbO34iiQBEBJk2XtqPksHXEpRY35bC99nb0VO2j2+HaqisGk2i+B4PJDQwJ/h1CG2lQ8p0vL+NGWt/jZtu1EhYdDBKydOIWHb30fewrzePz0cV7KOENTW+9r9DVTZxITHsGLGWd4PTuDnfm5/OtkPLNHjGTl+ImMiY07P+1fgLcDgq3mqwtK/5W3VPFg1hPer5JZkhTBtxb8nKPV+3ml6FmKmjQN+EAoeCIDoiCKiEhoSooYwRfnfItWTys59Rmk151iZ9mbVLVWBHpoIU1XAQUnhVBEJBiF4omg3gi1EEqLp5VvH/8VCxJncbw6FU83gZPFSXMoaSqjpPn8Pm9h4wg8WCJdEYyJHofBRX1b7bnQyZio8ZxtLhrU7ydQtA0kMnQV1NTw4L79PLhvPwDhuLhi8VS+tfYqvrX2Kg4WF/JSxhlezkilqF4hFAiOE1JdCeaTVF0JtW2Krjxd8BovFm1h3agV3D5hHe+b8lEmx07nX7kPBHpoA6YAigy2qFonfdKc4PL5uv3dpWQwuqDA4EzF0+J20+J2E97sIj2vgp9t3c6dSxbyi6tv4Lvrrua5tFP8bM/284GRHmycPotWt5vDJc4+U0NbK1nVlWRVV/Jmbiax4REAGDi3lxbucjHBNZJJCUmcaCykumXwg3zqgtI7wR5AaZdZH8mzBW9w04T1LB+xhiNV+3il6FnyG7MDPbSQpOCJ+JSCKCIioSGrPp8fnfwDC5Nmsyx5OteNu409FdsBWJy0gtkJTjeUrLo0Klv1Xt4f6oYSnBRCEZFgFMwHiwYiVE4YtVk3R6vPdPlYe+eTa8eu5ZKkubxcvJW95UcpbDpLi8fpGndpyiUsTb6CurYGdlfkAjAueiLfXPBTipsKOFZ1gKPVB8ipzxiy0+90pBCKyCAK0JT0/3j7MFtPZXHDHKcTyrfXbeBLq9ax/K+/o8XjJjkqmqrmQToR1B5CCZIACgR/CCUUtztC5eRVV1o8rWw5u5stZ3czO34aDe5GDDAhZjLvnvhBdpVv4WjVftps8PwN94UCKDLY2gMo4PsQirqg9E15QwN/2ruPP+3dx6pJE3nfJYu5YvJUvrfjDQBWj59ETk0VxfV173ju+LgEFo4aQ0RYGJ9YuooNU2ewuzCPfUX51La00ObxvCO8smjUWD65dCVXTZlBVFgYGDhYXMgfDu1le2X6oO9pBVu9D9aAabDX8Nq2eh7OfZ7nCjdz0/iruHXCVSyYu5RvHvssje6GQA8v5Ch4In6lIIqISHDyWA/7K4+zv/I4fwdiwqJpdDcDyVw3diLrRl3NhjE3AFDVUkFWfRp/y/rtuXkPpe90Eib4KIQiIsFoqE7JEyohlK64MGwv209KZBK3TbiGdaNWkFGXQ6O7mfjwWNaNWoEBHs/bxNGq0zS4k6lqtdyf8RhXjl7I1WNvZOO4W6lpreZPGT8ntyEz0N/SoNHJKBH/G6yTS53lVlWfO9k0NTmZuaNH0eJxA/DIre8jLjKSzdkZvJ6dzr6iAto8ft6XDMIuKBB8J6U6C7UQSihvTwCk1WV7P0tmTNRkRkWN4aPT/526tlr2lm9nV/kWSpoKAznEftMxDwkEhVAubrC2E/blF7Avv4AwYzDWYOItv7z2RsbFJbCnMI/7D+9nS+75/aDEqCj+cHAvbdbD1VNn8JFFy3j//Et4/PQxfrlvJ/Wt56eGt8DM5BT+eP1tTIhP4IX005wqKyUmIpy75i3mB1dcw5ffaOZgSREmvi0gUf9g7IISbHU92Gt4XVsDj+W9zAuFb7Igcda50MkHp36K/IYcdpdvpckTXD/TYKTgiQwqBVFERIJTo/v81Wh/yd7G33N2MC1uEnMTpjM3YTqjo2LPhU4+Pv2LJEWmkF2XRla9c9MUPX2jbijBRyEUkaHH43YF/cme3gi1E0K9EewHnDrzYNlXcYwjVadZPmIhG0avZuWIRYS7wmnztJFbX8i2sn28UPgmbutsL9W1NbCpeBubircRFxbDshELWJWymLNNThvpK0e/izkJCzlStZdj1QeH/JVUOhklMjg6nlyCwQui5FRVkVNVRTjOSb9/HjjChrkz+MCCS/jYJSuoaW7i1/vf5s9HDwzKeC4IoUDQBFGCfbsk1LY5Qm17orODVSf5xP4fcEnyXG6bcCnrR1/H2lEb+MbRz9BqWy++giDW+ZiHyGBQCOXiBiOE4rbOesPqXHzokSe5Zf48blk0jwnxCRcsd6aijDMVzn7Bg0f2kxIdw7fXbeBjl6xgRHQM92155dy6xsbF89XVlzMuLp5/HD/Md70dVQzwdkEef7/pTr69bgMfeP4JGjs2VwlA/Q+mWh/MdT2Yu6A0uJvYX3kcSCbKFcmoqLGsHrmemye8lz0V29lW+lrIhkQHg4InElAKooiIBCe39ZBRl0tGXS4vF2313psMwLHqIi4bmci60dewYazTFeVI1T4ezPwVACMiR1HZohMKfaEgSnBRCEVkaOp4AAgCfxCoP4ZiN5SOJ40gOA88tWvxtLK7/DC7yw8DMCV2Ai7joqChmNYe2uPXuxvZUXaAHWUHgCggisrWaCbHTuOS5BW0edpIrT3Bwcrd7KnYNijfSyAphCIyeALVDeWxo8d47OgxYiLCWTd1KtfMmklRnfP+Pj4ugR9fuZHXstLZnJ1OaeMgBO+CsBtKMJ2Y6kown6zqSqiGUCyWI1WnOVJ1mqSIp5keN/lc6OSLs79NYWMuu8q3UNCYG+CRioQWhVAurn0bwZ/bBzlVVfz27d389u3dhBlDuHXRFv/ODmgGqGhq5Ce7tzE9aQR3zFnAY6eOsbcoH4Brp87kXdNnsz0vm78ec0Ks4S4XbR4PuwvzOFhSyIJRYxgXF09WdeX5FQe4/gdTrQ/2LijBWrubPS189chvmBE3mRvHX8n60VexfvRGHsz8FUeq9gV6eEFJwRMJKgqiiPifcVsiq0P7ygkJrKcLXuPpgtcIN2FMi5vI3IQZ1LQ6e0URJoJvL/g5je4GMupOk153irTaUxQ3FWAD0ugwNCmIEjwUQgldqndyMUMtiBJsB5D6K5ROHOU29P8qp5eLtrKpaBuz4qdy2cilXD76ElamrD0XPFmSvJKsunRq2qp8NNrgpBDKwKjWSV8EIoTS2NrG5vQMNqdnOGPAxZSkJGYkp/A/V13Hj+xGDpUU8npWBo+eOkpVc9NF1jhwpi6cqPBwmtraFELppVDb3gj2k1jdqW6t43DVKdqvsC5oqmXtqKtYP+Y6cuoz2Fn2Jgcq36bFE3x/I/6kWicA40Ylsmz+JI6cLsBj+1bDBiOE4q+AyGCEUAZr+6C9e0l43fnfQXsIpf1VS+rr2JKbySVjxrFq/MRzwZObZ80F4KkzJ8iurnKe6/EQZgxuazlTUcbqCZOZkzLqwuBJR+0hlADV/mCZiidYa3qwHwfIrM/jt+n/5KHsZ7l27FrO1Bz3+WsMlXqn4IkENQVRRESCV5t1k16XS3pd+5U3yUS6IvhTxmMsTJrNkuSZLBuxGoBn8h/mzbMvE+mKYnTUWAob8xRE6QMFUYJDxxAKKIgiMtQE+0mfi1E3lNBjsaTVZZNWl81DOc8SGxZDgzuZufFtfGz6FwHIqk/lcOU+jlTto7J1aO8PK4QiMngC1QkF4EBBIdfc/xdmjxrJxlmzuHbuTP5z9eU8ecY5gL9s7HgADpcU+WWP8fJJU/n7ze/hVNlZdhbk8mpWGgeKCxVC6aVgPWHVlWA/idWTZk8Lv0r9Gw+Gx3LV6Eu5YfwaPjD1E7R6WthfuQuD0TEVGVYS46P5/bfvorSils27z/D6rtOcyizp83r8FUIZjICIv0MuMDjbBwlRUdQ2N3tfz+V9LQ8uY/BYS6vbDcDImFgA5qSMYuW4iZTU17E9P+eCdbWHWeaPHA1AaUMdF6UuKOeoC0rf1bTV8XTBa7R3MpV3UvBEQoqCKCIiwa3F08obZ9/mjbNvAzAmaiQLk2ZzpiYTl4E5CQv51MyvUN9W16EjymkKGnN00KQPFEQJDuqGIjJ0qRtKcArlE0i90eB2fk9n6sL54qEfsXbUMtaPWsydkz/EnZM/xEPZf2BfxY4Aj3JwtG/raBtHxP86nmSCwQuipJWVk1ZWzu937yElNoaqhiaIhy+suIwNU2dwtr6O17PTeT07g135ubR43D553eXjJgAQHR7OTTPn8m9LVtLqdnP4bBFbc7N44vRxSsNqfPJaAxVMJ6e6EkrbGqG6DVHX1sCLRW/xYtFbzE2YTmZdHmOj4eoxN7IkeRU7yjZzqHLPuel5RIaqjMyzfO8nz7Ph2gXcuXEp733Xcm7+7B+prm0kJiqCxua+/w+EaghlsKfiAd9tG7iM4cPLl5JVUcmmM6nnjgS3B1DCjYuN02cBsNMbMlk7cTJhLhc783OobHpnrRkZE8vkxCSa2trI8nZD6TWFUIDgrefBHECR7il4IiGtcxAFFEYRGWzGmPcAVwJLgSVAAvCwtfaDXSw7DcjqYXWPWWvf74dhSoCcbS7n7Nn29+VkatvO0uT+u9MRJWkGlySvBOBHJ++juKmAcdETiXBFUtCQg4d3zvkpXdMVwoGnEMrQp3o3vIV6EEXdUEJPfmMxj+dt4vG8TYyPHs1lI5eSVnsSgFUpl3P1mBs5XLWXw1V7KWnq/5Q/wU7bOINLtU4gMN1QKhoava/t4ivPbeKqGdO5dtZMbpu9gHsWLuXtglzufv5xAKLCwmh29z+EsnbiFFo9bv5zy6vUt7awePRY5o0czZIx4/jypZdztqGeJ8+cwOBt/a9OKL0SrCetuhKqIZQztc5bbn5jMtkNLVw2Ko4PTfsM7570QfaUb2dH2WZKm/veAWK4Ua0LTR6PZcv202zZfpr4+CgWzp1Ada3zXvPTr95OUnw0r+86w+tvn6a4rO/hQYVQeuarbYPo8HBmjxzJF9et5coZ09mSkcnJs2fJr64hJTaGL65by5Ix48mtqmLHyRyIg6mJyQDsLy64YF3tHVJWjpvAuLgE3i7IpbltADVbU/EAwdkFJVTrdqAFqt4peCJDjrqiiAy6b+EUrjogH5jXi+ccAZ7t4n7fT44nQaW6tY63SvfyVuleAEZGJjM/cSb7K+uZFONctXPZqKtodDeSWXeatLrTpNeeJKchM8AjDx3qhhJ4CqEMWap3ck6wn/y5mFA6OdRbQ/lgVFFTKU8XvO79KpnxjYYWTzM3T3gvN094L0WNBRyp2sumoqeHdHBXIZRBoVonFwhECKWmuZnnT53m+VOniQwL47IpU851x4yLiGD3hz/NoeJCXstO57WsdM421Pd63SNjYpk3cjQZlRUcLHFCe2cqyohwuRgVE8fkxCSOlTon7s99t3XhRIWFs3DUGNzRrRyrKMZjA9utM9i3Q0JpOyNUtx+2lu5ja+k+FiXN5vpx67lyzHWMj5nE79P/H4Cm4umZal2Iq6trZs+BLCK9X2/feoZrNy7ks3dfwWfvvoJjqYX866X9vLUvrV/rbw+h+DKAAkMvhNKf7YKG1lbu37uPxtY2Ns6exY1z55BVWUmEy8XI2DiSY6JJLSvjT3v24baW+KZIaIFWt5valpYL1tVei6+cPB1wOqQ0uX0QGlEXFCB4a7m6oPRJQOqdgicy5CmIIuJ3X8IpXOk4CcotvXjOYWvt9/w5KAkN5S1V7Cg7ADhX7TyQ+Sq7yjNZlDSbJckzWJi0jNKmYn5w8isAXJK0kurWSvIasvHgm1bLQ52CKIGlEMqQononXVI3lOATqieRemtfxTH2VRwjJTKJ1SlLuWrMIhYlLeeloicBWJa8muKmAoqa8gM8Uv9RCMVvVOtCjBnErFkgQigtbjdbs5yLL8NxERsTwb8OHWXj3Jn89/qN/OCKazlQXMCP397KoZKii65v9fhJJERGkV1dxdi4eKLCwiisq6XV46Govpai+nfWjA1TZvAfq9Yya0QK4S4Xbo9lc3Y696fu5nhF4LtLBMsJqu4E45XT3QnFk1nHq9M4Xp1GckQiceExGCA5IoUvzf0ue8q3satsC1WtFYEeZrBRrRtinn3pEM++dIhxY5O4ev08rt4wn7Ejnf/n2JhIrr50Dm/tS6OuoW/vkf7qggJDI4TS3+2Ck2dL+fqrr/H/tm7j5vlzuXrmTEbERHOosJD86hqeOHaM06XO9n1TWxvNbW4iwsKId0cSXuci3OWiObYNC6REx3DjzDm0ut1szcv2fTA0SEIoga7vwVjLQ7FmB0BA6p2CJzLsKIgi4lvW2nMFyxjT06IiF1XZWsOOsgPnwijJEYmMikomvzGZSTFV3DXlYyRGJNHkbiSzLpW0upOcrDlCYWNegEceOnSiJnAUQgltqnfSW8F+AuhigvXKpv4ayiGUipZqNhVvZVPxVsJNGG02mSkxtbx/yseJDY+jqDGfA5Vvc7ByN6XNxYEert9o28Z3VOtC02Bd4dxRIEIoAJWNTfxs23Z+tm07s0amcN3s2dwwfzb1ra0ArBo/kSVjxvNKZir5te+cauGSMeNocbdx/YzZLBs7ntyaKl5MP8Pjp4/T3MVV0hunzeRP199Oi7uNfxw/Ql5tNbNHjOR98xeRFBXNV9/cRGljg6bj6YVQ2r4IxW2HqtYaqlprgGRgNEWN+bxr3O28a9ztHK8+yI6yNzhdc0xdUFCtG8qKS6r51xN7+NcTe3C5DJEey2VrpvPNT72L//zYNbx9JJvXd51mx8EMmlv69r49FEIowTYVT1VTE/88dIR/HjpCZFgYUeHh1Da/s3adPOscPxuX4HwDbR4PYd4AygdXLCUxKpoX009zoszPx9kCOBVPsNT3YKzloVizB0ug6p2CJzLsKYgiEhATjDGfAkYC5cDb1tqjAR6TBKHzB0+cjihfOPQ/LEycda4jym0T7yYhPIlnCh7GRRjXjruZnPp0cuozafIExwZwMFM3lMBRCGXYUL0b5tQNJbh0PCgFQ+vAVJt1OsHlNibw2YM/ZO3IZVw9Zsm56Xiezv8nW85uCvAo/a9920bbNINKtS6IDKcQSnp5Benle/j97j3OOHCxfvI0/n3FZXxr7VW8npXOJ1559oLnvF2Qyxs5GbgwrBg/keunz+b7V1zDnXMX8pU3N5FRVYHBmWZn+dgJfGHlZdS1tPCDnW/y5JkT59aTX1vN19as57PLV/P9nVsCfkV0V4LlJFVXgvHEVXdC8Yrq/MYSvnX8AcZEjeS6ceu4buwaFiYt4zvHv0BNaxUuXEN6aj4/Ua0LMR6PU4+2v3qST2VVcM2VC9hw5TyuXDmLhqYW3vflv1Be1fup2joajBBKqHZBgf5tF7S43bS4u+5svSsnl/35BXxm9WrqmlvYmZNDUU0tn1mzmo8tX05eVTV/33Po3PLfXXc1iVFRvJB+mp35ObR6fPx+py4ogLqgDGEDrncKnoh0oiCKyKDY6L2dY4x5C/iItTY3ICOSkFDdWsuu8kPsKnd2KJIi4nGZMCpbklmTksQtE94HgMd6KG4qIKs+je2lr1PQGNg/q8mx05kQPZns+jRKmi/eAjpQdMVwYHQMoYCCKEOM6p1cYCgFUYLpAFN/DdWro6pba9lUvI1NxdsYGZnM2lHLKGp0jhXNS1jMeyffy7HqAxyrOkBmfeqQvPpZ2zSDSrUuSHU8wQSDE0TpeLIJBjeIAvDrN9/m6QMnedec2bREvPNk0Na87HOf7ynK569HD/KNy67knoVL+MLKy/jqm5vOnaC6ZdY8Fo4ay5+PHOC1rHQAwozBbS2bszP4xNJVzBs5mqSoaKqbm86/iEIofRIq2xahuM1wtrmcf+Y8z6O5LzM7YSq1rVUAfHrWfYBlT/k2jlbtp9W2BnScIUK1LoSdTi3mdGoxf/jzFpYsmsySRZPPhU6+9al3kZQQw7b96ew4mEFlTd/eh/wVQhkKU/HA+e2CgWwPVDU18eMtW/nv667la1deQVHtMpKio0mMjiK3qopvvbaZI0XFhOP8/G2LZePcmdw5dyHVzU28npXOk2dOsLvQD92yA1jzg6W2B2MdVwBlQAZc7xQ8EbmIzkEUUBhFZAAagB8CzwKZ3vsuAb4HbADeMMYstdb2L3Iuw0516/k9pd0V1dyz+6vMTpjK3IQZLEmaxLLk1eyv2AXA3ISFXDP2ZrLq0sioO01WfTqttmVQxjk5Zhp3TfkYAG7bRklTEZn1qaTVniSzLpWatqpBGUdfqBtK4KgbypCgehcMPMHfOjtYDhb1x1DuhjKUDlCVt1TxQmF7h91kEiIiKWsu4crR7+KasTdR21rN8epDPFPwMI3uhoCO1V/UBcVvVOtCzHDphpJXXc2D+/Y7r8/5k4Bt8Rde8ewyhsa2Vn57cDeXTpjExmmzmJ6cQmpFGXEREVwzbQZtHg8vpJ+mpsWp0W5rMUBGVQUVjQ3e4EnUhcGTjuo6HXoPgiBKMG97BOPJq66E2jZDm23jVE0GzjQ8cLQqj41jL+Xe6Z+n0d3Awcrd7CjdTH5jTkDHGaRU64KEcVsiq52QVEtSRL/W4fFYDh3N5dDRXCK995UV1rD82slcvnwmHo/lWFohz795lJe3n+zz+hVC6d5AtweOl5TwnocfYeOsmWyYOYMwl4t9+fkcLCgitezCbfwfb9nKz7ftYO3UKdw4dw7XzZ5FdXMTuwvzcBnD+snT/NsJRVPxBE0ND7V6HWA+q3cKnoj0g8IoEsqMx0NY79Pbo4wx+zt8fb+19v7+vra19izwnU53bzPGXAfsAFYD/wb8ur+vIcNbg7uRI1WnOVJ1msfzwGAwgIdkJsSMJDE8ievH34HLuGjztJHbkMmDmb+ktu2d83/70rHqA1SklzEiciTjoicyPmYSCxOXcvmoawBo9bRwtHo/D2X/wa/jGAgFUQJDIZT+U70TIChP+HRnKHVDgeA52NQfQ3lKnlM1GXzzeAYxYdEsH7GAS1MuYW7CHJrczu9rzcgrsdZyouYQdW1D5/uGodkFRbVO+ms4dkMJr7swhOKxFpcxlNTXcby0hNkjRrJq3ERSK8pYPnYCkxKSOFRSyKnyC7fBLRAdHs6khCRcBgpq+7AvGWTdUILlRFVXFELxn0fzXuKxvJdZmDSLDaPXsG7UWkqaCslvzCHchOMyYbR4gufvQbVOOmsPoED/Qyjt/vLPHfzlnzuYOX00l6+ZzeWXz2HqROfcT1iYi4/evoZdhzM5lVmM7UPZ6msIxRh6tf6hFkKB3m8PtHk8bEpNY1Nq2kWXbXG7eSszi7cys4h8LYzo8HDCm12smD2Bv910J5VNjbyccYbn0k6zryjft/0fNRVPUNbwUOiCMlTqnYInIj6iMIoMUWXW2pX+fhFrbZsx5kGcArYe7bCJj9gOzeP3Vhxlb8VRYsOimZcwgwVJs1iQOOHcSZX3Tr6XGXFzSK87TUbdaTLqzlDbVu2TcdS21XC69hgALlzEhScQ6YpkQeJSNoy9gZGRo6lrPb/huzplPe+Z/GFO1Rwjpz6dtLpT5DdkB9U8zAqiDD6FUPxK9W64UBBl0ATjwab+CsWTShfT6G5iZ9lBdpYd9N6TBMCakeuZGT8Pj/WQVZ/K0aoDHK3aT1nL0Ko7QzGE0guqddKtod4NZURMNJWN57uShNe5MDghEk+8ZUbyCAAyqysAWDl+IgC7C/MuuCK6/TkLRo4mMiyMk2Vn8fTlTGRHCqH0WqhsU4TS9oLFcrw6jePVaTyQ9ThYGBUFK0as5c7JH2Z/xQ62l75BUZMfpqbwL9W6YcZXIZSMrFIyskr5+yO7cLkMkR7LjKVj+cjtq/n4nZdRWlHLtgMZbD+QzoETebS5e398rqcQSrjLxfolM1gwdSzxMZGUVtdz4Ew+hzMKL7pef4dIB3s6HvDP9kCL202L2w3A0YxiPvHUM9w8fx53zFnIPQuXUlhXwweef5zs6iqfv7am4gm+Gh4KAZReCtp6p+CJiB91DqMoiCLSo1Lvx7iAjkKGvAZ3EwerTnKwqr1tpnOi5Xh1KWOixnHZyCu5asy7ADhTc5zfpv8PALFh8TS467paZZ948JwLtCRGJDEycjQHKt9mS+kr55aZGjeDSFcUk2KnMidhAbeGvR+LpaqlgtO1x9hydhMlTRffCR1Mw/QETsB0DKGAgighRvUumCiIMijUDSV0fPXI/zE9bhKXplzC5aMWcceke5gQM5l/5vwJgBERI6lsHVr7tdqG8QvVuhA21LqhRIeH8/nLLiOjopyXTqdS3eQEUNpfYd2IKSwZM56G1laOpBdDDExKcPYRT5aVXrAuYwzWWi4ZMw6XMRwvKyHM5aJtoO36g2x7JFhOVnUl2E5gdSeUTmw1uZ3fcX5jMuFVFUyMPcLlo67kitEbyaxLZXvZZg5U7OpwWY94qdYFmY4hFBjYlDwAmYcLuf0Dv+WyVTO4/LLZ3HjFQu7cuJTP/OBRDp8uYERiLM2tbTQ09n4a784hlHEpCXzw2hVcMmM8RRU1uFyGz9yylsLyGv7x+n6e3XmC1jZ3r9btz6DIUAmhtHo8bMnMYktmFjER4VwzcyYb5s0gr8Y5TvvhRctIjIziubRT5NX65mLEcxRCCaqpeEIpLBoE+lTvFDwRGUQKooj0aI33Y2aPS4n4yeslO3m9ZCdhxsWMuCksTJpFQvj5Pauvz/8fLPaCjijFTQX9fr05CQu5YvS1zmsXP09ly/kTHVPjZlHaXMzjuX+lydPEuOgJjIoay6SYqYyMHE1CeBIlFGIwQXnwR91QBp+6oYQU1btgFmQnfnqiIEpwGIoHrLLq88mqz+exvJcZHZVCmHERbmBCzGS+Pv8n5NRncLByN4cq9yiEIt1RrRskpnfnogYk1LuhRIeHMz4xgQ8svYRrZs7k7dw80svLqW1uZsGYMXxh3WV4rOXRI0dpcbtJao7C02xpaXPT2HbhScz27iarJ0wG4EBx4cBDJ10Jom4owXKyqiuhEEIJte2E7Pp8fpP2D/6a9TQbxqzmpvFruXrMDeyv2An47oKcIUK1Lsj5ohtKbV0Tr205yWtbThIZGc7yJVM4fSAPElx85LZLeffGpRw4kcu2/elsP5BBWVV9r9cdVeuhzTTziwc3U1RaTdz4OFIS45g7eTQfuW4ln799HW1uD8/sOH7BVDwxURE0Nrf2uO7BCqH4Y/3t/B1CaWxt48XTZ3jx9BkMhnAMy8eO5/Y5C/jq6ss5WFzIc2mneCnjDGWNDb598WEeQgm2+h1KYdEA6VO9U/BEJIA0PY8MN8aY5cBha62n0/3XAF/yfvnPQR/YMNTqCQu5AyCDxW09pNVlk1aX7b0nGRcuHs97nQVJs1ictIBVKesAeKPkJZ4t+BcGw8SYKRQ05vYqCBJhIrlt4t1Eh8XyYuHjFDXln3ssPjyBsVHjOVN7nNQ6pytLboOzXRcTFku0K+Zcx5RgDJ10RUGUwaUQSuCp3g0hCqIMimA78NRfQ3HbqrS5wvtZMrVtlr9nP8OG0Uu4Y9I93DHpHrLq0vhHzh8pbS4O6Dj9oX37RdstXVOtCx6D2aEkFLuhVDU18YttO0gvK+eGuXO4Yvo0mlrbiI5wary1lj/s3ss/Dx8GoL61lYrGRiLDwxhJLOF1LsKMoSXOjQXGxMZx9ZQZ1LU0s7cwv/sX9hWFUHolFLYlQmk7obatnucL3+SFwi0kRSQQHw7Rrhh+sOjXZNWnsaNsM8eqDuFhENJvAaRaN3T4IoTS0tLG7n2Z3vW5eeOVE3ga3Fy+bjb3fXwj9318I28fzuLLP3261+usrGmgssYJNdRntnCWSk4fzmfX8Wwe/+6H+fzt69h2NJNy7zIuY/jb195PSkIMW49ksu1oJntO5dDc2v3/or8DpIMRUG3fFvD31Hxffe4VfpGwk5vnz+WWhfP4/hXXsGLcBL6w+SUAYsIj3hFKHbAgCKGoC4ojlOq0r/my3il4IhJk1BVFQo0x5nbgdu+X47wfLzPG/M37eZm19qvez/8XmG2M2QW0H6G5BLja+/m3rbW7/Dpg6dJQbxs/UB48bCrexqbibQCMix7NwsRZ5DY4091MjJnK1+b/iIa2eo5XH+IfOX/odl3hJoJ1o65mUsxU0mpP8nrJCxc8PiZqPBGuSGrbahgVOYY220ZtWzVu66bR3UCj28cp+wBQEGXwKITiO6p3AoRsECXYTg71ZKh0QxmK21bVrXU8W7CZZws2My56FGtHLuPyUQupbq0EYPmINcSHJ3K4ci81bVWBHawPDacuKKp1Q8NgdigJlW4oGRUV/O+Onfzvjp3MSBnBZVOmMDY+noKaGg4WFpJeVo4FDM4UPLlVVQCMjXc6irutJazORbjLxV2LFhMRFsaLGWfIq63GAL+85kb2FRWwOSeDkno/doMIou2QYA68hlIIJdi3DyyWqtYaqlqTiQ2L5qn817lh/Fr+bcaXqGqp4O3yt9he+jq1bTWBHmqvqdaFJuPxEFbTiDsx5uILX4QvQigAZ9KLOZNezB//8hZTJ4/kistmY2PDzj3+oy/ezKnMEt7am0Z+SVWv1ukyBo+11BfVcTy1gMVzJjB38mh2ncgBYGxKAlPHJFPX2MKl86Zw+7pFAGQUlbP7ZA4vvn2StILut1dDPYTi7y4oAEW1tTywdz8P7N3P7JEjz130NzM5hZfe+yHeyMnk+bRTbMnJosXj4/BdgEIowXDsINhqd6jU6Z4Eqt6FRPDEGHM98GsgDHjQWvuTTo+vB36F80N4v7X2Se/9S4E/AImAG/iRtfaxwRu5yMApiCIhYCnwkU73zfDeAHKA9gL2D+AOYBVwAxABlACPA7+11m7392CDVbDVus4nSyC0N7R8rbiplOKm9ukNk6lsaeaXqX9jUeJsosOaunxO+7Q4CxKXsHHcrVS0lPHm2ZcveAxgfMwkAFanrGd2/AKqWis4UX2IPeXbqHfXBe30OgOhIMrgUAhlwJaiejcgwVbrfCKIrkLuSTCfHLqYYDsA1V9D7eqp4qYyni54nacLXgdigBjunbaCFSlruXPSh8ioO83Byt0crtpHXQidjLqYYdAFZSmqdQMWTPVuOHVD6e0JqMyKSjIrKrt8rH0Nm9Mz+NDypXzusjVUNjbxdm4uRTW1/NuqlXxuzRqyKyt5dP9RwutcjI2PZ9nYCdw+ZwE/YiNHzxazOTuDp1NPkl9bPdBvsWdBtB0SDCetuhJMV1J3JZS2DxrcTTyR/wpP5b/GipSF3DZhDe8adzsHK3dT21ZDTFgsTe7GUDhesRTVugELVK0Lqzn/vxxMIZScvHJy8pzzNpFAfHwUY0cmcvXquXzu7vWkZp/lzT2pPPHqQRqaLt4xY0RiLC1tbppb3cS3hRFV6zQhWHrpBMLDwnh86xE27T3N3EmjmTlhJItnjOf6S+cxdkQ8//XAy70a82CFUEJ1Kh6AtHLndxqOizaXh8eOHOfG+XO4aeZcapqbeDkzlf/du5OzDb2fYqnX2mu8uqAEVCjV6S4sJQD1LuiDJ8aYMOB3wEaclM0+Y8zz1tqTHRbLBe7l/A+oXQPwYWttmjFmAnDAGPOqtbbK/yMX8Q8FUSTYWGu/B3yvl8v+GfizP8cTikKl1g3FK3d9pd7dyLbSfWwr3ee9J/ncY5NiqgDnSqFIVxRXjbme+PAEHs97klM1R8891q66tYpn8h+mpq2KqbEzmRk/l9sm3s360Rt5LO+v554zlA2nK4sDRSGUvlO9G5hQqXUDEkRXIV9MqAZRhmI3lKG0PfXfpx5mUswbrBu1nKvGLOWuKR9jcdIK/pDxU8Bp0d/kCc3fWWdDdVtFtW7ggr3eDeVuKAOdkqej8oYGvvHK6/zk+uv4xoYryamsIiU2ljHxcZTW1/Pd19/gcGERACV1dVxz/1+YNTKFa2bO5Oq5M/iPVWs5WlpMfm01kxOSmJaUzO7CPFo9nou88gAohNKjUAiyhsr2gQcP+yqOsa/iGCMik6hsqWdSDNw1+aNMjZvFzrI32F2+NdDD7JZq3cAFS60L1hAKQF1dM5/94j8YOzqRK9fNYf2V8/jATSt4+MV93T7HGPBYp3bdumExay6Zzt5j2WTkn9/WXDtrMo3NrRw9lEve2Sryzlax+WAaKQmxjB0RT0tb/zpw+LNmD+ZUPOC/EEpBTQ0/fHMLP97yFmunTuGW+fO4duYsfrTrLQBWjptIQ2sLJ8tLe15RX6kLChActTvUuqAEqt4FffAEuBRIt9ZmAhhjHgVuA84VMWtttvexC7berbWpHT4vNMacBUYDVd29WLONILNljA7AS8joGERRCEUkZA1qrfMVdUXpnfzG5HOf3zPlFmbEzyGj7jR7y7d3eSXQ8eqD5z4/WLkbg+H68Xdw3djbuG3C+yluKqSyZeic4LgYdUPxP4VQZJAMeq2bkpjEf629ip0FuRwoLqS6ueuOVH4TokGUYDlB1BvBdiCqP4ZasDe/sZjH8l7msbyXmRI7gXDjtBtPjEjmewt/RXrdKQ5W7uZ49aEh0wllqIZQpN9CZt8uUN1QgnlKno6OFhfz4See5LrZs7h82jSKamv5y/48duXkcrr0nf/r6eUVpJdX8Ke9+0iJjaGuuQVi4D3zFvLFlWupaW7mrdxMXs9OZ1tejn+3SxRC6VEobD+Eysmtyhanq09+YzJvnD3JHROTuW3i3dw4/k5+wp8CPDrxo6CrdR1DKDDwIIqvQiglpTU8/ux+Hn92P7ExkbgaWyEprMtlrYWUpFg+escabt2wmNr6Jv76zG7yi6sAiIoIZ/mCyZRV1hEVGc6k8FjqG5tpaGqlggYqan0zNbdCKD1zW8v27By2Z+cQZgxuayEe/mvNelaOn8jp8lKeST3JC+mnKazz8Xu4QihBUbdDpUYHSigETyYCeR2+zgdW93UlxphLcbpcZXTx2CeBTwIkj48GLjwA304H4iXYqRuKSMjye63zPn6u3kWOSez7KHthqJ088aVpcZN496TrqG2t5w8Zz5HV4MwX3t4RpSvt0+psKnqaabGzmJe4mGmxM4dV8KQzndzxL4VQxI8GvdbNWbCADy5cyseXrAQgvbKcB47s57FTx5xlYXAbgYdIEEXdUAIrVK527o3chkLvZ8kktyXyXMGbXDlmKfdM/SQe6yGnPoPH8/5GfmN2IIfpU8NgKh65uEE9jhkRP6J/o+zCYIVDQmVKHoCy+gb+dfgo/zp8YddJlzHnrkrvSkVDo/d1Xdy/fT/Hc0q4dtYsrp49g1tnz6e2pZllf/0dbR4PE+ITONtQT5u/uqEohNKjYA+hhNJ2wdvlh3i7/BCTYsZxSfLcQA9H/GvQ9+2iI/p2HNOX3VA6hlCg/0GUhsYW7/qcriTGQHOiE0KJCA/jylWzuPvGFcybPo5TmcX89l9bOZpaeO7548ckMiIxlsiIcL7/+RspOFvNvmM5PL/lGAVnz0/t1pzg6tf4uqIQSs/c3m2B8DoXn37yOW6aN5fbFs/n65ddydcvu5KHjh/iO9vf8PnrAsN2Kp5gqttdXZQroRE8GTBjzHic+Yk+Yq19x1a8tfZ+4H6AyYuSun33URhFQo2CKCLDx8VqHVxY7+LnjB+Uc23DvStKe3BkTNRI3j/5RgBeKd5OVn3+ucc6dkSBC4Mo7R1RDIb0utNO8CRuFoeq9gzWtxDU1A3FvxRCkWDT11oXPWmyXfab37FswniWTRjPkinjaXE7B/kmJyTx4ns/xKGSIg4UF3CguJAjZ4uob734fNs+0+FE0PUzZpPaVEJmTcXgvX4vDYUgSqAPSPVXKJ1supiq1hoezn2eh3OfZ3rcJFalLGbtyPnnup4sSV7FrPh5HK8+SHrdady2f23Cg4WCsjIQfTmOGT9ysl/264ZyNxRfTMnTU+iks+a2NrZkZrElMwvX64Yl48cxJTn5XNDkgetvZ0pSMm8X5LItL4ftednk1FT1eUy9ohBKj4J92yFUtgvyG4vJbywO9DAkyPV13y4ptv/HMYNxSh5jnA4nkdVuLlk4iffdsYp1q2dRXdfILx/awsvbT9DQ2HJB0LG5pY2f/mUzWfnlLJo9nnXLZnD3TSu5/vIF/OnxHby83Wk4E1V7/sepEMp5/g6hVDU18fDhIzx8+AhTk5O5bs4sMhud/fuU6Bgev/39bM7O4NWsNA6XFPnuIhh1QQnKmj2chULwpACY3OHrSd77esUYkwi8BHzTWrvbx2PrMowCOjgvwUlBFAGgzYOpDN4d1GEqqGudrw2nMEp7cOT2ideyKmUxO8oOsLlkV5fLunDhwXNBEMWFwYNlWmwdxpgL1gkwOmocYCltLvHb9xBKFETxn87bvCGxrat6F2wCUuta3G725OWzJy8fvJm9cFyEh7l4+XQqyyaN50ur1uEyBrfHw8defpqtedmkRMcQHxlJbk11zy/gA7NHjOT3190KQJvHQ051FYdKCtlTkcP+0nxyaqv8Poa+CMUgylDohjKUuspl1eeTVZ/P43mbvPckc924CawddTVXjbmeRncDp2qOcqzqAPsru95uCiV+DaGo1gWjQa93HU8wgW9PMrUbrCBKoLuhgH9ORrXzWMuhwiIOFRYRjvN7+u3OPVwxbSpXzJjGddNnA/DwicN8c9tmAOIiIvwTjA2iTmzBcPKqM4VQgohqXTAK2eOYwRJCsRZmTBvNrTcs4caNi4mICOeZFw/y0KNvU1XtnTInKeyCoGNRaQ1FpU645HRWCU++dpibr1zEVz96NR++bTUnMorJKbzwQgZ/bSP4s14PxraAv0MoOVVVPLB3v/NauBgVGUthbQ0fv2QFn152KcV1tbyencGfDu8lv9aH048O0xBKsNfsXhsi9S4Ugif7gNnGmOk4xev9wAd680RjTCTwDPCQtfZJ/w3xnboLpECIHKiXYaFzEAUURhEJkJCsdb40lMMo0a4oEiLiaHA38Uz+65S1VAIXdjOxWFalLKbJ08yRqtPnnutpX8bMY0bcclo9bRyrPn/C4vpxt3PpyCuoba0huyGdrLo0MutTyag7jSiI4k/qhiL9EFS1Lq+6mm+/5pzQiY+MZOmE8SyfMIGTZc7f822z5/Pdy6+mtKGeQyWFHCh2bofPFvm8Ff7sEc42+ZacTA6fLWLpmPGsGj+JW2fPIzIsnFcy0/jsa887Cwfh9DwKogTGUAqiAPwlayv/zNnFkuR5rBqxmNUjFzI6aty54MklSSspasqntDm0r6DuvG0iQ1LA652CKAPj75NRnb2els7raekATE1O5vJpU8lqdvYZx8clsP2Dn+BwSRHb87PZnpfNkbPF51r7+1SQdEMJxu2KYD+hNaxCKBIsBr/WeU/I2hG+m9qiYwgFfDslT08hlLAwF7fduJT33bGKsaMT2bk7jT/85S0Kiqo6re98B8CWpLALHvNem8aLW4+zcPZ4br1qMeuWzSCnsOJcN5WuqBvKOw1G3c+sqORjjz1DYlQUG2bOYOPsWdw5dyF/OrwXgJXjJjIyJpZtedk0tvkobKoQSlDW7OEg6IMn1to2Y8zngVeBMOAv1toTxpgfAPuttc8bY1bhFKsRwC3GmO9baxcC7wPWAyONMfd6V3mvtfbwoH8jHahLigQzdUURGXxDsdb5wlAJozR5mvnFmb90+3h7AGVx0hyuG7eOo1WpHKg8QX5jMfVtDcSERXPXlBuZnziDHWUHOF6dSnlLMgB/z36TfZW5zEuYwcKkKSxOWk5RYwE/PnUfAFeMupZGdwNZ9WmUt5T6/XsNdgqi+IdCKNIbwVzr6lpa2JGdw47sHMC5ImnLyUxaG9wsnzCBpZPGc9302bg9Hhb/+f9o83i4YtJUosLD2V9UQFVz04Be/5Ix4wB49NRRXs/OYHRMLMnRMYyNi2d60ggyq5yr1lzG4PEePAozhsWjx7Jw4iiKGmo5Ul5EeVPDgMbhK8F4wuhihloQJRS3lwBaPK3sqzjGvopj/CHDkByRQGVrMtNi6/jwtM8QFRZNSVMhx6oPcrz6EFl1qXjwbRBMZKCCsd756yRTR4M1Xc5gB1EGsxsKOFdI5xyucl4bF1j44+69XD5zKl9cuZYvrVpHdXMTn37lOd4uzPPfQNQNpVvBvs0wFLYHJPgFstZ17gbgryCKL0MocGEQZeL4ZO79wDoS4qNJzSjh4NFcEuKjmTAumbNlNbS1vXP7NrLafUGgpGMQJTWrBM+Vi5gyfgRREeE0t/buPXswuqGEcggFfF/3a5qbee7kKZ47eYrIsDBa3G7CcXHPwiXcMWcBja2tbM/P5tWsdDZnZ1A9wGMN5yiEEnT1eigL+uAJgLX2ZeDlTvd9p8Pn+3DaeXV+3j+Bf/p9gD6iQIoEI3VFERkcw6XWDdRQCaN05c2zu/HgYXXKUpaOmEezu4U26yY+PBaAw1WneST3Rcpbqs49p7DpLIVNZ89N3xMfHktKZBL5jclMiqnimrE3MzJqNAA1rdVk1adysHI3ByuDfkamQaEgiu8phCI9CaVal19dw6NHjvHokWMApMTEMGfUKFqq3BAPn1i6ivWTpwGQWlHG/qICtufnsCkztc+vtWDUGFrcbWRWOVc3lzY2UNrYQFplOTvyc96x/LSkZL60ah03zJiD9R559FjLM6kn+cXJrUETQGmnIMrgGwrdUCyWylan7XR2Qzz/fuhHrByxmCtGz+Oq0ddz7dibeanwSV4pfoYwE0aEiaTJE1q/Jxm6grneDaVuKIP9WjD4QZTS+np+tXMXv9q5i+ToaNZOncLl06aS7g2lfnDhEu6av5g3czJ5IzuTY6XF+GVEQdgNJVi2J4L5pJZCKOJPwVLrOgZRgjWEAhcGUQrDq/jprzdx1eXzWLV8Gv/+yWtoam4lM7uUk2eKSM8sITO7jJzcMlpa3cTGRNLQ2HJBF5PoWg8ejyXMZTDG4DKGMJfrXOhk1pTRlJTXUFvf+/dKfwRVB2tKHn/Vf392Q2lxn+9o81/Pv8pTk09w3exZXDtnJtdNn83JsrPc+MRDAES6wmjxuLtbVd8EOIQSqPrdXq+DrVYPRSERPBnuFEiRYKOuKCISTIZKGCWzPo/MrDz+nv0MM+ImMz9xJmOiRlLvbuBw5WlyGgppcPe8cVzX1kBdm3PCMb8xmU/u/z6TY8czL3EGcxOmsyhxGmdjijhYuZtwE8HHZnyBrLpU0upOkdeQhdv6aCcmRCmI4lsKochQUtHYyO4858ri8DoXn3nyeRaPHcuqSRNZPnUCN8+ax+TEpHPBk6+tvoLCulr2FxdwpqLsgrm5Owp3uZg/0gkIrp88jZXjJ5JdVcnJ8rPUtrRcsKzHWiJdYfxsw/WsGDeRJ04fZ0tuJtZabpk1n7vmLyYiLIzvbn+Dhvb2vJqaxyeGUhAlFLeRAEqbK9hUvJVNxVuJCYtmafJ8suvzCTMwN2ERn5jxZdLqTnGkai9HqvZR1xaa36dIX41MjuN/73s3e4/nsO9YDhl5fdt+9WUQJS46EmMMdY0Xvq8HKogy1KblqWpq4uUzqbx8xtnWCMdFVWUTjW1tfH75Gr64ci2lDfW8kZPBN7a+3u22x4AFSTeUYNyeUAhFJLAGI4QCAw+iuMqb2fvqKfa+egqAMfPHsHb1LNatnsVtNywhIiKck2cK+e7/PEdZeR133rqCqKhwXnzlCMVnnWC2x+O8x0+elML6S2bgsZa0k0VEVrtpSQrj+5+/kSnjUziWWsiuw5nsOpRJZn7vz+GE2pQ8g7Gt4c+a77aW3bl57M7N4wdvbGHxuLEkRjl1LiosjF0f+hQHiwt5If00m7Mzzu/vD1QAQiiBDpEGc60eKhQ8CWFdBVJ0UF8CQV1RRCTYhPKVvm7rIa0uh7S6d17l3lceLDkNheQ0FPJq8Q4AXLjwkMy46NGMihzD4onLAWh2N5Fdn85LRU+RVd/3K/aHIgVRfEchFBlqmtva2F9QwP6CAtjjTIOTHB1NeKOLCJeLW2bPZ1JCIgA1zU0cKC7kkZNHeS07/YL1TIxPpKiulvKwML625goiw8KpamrkjZxMHjiyn9SKC993PnrJcpaPncCbOZl8e9vmc1c9vZqVzr9ufR93zl3IIyePcrCk0HlCkJwc6kkwnji6mFA+WBXK20jtGt1NvF1+yPtVMh4aeaFwC+tGLeL9Uz7O+yZ/lLTak/wt+7cKoMiQZ92WCWOS+OIHrwKgvKqerfvT+Nlf3ujX+vobRNmwdCa3rVvEiPgYoiPDqW1sZv+ZfN48lE56wYUBzMEKogz1bigAr6Sm8UpqGsnR0ayfPo0NM2cwIS7h3M/7y6vWUd7YwJs5meTVVvtnEOqG0qVg3lZQCEWGA3+FUMD33VDOnjrLs6fO8uzfdtGSFMHC+ROYMjGFsnKnkEVHhfPuW1ewZuUMdu/P5Ex6MVVVDUREhPGhuy5j6eIp7Nqbzr5D2YAzLc/P/ncTa1bN5LLLZvG5u9fzubvX8/Tmw+e2DyIjwmhp7d3FZ5qS5538HTw9VlzivA4u4qOjePb4SW6cP5eN02fR2NrKGzkZ/O7gHk6V+3Ba9WEcQgm2Oh3qFDwZYtQdRYJFxzCKQigiwcftMRdfaAgZKl1RfMGDs8NY3FTKZw/+D0kR8cxPmMnCpNksSZ6O9T6+IHEJG8feSnrdKdLrTpNVn0aLJ/AH8AKpYxBFIZT+UwhFhiKPtVQ0OgcrWj0ervrjg0xMTGTVpImsmDSRFZMnMC7+nbWooqmBr299jfTKClo9bmaPGMkHFy7lnoVLWDluIve+9BQ5NVUAjIuL5/Y5C6hubuLvxw7S4nETZgxu78mlJ08fZ82EyVw1Zfr54ElnCqL4nLqhBF5h41keynmWh3KeZWrsBNaOWs7S5KnUtzlHnK8YdS0erLcTSk2ARyviW5WV9Xz4Ew8yelQCK5dOZeWyaURGnH+v//F/3EJpRR37judw8FQ+DY0tPaztnXpzsik8zMXcyWOYMiaZ0qo6CstrGJkYyz3XLuf2dYv4v2d38NLuU92+xmB1KRnKQZSqpiaeP3Wa50+ddl4bFwa4ZuoMFo4ey/evuIa0inLeyMnghfTTnCjz0za4QihdUghFJLA6hlAguLuhRFa3krY7hzRyiARakiJ46oWDFJ+t4YZrF3H3nZdiLTS3tBEbEwnAkeN5/OmvWykorDy3npNnijh5poi//HMHo0bGs2blDLJrnO3gCWOSePj/fYQ9x3LYfiCdnQczqart/XuTv6fkUQjlQtVNTfzPW9v4yVvbWDFxIjfPn8v18+bwwJH9AMxJGcWE+AR25OfQ5vFcZG29NMxCKMFcp0ORgifDhLqjSCBpah6R4NT5RAkMr40rhVEc1a117K44wu6KIx3uTWZcdAIRrgg2jruV680duG0buQ3Z/Cn9Z9S767pd33Chbii+oRCK+JXn/EGgwbjquLOCmhoKTtbw7MnzJ9vCcXnH4xwQqm1pueDkz5mKMv571xbaPG4+sng5n1q6im9sex2ARaPHMjdlFDvyss8FS9zWYgAL5NY4VzLPTXHen9rv75GCKD4XykGUodANJaehkJzcQh7JBUgCYOmI1cxJWMD7Jt9Let0pDlXu4UjVPmoVQpEhpLSslk2bj7Np83EAIoHwcBdRkeHcumEx77t+OW1uDyfSi3hs0wG27E3r1+t0dbLJ47E8ue0o97+4G4+1REWEkRQXw9JZE/iPO9fzjXuuwe328Mq+M+eee8Xi6fznXRvYdSKbo5mFHEwtoLiyVtPy+JAFbvvbw0xNTuaqGdO5as50PnbJCuq82x4x4RFsnD6TrbnZVDc3+X4AQbKNoRBK77VvB4Ri/Rfpi7jWFozLRX1NY9B3Q4msbqWuuopXs/fzwitHSBkRx4qlU5k+dRT19c0cP1VITl45VdUN3a6jrLyOF1896qwPCIts4+XXjrH2sllcuXIWbo+HY6mF/L8/v052QUWfxufvEAr4b0qeUAuhWDjXdfWHb2zBbS3huPjA5Zdw7+LlVDQ2sCkzlRfSz7C3KN93U+4phCJ9pODJMKYwigSKgigiwSuUT5b4wlA44eIr+yuPs7/yONFhUcxLmMHCxFnMTxx7LnRy1+SPMSVuBhm1p0mrO0V63Ska3d3v6A51CqIMXHed+0R8IRDt73sSXnfhQbn2IApAs9vNo6eOsXHaLNZNmsrM5BQyqipYOmY8APuLC6lvPT+nszEGay0TvB1VqgZy8ihIThL1JJSDKKG2XTVUroL+2tHfMSV2AutGLWP96CXcNeVjTI6dziO5DwIQH56oTigyJLW1efj6N58kIjyMhfMnsHLZNFasnEZcjPM+Om5UAl/+yDXsO57D3mM55BT2/2RTbU0tHu8Jp+ZWN2er6nhtfyrJ8THcd9cGVsyZxGv7U8+dBFk8YzwTRiayccVsblu3kIiwMOqbW8gqKuftEzk8v+sERRWDF0QJZDcUf2+T5FRV8feDh/j7wUPER0biMobwZhfrFk3hN9fejNvjYX9xAW/mZPJGTibplX46RhcE3VAUQumdoVL/ZWiJjPLdqcybP3IFH7nvJgqzyzi+J4Pdrx3jyK40mhpagr4bSl11FVuzq3g9KaLf6ykpreHXf9zMr/+4mdkzx7Ju9SwuWzuLSu94N142j1lTR7P9QDon0ovobX7BHyEU8F9YJJRDKO4Ov5Sfvr6dt9NyuWnePO6Ys5B7Fi7lZNlZbnziIZ++JhDQEIqm4gkdCp7IBRRGkUDQtDwiwUtdUdQVpcndzOGqUxyuar9iPxmAEzWljIkez7rR17Bh7A14rIfj1Yd4IPN/ARgZOZrKlgo89G7O2KFG0/KIBLfOQRQITBilvStJxyCKyxgyXRXERUaSFBV97irkuSOd95UTZSUXrMN6DzpNSxoBQGGdcwLdJ99NiAVRguEkUk9COeAb6iehchsKyc0t5JHcl5gSO542jxuXgckx0/jqvB+SXZ/OiZrDnKw+TH5jTqCHK+JTrW1uDh/L4/CxPB58aDvgXPE8cWIc0yemcMWKmQCUVtZx9EwBf3h0OwVnq/v8Oh1POBkDTfEu8kurqW9qZlxKAknx0VR6W/lfMmM8Z6vq+Pnjb1FT38SsiaOYOnYE86aMYcPSWaTml1JUUYsxXHDCayhOyzOY4di6lvNTLW09nsWd5f/iqhkz2DB7Ol+/7Eq+ftmVXPvoX0mvLGdifCIt7jZKG/1wcYFCKO+gEIpIz6bOHc8/Xvg+W58/yJZn9pNxoqDf65qxcCIAlaU1LF4zi+vuWg1AfsZZTuzL5JFfv0apd9oafwVRfBFC6ailn0GUtIwS0jJK+Nu/djrrBWZPHc3dN67gw7deSkV1A/tP5PL24Uxe2dH9tHmd9Waqvv4YjBCKr9cN/g2htLjdvJGRyRsZmcREhLNhxgxiE52/BwM8fvv7OVNRxhvZmbxdmEtTm4/q7iDXcnVBCR0KnshFdXf1qQIp4g/qhiIS/BRGUVcUgFeKt/NK8XYiTDizE6axKGk2Ua6qc49/Ze4PiAqLIq8hm5z6DHIaMsisS6WqtW9XUg4F6oYiEhoG88SPyxjiIiOpbX7nAROPtcyJHEVSVDRn6+qpKm0i2hVOclQ01lpK6usvWL59lLNGpACQVV2J3wR5EEXdUAZHqG8L5TYUeT9LptENj+VtYu3Iedwy4X3cMuF9VLdU8vuM/0dhY15Axynib8dOFnDPxx9g3NgkVi6dytLFU1i8eBJNLc57+x3XXMJVl87hyJkCjqUWcDytiMbm1ous1WGtc9Jp6cSxxEZHUlleT229854cFx3JrAmjSCsoY8vhdKyF/an5RISHkRwfTXxMFCUVtefW052hOi3PYG2PWOBIUTFHior59c5djIuP54rp08jOq4R4+MLKy7hr/mLOVJSxKz+HXQW57C7Mo7ZDeMUngiyEAoHffgjW7QOFUCSQSnLLSDuWx60fXc+dn7qa3NRivv6B31FR0reuda4wF3OXTKW8uJqffO7vJCTHMXnmGKbMHsesSyazasMCXvjb9nPBE1N5/m89WEMocGEQpb8hlHYP/uktHn34bdasmMHqVTNYsWwaY1LizwVP3n/DcgrOVnPwZB71jb2rCf6eksfXtdmf6/ZnnW9sbePlM6nO6+AiKTqaitpG7pizgA8uXEpTWys78nO5//A+9hbl++x1z9XyYTQVTzDV52Cj4In0mwIpMhgURPEDdxueCj+ekJBhqaswCgyPjbDh3hWl1bZxsiadkzXp3nuScWG4P/NJZsdPZVHSZC4ffS1Xu27kjZKXeLbgX4SbCK4ZexM59RnkNmTS4K7v8TWGGgVRBonqnQyQP7uipMTG8N1rria1rIxNZ1IpqKmhsdU5SDM1OZkvXb4WgC2ZmQAkx0TT2NRGdVMz0c3hhNe5LpieJzo8nIWjxtDibiOj0gn4JUZGUdPi54MwnYMoEFRhlM4nkiDwJ5O6E8rB3lA+EVXeUsXjeS/zeN7LJEUksDx5ActGLKC0yeksdN3YW5mVsIBjVfs5Wn2A6tYgqyuqdcOecVsiq1sHdJKpuKSaF189youvHj13XyRAnZvkhBg+/u7LcLkMbW4PpzOL+dT3Hj03ZU5Prlw5i3tvX0NldQOvbD9JWHUbYcD0lASS42MoqaxlbHICTS2tVDc00drmprSqntKq/u0bDIdpecB/QZTiujqeOHbc+5ouHtpziJyzVayZMZn3z7+Ej16ygqyqSjY88mcA5qSMIqe6ima3D+t+kARcg6kbikIoqNYJADXltXzvjp+RkBLP+jtWsXTjUirPOn97t350Pe42N9tfPExNZc81ZObCiYyZNILdrx+noqSGipIacs4UAUdIHBFH8ugEclOLu3zuYIRQIDi6odTVNbN56yk2b3XCJokJ0UTWuvGkRPDRd19GYlw0bW4PpzKK2Xsshy17U8nI692xLX90Q/FnbQ7lKXmqm5r43HMvEBkWxqpJE7l65kyumT2DxCinzs1MTuFd02fxWnaGb6baC0AdD1TN7u5cyIAMkXqn4In4nAIp4k+dgyigMIpIMAvlkygD0VUYBULvZEx/ebDsKDvAjrIDAIQZF1NjJ9LgbqK4KZnVKUncOP5OXMbZuTzbVER2fQZvnd1EXmN2AEceGAqiiIQOX4VRwoyLqLAwPnXppVw/Zw6nzp6luLaO0fFxrJw4kcnJSezIzuHxo8cAqGxopLi2lnVTpzAmPg6A6IZwPNbisZbFE8YyPTmFE2UlnCxz9rt+d90tLBg1hkMlRRwsKeRQcSFHzhbT0Na7q9X7LUhOGnUn2K5q7kkoTs0Tyt1Qqltr2VK6hy2le4BYIJbCJsvqkaN435SP8j4+SnZ9Ogcq3uat0lcCPVyRC/iq5X5HL712lJdeO0pcbCQL5k1g8YJJxI+KvWjoJDYmkvdet4x7b19NfUMzDz2/lwMnz3cQmjHJ2fbduHwOs8aOpKS8hrcPZ/HS8TQam1vfMb1Of3U+CQX+OWE0VIMoZ0rLOFNaxv179xEZFsbS8eNJjD5fQx++5b0kRkZxoKSQnfk5vF2Qy5Gzxbh98ctrFwTbFAqhXFwoB1Al9NRW1PHSn7fw0p+3nLtv7fWXsGTtbD79/Ts58NYptjy7n92vHae56Z37PUvXzQGg8mwNE6aNJjzCRVFuOa3NbdRU1l80uNLOXyEUCM5uKDW1zvSvropW7vjAb1k4byIrl01l+Ypp3HvHahqbW8jIKyMhLop3rZvPnqM55BX37gS6v7uhgO9qc6hOydPidrMzJ5edObn88M0tGJyOKGsWTOa+Neu5b816sqsreT0rg83Z6ewrLuhVyPiihtF0PHKegicyaBRIEX9RVxSR0KLuKBcaDgdm3NZDZv35g817Kqr50J7/ZGb8VGbHT2VOwjTmJS7k7fK3AFiQuIR3jbudjLozZNSdIas+dVh1RVEQRSS09CeMUlJXx3c3v8m6qVPYMHMGl0+bSkJUFK1uNzlVVTx+9Bj3791HblU1BnBbS0ZFBcYYpo5Idl7D4xygS4yK4n2LFwPw6qk0wupcGODZo6conlLHsknjuXbaTAB25OfwwReeAOCaqTNIr6wgp6bKJz+HboVYV5RgPkClIMrga59acFLMWFaPXMoVoxYyO2HBueDJlaPfRXZ9OrkNmVj8Nz2XSF/5MohS39DCvoPZ7DuY7ay70+NtyeHnTk5cMnciH7tjDasWTSUjr5RfPrSFQ6cubOXe2NzK357dTWVNI4tmj2fhzHFcuXI278ldxq8e2sKh0+eX99UJqHaDERIZrMBLO39eKd2uxe1mb77zewnHhcsY/uvl11g7dTKXTZ/Cf66+AoD7D+/jx29vJcwYZo8YyZmKMt++MwY4iBJM2wzBuk3Q3UU4Iv5038YfMmPxZDbctZar37+O1RsX8eyDb/Gn7z/zjmUnzx5LY10zN9yzlvW3LKcot4xtzx/kxb/v6DKo0hsdQygw9LuhtLV5OHI8jyPH8/jzP3YQH++8N0bWuVmyfBJfufcaAArPVrP3WDZ7juaw52h2r6br80c3FPBf15JQnZKnfU2P7T3G1pNZbJg5g2vmzeTDi5fyoUVLWPbX39PY1sqM5BEU19X55uIVhVCGDQVPJOC6CqQojCIDoa4oIqFpuAZShmsYpcHdxLHqMxyrPtPpkWRGRcUCsGHMDWwcdwsAhY15/Dbtf6htqybMhOG27kEeceAoiCLSP8Zz/kCQv68+7qw3B4mKamt58vgJnjx+AoCk6GjGxsfT0NpCfvX5ucrbn/nCydO8Z9FCvrRuLa1uN2/n5lHf0sInVq3kzsUL2ZaVzaYzaeee89zJUzx38tS5dS8ZP45Wt/PeGRsewf3X306Yy0VZYwOHvB1RNudkkloxCO8xQXAFc3dCeXqeUNhmCtUgSn5jCfn5r/JU/quEmzDabDJz4tu4feIHCHeFU9VSwbHqAxyp2k963alhtZ0iocEfHVHahVe1MTIlnltvWML7bl9FdHQEf3nmbf75wj6aW975/r5tfzo7DmTgsZYnXztERHgYn3rfOu6+cSWfuutyvvXrFyjzTrfjrxNQ7QIxPc9ghVDAf0EUj7Vszcpia1YWvAUjYqJZM3kyWZXO1e1Lxozj6XffQ1ljA2/n5/JWXhZbc7Moa2zw7UAG+SRWZ8F0UitYu6GIDJbMY3lkHnuMv37ncRZfPo+ywq67bbz13EGeeeAtXGEu5i2byvpblvGxb9zKxveu5oef+DMFWaUDHkuodkOB/k/L027vllTuPnU/l66YxvJLZ7Dxsnncfs0S7vrKX8gtqmTWlNHEREVwMqMIt+fiNSqUpuUJ1W4oxXV1PHLkKI8cOUpcRATzxoymtcoN8fDLa25i3shR7MrP5fXsdDZnZ3C2wQcXBiqEMqQpeCJBSd1RxNfUFcX/jDGTgB8A1wMjgSLgWeD71trQn5xOAmY4TtcTqidlfOVw1SkOV50i0hXB7PipzE+cyaKk8dS1OSdi3zPpIyxIXEJG/Wky6lLJqDtNSVPhsLnSuGMQRSGUwaVaF7oGuw1+Z73pilLd1ER1U1O36zhbX89XXtrEj961kf9cfwUldXWkxMQQHRHB6bOl/HjLW+RUVXX53OqmJrZlZTtjwUUrbm596J8smzCeZRMmsHTSeDZOm0WTu43UijJGx8bx7yvWcLC4kIMlheTWVPf7e+8VdUXxiVAPooTK9k6bN1SSWhfOvXv/i5Upi7g0ZQmXplzBFaM38kjOg+wq30Kky/m7afEE599Ld1TrhofOJ5qg/2GUK9fN4f13Xsq82eM5fjKf/3vgTVLTS3C5DJEeS0tS2Due094hxRhobXPz239tY+aU0Vy6aCqL50xgy960Ll9rKARRBnObZLCCKJWNTWxKdX5n4bjIbavmvpdfYe3UKaybPpVbZs8D4N1PP8zBkiLiIiJoamsbUtPyBNO2gkIovaN6NzR5PJYj205dcJ8rZcS5zw9uPX3u84zj+bzx1D4++vVbuPnDl/Oh/7yRn37+ITy9CET0Vih1QwHfBFEKi6t49qXDPPvSYcLCXMybM47i02WQFMYHblrBDVcspLa+if0nctl7NIfdR7MpLqu5+Irx/7Q8odgNxZe1vb61lQMFhd7XcPGTN7aycfYsrpk9kx9feR0/vhL+cvQAP9i55SJr6gOFUPwqELVOwRMJKeqOIr6irii+ZYyZCewCxgDPAaeBS4EvAtcbY9ZZa/UDFp8ZbmGU4doVpcXTyomadE7UpPNkPkASAHsqcjBmBIuSFrIq5XIA8hqy+OnpbwEwOmosFS1lw+JqY3VDGTyqdUPLYLfB70p/pug5ebaUjz/1DKsnTWLlpElEhoexLy+fNzMyqWtp6fVrW+BMaRlnSst49MgxAJKjo/FYS3izi5kTUnj3nIV8eNEyAEob6jlUUsjP9uwgrXKQ/sxDqCtKsB6wCrUTT6EYvK13N7K1dB9bS/cR6YrgkqS5pNZmkxgBK0es5T2TP8yZ2hMcqdrH8epD50K0wUq1bnjr68mmmOgIPnL3Wu64eTkej+X+v23lkaf2nnu8/cRdZPWF2+QdgygdcweHT+WzatEUFs+ZwFv70uhNJkFBlL4ZrCBKVVMTT584ydMnTmKA+WNGs376NI6XOsdwP7d8DfcsXML2vGzeynW6oZSqG4pfhNq2wGBRvRtePBXnz612DKG4XIamhhae/MObLF07h6Xr5jB59jhyzhT5bSyhHETpTwjF7fZw4lShd11ufvfbN9i9I51Vy6axcuV0Nlw6h5zCCt7/1b8CMH/GWHIKK2joxbRH/u6GEoohFPBtbd+XX8C+/AJ+vGUrs0eO5JpZMzhT7xx3HB0bx5O3380bORm8mpnGvuKCc8HiflMIxacCVesUPJGQpzCK+Iq6ogzI73EK2Bestf/Xfqcx5n+BLwE/Aj4doLFJFzxuF411UUNqo0phlNA4QeMLO8oOsKPsAADjokczP3EmYeb8DuYX53ybmLBYsuszyKw7Q0bdGbLq02j2dN9JYKhQEMWvVOuGuGAMo3R10KiioZFNqWnnrixuZ2BAfZ+qOnRb2Z9awPK03zFn1EiWTZjgdEaZNIGmNufgz51zF3Ln3IXsK8pnb2E+h0qKfDPvc08URBkQdUPxvxZPK/srjwNQ05bM3oqzxIVvZ92oxSyaugyP9ZBZl8rv0n8S4JH2SLUuxBiPx+dt99tdLIgydfJI3n3rCiLCw8jMLiUpKZZbrl9CSWkNRcXVVFbXX9B+3+UyeDz2giCKMdCcGEZ4mIvWNjcGg8vlOhc6GZUcd27and5QEKVvBiOIYnGCsyfPOtNXhONid3oeYyLiWD9jGjfPcrqhvF2Qy93PP+7z1wcCvg0RLCe2FEK5gOrdMNUxhOIBwkePpLSwkvyss0yaNYYps8eeC55ERIbR2uLfC5oGa1oeGPg2gi+6odTWNfHWjjO8tcOZdnvyxBRSRsQB4DKGX/3XncRGR3IsrZA9R3PYeyyb01klfQ6jakoehy/relp5OWnlzvmycFyMiIwmvbKcexYs4WOXrKC0oZ5Nman88dBeCut8sO8WoBBKMO7LD0BAap2CJzIkKYwivqAgSu94k5PXAdnA7zo9/F3gk8CHjDFfsdb6YBJA8aXOJ0pgaG1gDfcwSiicpBmo4qZSipva5+BNxoXhjxlPMj9xJkuTpnHduNtwGRdvnX2Vp/IfwoWLeYmLyaxLpckzdP8W2imI4huqdcNXoMMofemK4utTRR5rOV1axunSMh45cvT8mHBBIyRGRvH55WsIW+mizePheGkJ7332EVo9HsJdzn1+FSJBlGDdrgq1IEqohVAAshsK+Gv20/w1+2mmxU7k0pFLmBmXRJv1c0irn1TrhgZ/XO3crvMJp7TMs/znt59gxdKprFo+jTtuWkZkZDitbW6KS6rJza/g2Ml8Xn3jBFXVDaxaNo3auiZOdriS3FrnCugpk5K4dN5k3G4PmXnO9mpKUiwv/P7TFJ6t5tCpPA6dzufQqXwKz/Z++jcFUfpmsDqi7MrJZVdO7rluKFdOn0547PlOOI/f/n7O1tcNyW4owRJWHc4hFNW74SkhJZ7aigvfUI0xtJU6x/unznTOJ+WkFp97/D2fuYbbPnYlqYdzOX0oh9TDOZw5nENtlY/fk9rH48duKBCcQZS8ggryCiqIxAmofudHz7Fy2TRWrprOp++6nE/fdTl/e3Y3f3p8J2EuQ0x0JHUNF3/fVDcUh79CKACZFZV86onniI2I4KoZ07lh7hzunLuQ3x3cA8CyseOJDgtnT1F+SHVCCYV9+d4IZK1T8ESGja7CKKBAivSepufp1gbvx9estRds1Vlra40xO3GK3BrgjcEenPSdwihDx3DsiuLBsrv8MLvLDwMQHRbF3PjpVLY6B6gnxU7jM7Puw23d5DVkkVZ7irS6k2TUnaHFM3T+zrvTMYiiEEqfqNbJOYN5Aqgrg3VSqCfPnTzFcydPER8ZyfIJE1g5aSJjU+Jp9YZN/vSu25ialMzeonz2FRWwryif/Fo/TzHSOYgCQRFGCZYTTBcTSiegQjFom91QQHZDgfer5EAOpSeqdUNQ55NM4LswSlhFM6d2ZnJqZyb/9N43et5oliyazLJLprBw/gTGjUnk5decKd3WrZ7Fu65ZxO79Gezam0FufgXV1Y1ERYXz8Q9ezqpl09m9L4Nje7KJrHZj3K385o+bWbJ4MmuXzeCmKxcB8L3fvcyrO0+RGBdNYkI0+cVVvR6zgih9488TVtB1N5Rwl4ucsqoLuqEcLy3hD4f28lLGGZ+PIdBB1mA4wRVK2wA+ono3zETHRXHvd+4k90whW5/aQ9VZZ7/Eek+Gr3/3pYyfMYaygkqyd58697zT208yelQc89bM4Z6r5uFyuWhqaObOBf+Fx+1h8ZqZtDa3kXEi3y+dUQYziBIM0/J4PJZDR3M5dDSXB/6+jaTEGFYtn05mlnP+7pI5E/nNN9/LsdQCdh7K4u3DmWTm9+7cTCh2QwmVEEpDaysvn0nl5TOpRIaF0eJ2E46LT7/rUt41YzalDfW8mpXGSxln2FuYj1shlMESsFqn4IkMe+qOIgOhrigAzPV+TO3m8TScIjYH7bCFLIVRho7hFkZpcjdzpPq096tkSppq+M7xX7MoaQ4rRkxnw5gb2DjuFn6b9mPO1J4I6FgHm7qh9IlqnXQrlLqi+FpdSwvbsrPZlp3tjAXnIN6O9BzMbMPNM+fygQVLAHglM5VPv/r8oIzr/ACDrytKKARR1A1l2FKtGyb8GUYpPV3K5tOlbH7yIABtIyLxeJya9OqbJwiPCGPNqhmsXT2L2tomWtvcjBmVCEBqejEPPLSdgqIqwGnH/9QLB3nqhYMY40zts3TxZI7tyQbgylWz+cYnr6OkvJafPPAau49m93m8Qy2IMhS6obR5PHz9ldeA9m4o07hi1jTCXc7vZlJCEqsnTOKpM37adwuSbigKofiV6t0wExkdQcq4ZK6/90ouu3k5x7afJi+1iJamVuZfOpP3fukmmuqbeeGBC3/dBzYf48BmJzwZEx/N7GXTGD0phbbSclwpI/jYN25l3rJptLa0kXmygG/e8wfqu6ixvuLPIEowdkOprmlk81snnfUBlTnVPPLkHlavnsnnP7Cez39gPUWlNXzhx0+QX1LV6/WGSjcUf25D+Kumt7jPB7C++vwmXph+mhvmzuHdcxbywYVL2Zmfwz0vPOGT1wIUQulZwGqdsQNNFw0xxphawA/xab8YBYTCGQKN07dCZZwQOmMNlXHOtdYOeIvSGPMKzvfcG9FAU4ev77fW3t9pffcDnwA+Ya19sIvX+xHwDeAb1tr/6d+oxddCqN6Fyv+nxul7oTJWjdO3fFLrwLf1TrUuNKnW+UWojFXj9C2N0/eCbt9OtS40hVCtg9D5H9U4fStUxgmhM9ZQGWfQ1Trv+lTvQlAI1btQ+f/UOH0vVMaqcfqWjmN2oo4n73TGWrsy0IPoDWPM/lAYq8bpW6EyTgidsYbSOH2xHmvt9b5Yj4S8kKh3ofT/qXH6VqiMVeP0LV/VOlC9E0C1zudCZawap29pnL6nfTvxoZCodRA6/6Map2+FyjghdMYaSuP0xXpU68QrJOpdKP1/apy+FSpj1Th9S8cx38m3/QNFRGQ4qvZ+TOrm8fb7q/w/FBEREb9QrRMRkaFOtU5ERIYD1TsRERnqAlbrFDwREZGBam9zOKebx2d7P3Y3n5yIiEiwU60TEZGhTrVORESGA9U7EREZ6gJW6xQ8eaf7L75I0AiVsWqcvhUq44TQGavGOTBbvB+vM8ZcUFeMMQnAOqAB2D3YA5MeBevfU2cap2+FyjghdMaqcfpWsI5TtS40BevfU2ehMk4InbFqnL6lcfpeMI5VtS40BePfUndCZawap2+FyjghdMaqcQ6M6l1oCta/p840Tt8KlXFC6IxV4/StYB1nwGqdsdb6ep0iIjLMGGNeBa4DvmCt/b8O9/8v8CXgT9baTwdqfCIiIgOlWiciIkOdap2IiAwHqnciIjLUBarWKXgiIiIDZoyZCewCxgDPAaeA1cAGnHZda6215YEboYiIyMCo1omIyFCnWiciIsOB6p2IiAx1gap1mmqnA2PM9caYM8aYdGPMfwV6PN0xxvzFGHPWGHM80GPpjjFmsjFmizHmpDHmhDHmi4EeU3eMMdHGmL3GmCPesX4/0GPqiTEmzBhzyBjzYqDH0h1jTLYx5pgx5rAxZn+gx9MdY0yyMeZJY8xpY8wpY8xlgR5TV4wxc70/y/ZbjTHmPwI9ro6stRnASuBvOMXrK8BM4NfAGu2sBQ/VOt8KlXqnWucfqne+o1onvhYK9U61zrdU6/xDtc53VOvE10Kh1kFo1LtQqXWgeucPqnW+pXonvqRa51uhUu9U63wvVGodhEa9U63rnjqeeBljwnASPhuBfGAfcLe19mRAB9YFY8x6oA54yFq7KNDj6YoxZjww3lp70DjzRR0Abg/Sn6cB4qy1dcaYCGAH8EVrbVDO42iM+TLOm0WitfbmQI+nK8aYbGCltbYs0GPpiTHm78B2a+2DxphIINZaWxXgYfXI+15VAKy21uYEejwSWlTrfC9U6p1qnX+o3vmHap0MVKjUO9U631Kt8w/VOv9QrZOBCpVaB6FR70Kl1oHqnT+o1vmP6p0MhGqd74VKvVOt871QqXUQevVOte5C6nhy3qVAurU201rbAjwK3BbgMXXJWrsNqAj0OHpirS2y1h70fl6L08JnYmBH1TXrqPN+GeG9BWUiyxgzCbgJeDDQYwl1xpgkYD3wZwBrbUswF68OrgEyVMCkn1TrfCxU6p1q3fAVovVOtU4GKiTqnWqdb6nWDV+qdTJMhUStg9Cod6FS60D1brgK0VoHqncyMKp1PhYq9U61bvgK0XqnWteBgifnTQTyOnydTxC+4YYiY8w0YBmwJ8BD6Za3FdZh4CzwurU2WMf6K+A+wBPgcVyMBV4zxhwwxnwy0IPpxnSgFPirtw3ag8aYuEAPqhfeDzwS6EFIyFKt86Ngr3eqdX6heucfqnUyUKp3fqJa5zO/QrXOl1TrZDhSrfOTYK91oHrnB6p1/qN6JwOhWudHwV7vVOt8LhRqHYRmvVOt60DBE/ErY0w88BTwH9bamkCPpzvWWre1dikwCbjUGBN07dCMMTcDZ621BwI9ll643Fq7HLgB+Jy31VywCQeWA3+w1i4D6oGgnScSwNtW7FbgiUCPRUQuFAr1TrXOL1TvfEy1TiR4qdb5hmqdX6jWiYhPhEKtA9U7P1Ct8wPVO5HgFQr1TrXO50Kh1kGI1TvVundS8OS8AmByh68nee+TfvLOvfYU8LC19ulAj6c3vC2btgDXB3goXVkH3Oqdi+1R4GpjzD8DO6SuWWsLvB/PAs/gtMULNvlAfoek7JM4BS2Y3QActNaWBHogErJU6/wg1Oqdap3vqN75hWqd+ILqnY+p1vmUap3vqdbJcKRa52OhVutA9c5XVOv8RvVOBkq1zg9Crd6p1vlGiNQ6CL16p1rXiYIn5+0DZhtjpnsTSu8Hng/wmEKWMcbgzMF1ylr7v4EeT0+MMaONMcnez2OAjcDpgA6qC9bar1trJ1lrp+H8fb5prf1ggIf1DsaYOGNMQvvnwHXA8cCO6p2stcVAnjFmrveua4CTARxSb9yNWnbJwKjW+Vio1DvVOt9TvfMb1TrxBdU7H1Kt8y3VOt9TrZNhSrXOh0Kl1oHqna+p1vmV6p0MlGqdj4VKvVOt861QqXUQkvVOta6T8EAPIFhYa9uMMZ8HXgXCgL9Ya08EeFhdMsY8AlwFjDLG5APftdb+ObCjeod1wIeAY9552AC+Ya19OXBD6tZ44O/GmDCcMNbj1toXAzymUDYWeMbZhiEc+Je19pXADqlb/w487N1wzQQ+GuDxdMu7QbAR+FSgxyKhS7XOL0Kl3qnW+Z7qnY+p1omvhEq9U63zOdU631Ot8zHVOvGVUKl1EDL1LlRqHaje+ZpqnR+o3okvqNb5RajUO9U63wqlWgchUu9U67pmrLWBHoOIiIiIiIiIiIiIiIiIiIiIhCBNtSMiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYiIiIiIiIiIiIiIiIiIiIj0i4InIiIiIiIiIiIiIiIiIiIiItIvCp6IiIiIiIiIiIiIiIiIiIiISL8oeCIiIiIiIiIiIiIiIiIiIiIi/aLgiYgPGWOyjTHWGHNvoMciIiLiD6p1IiIyHKjeiYjIUKdaJyIioU61TCS4KHgiPfK+Yff3du8gjTHZGPM97y15MF4z0IwxicaYrxljdhljSo0xzcaYfGPMloH+HIwxCd51HDPG1Bljqo0x+4wxXzHGRF7keb80xuQaY1qMMTnGmJ8bY+J6eE6UMea093Wm9nfMIiIDoVoXPPr4s98ygNcZa4z5hTHmjDGm0RhTYYzZboz5N2OM6eF544wxfzbGFHtrXaox5lvGmPAenjPKGFPmrdOJ/R2ziMhAqd4FH2PMRmPM4959pyZvTco0xjxsjLnSB+vXvp2IDCuqdcHHGHOFMeYx7/5QszHmrDHmdWPM3T5av/btRGRIUS0LHsaYWGPMDd768LR3v6j9Z/29PqynX7WqD+ufaYz5kzEmy7tfWWqMedUYc+dFnjfbuz9a7n3eMWPMZy7ynLneZQ/1VDNl+NIfhVxMSTf3xwNxF1mm0ffD6VIy8F3v538DqgbpdQPCGLMBeAQY672rBWgAJnpvVwHPAof7se6pwFvANO9dDUAUsNJ7u8cYc421trLT8yKBzcCl3rvqgSnAV4DLjDFXWWtbu3jJbwBzgS9ba3P6Ol4RER9RrQse3f2c20UAKd7P9/XnBYwxK4BXgZHeu+qABOBy7+09xphbrbUtnZ43AtgJzPDeVQ/MBn4ILAHe281L/sL7Wu+21tb0Z8wiIj6iehckvAcX/wB8qsPd7T/j6d7bB4wxv7TWfrmfr6F9OxEZjlTrgogx5ifA1zrcVYXz/V8LXGuMeS/wPmttWz/Xr307ERmKVMuCx6XAywNZQX9rVR/WfyPwBBDrvasG59jpdcB1xpi/Ah+31tpOz5sO7PYua3H+dhYBvzfGzLDW/mc3L/knnOOzn+hv/ZahTR1PpEfW2nFd3YCfX2wZa+1jARz6kGSMWQe8hBM6eRpYBURba0fgbHRcCvwIqO7HusOBF3AOTBYBG621cTgF6/1ALbAM+GcXT/+g97VzgcXW2njvsoXAWuCeLl5vHvBfwEHgN30dr4iIr6jWBY8efs7tv5Mfd1j8z31dvzEmCXgRZ2fvNLDKWpuAU0M/D7QC7wJ+1cXTv4RzYPIIMNVb667G2WF8jzHmqi5e72rgw8Bz1tpn+jpeERFfUr0LKvdyPnTyJDDHWhtrrY0F5gHPeR/7kjHmjr6uXPt2IjJcqdYFD2PMpzgfOnkUmOw9fpmAUwfrgTuAn/Zz/dq3E5EhSbUs6FQCbwA/A+4Ginv7xAHWqt6sfzrwOM5+3k5grrU2CUgCfuBd7KNAVyGS7+OETjYDo3GCTXcBHuDLxpgZnZ9gjPkYcCXwf9ba/f0Zswx9Cp6IhAhjTCzwEBCD88Z+p7V2f3tS0VrbYK3dZ639lrU2qx8v8RFgsffzO621m73r9Xg3WNoPjN5ojLmm03M3ej/+1Fp73Pu8w8D/895/XafvxQD3A2HAJ6217n6MV0REhp+Pez/usNae6cfzvwqMw0nx39i+k2StbbHW/o7zV2t80hgzp9Nz22vdN621ud7nbQEe8N7fudZFA3/EObn3+X6MVUREhq4Pez+mA3dba9PaH/DWt/cCmd673teP9WvfTkREAsYbgPy+98uDwD3W2nwAa22ztfbvOPtmAP/e1cmtXtC+nYiI+Nt2a22KtfZaa+191tpHgeY+PH8gtao3foATYikGbrbWpnrXX2et/S7OfhrAN73dvjpqr4X/Ya0tt47HcS6CcOF0JzvHGDMaJ3yTB3yrH2OVYULBE/EbY8xoY8x/e+f6qvbO+5VpnPkzF/bwvEnGmU/6hDGm3jjzfxYaYw5471/VYdm3gI4hi6xOc9q91cX6I40xnzXGbDHOvJwtxpnP8zljzA0X+Z5ivPO5nfTOxXbWGPNyFwfr/OFDOGn8YuA+P6z/I96PW6y1b3fx+KOc/1l/uNNjo7wfMzrd334AdXSn+z8OXAH8xlp7oB9jFREJCqp1g8cYsxaY7/3ywX6upr1+PdpNSPP/cK5yC+OdV3T3tdZ9C6dd87faD7KKiIQq1TufG+/9eMR20Z7YOlPZHPZ+Gd+P9WvfTkSkj1TrfGoF56cI/4W11tPFMg/gTM0QjtNtq6+0byci0olqmW/5IFQ/kFrVI2NMHHCn98s/WGuruljsf7wfE4HbOz3W11r4S5wOKZ+31tb1ZawyvCh4In5hjLkWSAW+CSzF6dLRhjNX9ceAg8aYzge4MMYsAY4C/wEswJmDuh4nFbjce//nOjylAijr8HUZzvx27beKTuufipO0/x1wFc4bZQPOztCtwMvGmD908z2l4LSr+iHOia9wnLnMbgBeN8Z85iI/k+91KL7Telq2G+0/ryestU39eH63jNNNZZ33y01dLePtrPKK98vrOj1c7v04s9P9s7wfSzu81hicNpq5wLf7OWQRkYBTrevy+QOtdT1p73ZSjTN3aZ8YY+YCU7xfdlfr6oDt3i8HUusW4oRE9wG/7etYRUSCiepdl88faL1r72ayxDhXhXdefwTOzxqgTy2MtW8nItJ3qnVdPn8gtW5qh89PdrWA92ReqvfLzrWoR9q3ExF5J9WyLp/vz+OUPfJBrbqYy3F+xz2tPxs41c36+1ILN+IEY5621j7fx3HKMKPgificMWYx8DyQjJNeXwDEeOfLnAr8HogE/myMWdnp6b8ARuAUosuACGttChANKyAsdgABAABJREFUzMFpTXWifWFr7buBVR2ev6rTnHbv7jCuOJyDawuBt3CKXIy1Ntk71i/jpAs/bYz5Yhff2oM4c1s3A58GErxzk04DngV+zTtTgD5hjIkC2n9WB4wxU4wx9xtj8rzp0BJjzAvGmJv6+RLzOf9+cLyH5dofG+ct+u1e9368rz056/07uK/T4+DMVzcC+Jy1tr6f4xURCSjVusFljInn/FQDj1hrG/qxmkUdPu9NrVvQ6f72WvbfxpjJ3nGtBz7R8XFjjAH+BBicKQe6urpPRCQkqN75TfuB1VnAI8aY9oN77QcoH8fpdpmBc2VZX2jfTkSkD1Tr/C6sF48t6mGZrmjfTkSkA9WyoDTQWuXr9XfueNNeC39pjEkxjjuB2wAP8AY4HWdwppurAf69j2OU4chaq5tufb4B3wMs3oulOj32hvexH/fw/F97l3m20/0N3vsv68NYprWPBZjWw3Lf9i7zFk7x7GqZO7zLlALhHe6/tMNrfKyL54XhJBPbl7m3p59ZT+PsZlxzOzz3+zhv8han4FZ2eMzibFiYPq7/lg7Pv6SH5W7rsNyiDvdH4ST/2x+r7fD5biDSu9y7vPc9Eei/Yd100023i91U697xPL/Wuot8///WYb0r+rmOf++wjsQelvtih+XiO9yfAmR3eKyuw+dPd1juU977fhbov2HddNNNt97cVO/e8bxBqXc4Vw42d1hPQ4efWSXOweGUfqxX+3a66aabbp1uqnXveJ6/j2N2fP2PdLNMJE43y/bl4vqwfu3b6aabbsPuplr2jucF5Dhlh/rxvYssN6Ba1Ytx/ML7nIqLLPdL73Jlne6fyflzix6cLjft4/hlh+X+x3vf5wL1t69baN3U8UR8ytuu6mqcFl4/72HRh7wfrzXGdEy+V3k/jsf32lv0/6915szuyrM4oY5ROPORtnu/92Me8NfOT7JOe8gf9vTi1trvWWuN95bdh3GDkzht9y2gFXgvTiEagZNabZ924N+AL/Vx/QkdPu/pKvKOj517jrW2GbgW+A1QgJOGzce5Am6jtbbFm4z8A85O5RcAjDErjDGvGmNqjTF1xpjXukjciogEFdW67g2w1vXk37wfj1hrD/RzHQOtdRXAWuDvwFmcA6UZODuxdwEYY8YBP8HZCf2u976NxpgdxpgGY0yVMeYpY8zsfn4PIiKDRvWue76od9baXwHvxqkp4LRJbm+VHAnEA0n9WLX27UREekm1rnsDrHUHcaZbAPhaV9PK4ZyQS+zwdWIXy3RH+3YiIl6qZd3z43HK3hhQrerD+i/WFbr98QvWba3NANYAT+P8DYTjdLb5Ak4nmvZOOl8F9uDt2mmMudsYc9AY02SMKTXG/NVbM0UA5w9JxJfa55J2ASedjoRdai9sccBIzh9sexGnreHfjTHrcNqD7bP9a6l/jjFmIufnF/2zMcbdw+Lx3o9Tcd5Q4fw0N29Za203z9uGU9z98X/l6vT5x621z7bfYa3NNca8H6f12RLgG8aY31hr2/wwli5Za6tx0plf7GaR7+LMJ/hZa22R9yDkNpydu+dxUpO3AduMMeuttX2ay1xEZBCp1g3iNqS3zf9q75cPDtbrdsVaWwjc28Miv8JpFfoBa22DMeZWnB24ZuA5nCDpu4H1xpgV1tpcvw5YRGRgVO/8VO+MMbE4B0/fB+wHPggc8j68DPgx8CHgBmPMNdbao/4YR3e0byciw4hqnR9qnbW2zRjzA+B3OFPAvWiM+QZOu/8UnBr3I5wL6yK8TxvUaWy0byciQ4hqmc5194u19gxwZ1ePdZhuDuAT1lqPMeZzwG+BCs5PD3svcLm3Ftb4f9QS7PTPKL42wfvRBYzt5XNiO3x+H8481xtwUnVfBtzGmMPAS8D91tqCAYwLnORkX8c1xvux29e21jYZY8rp/ffdF7UdPk/rGDrp8PoeY8zPgX/gbDis4HyR7sv6Y7td6sLHartdqhNvMvIrwC6c+eDAae0Wg9MC7e/e5f4NZ6qgX3N+g0lEJNio1vmn1nWnvdtJE/DPAaync63rbmeov7XuBpyr4x611m7yXj3yfzg79jdaa7d6l/tv4Js4rSrv6f3wRUQGneqd/+rdz3BCJ2eAK6y1TR0ee90YswM4jHNhwe+AK/qwbu3biYj0nmqdn2qdtfb3xpjpOFdKv8t76ygN56TVN71fV/Zh9dq3ExE5T7VscI9T9pZfa1WHZXva5+v4eF/WDfBp4DLgJ9baY8aYZOCnOAHMy6y1qQDGmH/gXEjxNc7XdBnGNNWO+Fp7arKkQwuri92y259sra2y1l6Nc2Dtp8BOnMTiCuA7QJox5u4BjAtgfi/H9bf+/AD8pGNxPd3Dcic7fD6126XeqbDD5xN7WK7jY4XdLtWBMcYF3I9z1dunrLXWGDMGp6VlKU5Qpt1fgTJgrTFmdG/WLyISAKp1g8QYE4mz8wLwlLW2agCr62utq7HW1vVmxd4r13+P05ryP7x3rwCmAAfaD0x6/RynJt7irZEiIsFK9c4PjDEJwCe9X/6uU+gEAGttI86VZOBcPTam8zI90L6diEjvqdb5kbX2P4HLgb/htO/PA/biTCO+DGi/+j3HWtvSh1Vr305E5DzVsuDkt1rVaf0jvFOhXmz9vdrnAzDGjMcJVWYCP/DefR1OiOVF6w2deLVP73R7b9cvQ5s2iMTXir0fRxlj4vq7EmvtDmvt16y1l+O0NbwNOIZzFdVfjDF9TTAWd/i8L4GMdu1tx7otEMaYKJxOIz5nnblHe5Mq7dhHrbv2Y105xfmWlot6WK79sWLvmHrjMzhzxf3MWnvce98M78csa+25VprWmZcvq9MyIiLBRrVu8NzG+asiBjrNzvEOn/em1p3sYZnOvg9MA+6z1rbPZd5exzI6LugNz5ThzK2qE3EiEsxU7/xjDue7z2b0sFxah8+n92H92rcTEek91To/s9butNZ+1Fq7yFo7xVq72lr7I2ttPeenUdjVx9Vq305E5DzVsuDkz1rVn/Wf6MO6fwMkAZ/xXhQB3dRCzu+3ap9PAAVPxPd2ej+GATf4YoXW2iZr7fM482YCROOk5dt1nAO0ywnsvAnO9uDGLf0YRvuc1Fea7ifJW49/p696zftxfg/LLOjweVa3S3Vinfn62n9313e1jPf7bm+L+VpXy3TxnAk485OnAz/sYpGukpg9pTNFRIKBat3gaZ9mJx3Y2tOCvZAKtM+73V2ti+P8dAa9rXVLca6E20HX4RjVOhEJVap3/tHxe+zpAGzHA7u9bousfTsRkT5RrQsQ7wnMa71fPtTHp2vfTkTkPNWy4OSXWtXBDqA9FNLd+qdy/nxib2vhzcB7gIettV09p3PdUx2UCyh4Ij5lrU0D3vJ++SNjTFJPyxtjUjp8Hn6RtoSNHT7vWNg6zo2W3MPzH/B+/LgxZllvx+X1mPfjFOAjXSzvwmkT6U9/9X6cZYy5vZsxfNX7ZQFwsI/r/7v34wZjzOouHn8v51OLvd0h/D8gEScZ2bGFdHsoZrYxZkT7ncaYkTjzCXZcRkQkqKjWDQ5jzBTOH4j8i7W2L5283sH7/Pb69X5jzLQuFvscEI/T8vnhXoyxfcoBD/DJTmNsr2PLjTHhHZ6z0PsatTjTEoiIBCXVO785zfnv/9861ogOYwjj/HQ8lcCZPr6G9u1ERHpBtS4wvHXuj0AkztQ7r/bl+dq3ExE5T7UsOPmjVnVafz3wlPfLz3Tze/+a92Mt8OzF1ukNwvwOqAC+1Onh9lp4aaf713g/Zl5s/TJMWGt1063PN+B7OFO52C4eW4TzRmZx2vzeBkR3eHwi8CHgDeCBDvdPw2nT1D7PZ3iHxy4BtnjXWQekdHrNfO9jv+n4vE7LxANHvctVAZ8HRnZ4PBknEfoQcKKL5z/nfW4j8Akgynv/FOAJoAWo9y5zb08/M2BaP3/uT3ifXwbc2f69esfwWIf1f6SL597b4fGrung8vMPPJx+4xnu/C+fAZLX3sZd7OdZbvcs/1M3ju72P/xNnbrhY4F/e+3YF+m9cN9100021LjC1rot1tQLj+/o76+r1cdpEFnkfPwGs8N4fiTN9QLP3sd/38vW+4F3+B108Ft7hd/YT72ukAG967/tXoP/GddNNN92sVb0LRL3zfm/tz98ELMbZ73J5fz6vdnj8O108/94Oj1/VxePat9NNN91063BTrQtIrZsB/AhY3v7z9Nahdd6fpcUJV86/2O+sq9dH+3a66abbMLuplgXsnNwInGnA22+53vX9tNP98V08d0C1Cvhbd79z7+PTvb8bC2wDZnvvjwO+gxMWsjjTx/Xme/1f7/If6+KxFKDB+/jncDrsTMaZjskCPw70/4huwXEL+AB0C81bT0XO+/i6Dm+oFmjDCUs0dLjPdlHkbKfnlHd487Xez9/Txet9q8MyTd43/2zg0U7LTQDe7rCsB2cnp7rTa6d18RojgcMdlmnxPrd9PZ/1vqY/i1wczlQDHb/Xik5j/143z723wzJXdbPMNJzkYvty9ThFvf3rg8CIXowz3vs7KANGdbPMZd7xW5yTii2c34i4NNB/47rppptuqnWBqXXe9biAHO96nuvP76y71wdWeH9P7cvVdKhBFudkX1QvXmuS97lnulseJyTavpPXhHMFg/X+zvv989FNN9108+VN9W7w6x1OO+JNncbZxPn9o/bbv4CwLp5/b4dlrurmNaahfTvddNNNN6xVrQtQrVvaaYwVXLjflQMs783vrLvXR/t2uumm2zC6qZYF5jhlh/Vf7Pa3bp7f71rFRYIn3mVu5Hz4xuIEfNo6fP0XwPTi+1zufd5b3S2P0wWlfb0d9y0zgORA/4/oFhw3TbUjfmGt3QnMwZn6ZRvOm10yzkb5KZwroe7BmTezXQHOlVS/xLliqgjnIFcbcBKnxdMia+2TXbzkj4Ev4sz71oqzwzAVGNdpXIU4c9HdDTzvfY1YnIRhNvCCd0zru/ieyoG1wHdx2iN7vGN7Bdhorf39RX8wA2Sd9lkbcNKd23AKSjzOz+5RYJ219nsDWH82TpL1B8Bxzh84PIDzu1xjra3sxar+Gyft+FVrbVk3r/U2cCWwGWfjpQUncXultXZvf78HEZHBolrnV9fiXL0AXc+t3W/W2gPAQpzfQRoQgVNPd+DU1xustc29WNVvgQTg090tb619CrgJZwfbg3MVwrPAWm/NFREJeqp3vmetbcQ5QPhenKv48jk/N3oeTsvkm621H7DWuvv5Gtlo305EpFdU6/wiG6cGbQMKcS6mqwF2Al/B6XTS12nCL6B9OxGR81TLgpMPa1V3638ZZ7/vAZyfZzROOOd1nMDQx6y1tqd1eKfAux/nZ/up7pa31v4S+DBwBGf/tRynW83l1tqq/n4PMrSYi/y9iYiIiIiIiIiIiIiIiIiIiIh0SR1PRERERERERERERERERERERKRfFDwRERERERERERERERERERERkX5R8ERERERERERERERERERERERE+kXBExERERERERERERERERERERHpFwVPRERERERERERERERERERERKRfwgM9gGATlRxjY8clBHoYXZoUO4YIVyQVzWU0uOsDPRyfi3K1BXoIQSnKtAZ6CMNGpAnuv8FTx1rLrLWjB7qeazdE2/IKT6+WPXy09VVr7fUDfU0JPuGJsTZiTPK5r8NcNnCD6UKEyx3oIfRKXHgM46JH0+Cup6K5DA+9+98KZsO9Hqvu+l5f6quvah2o3gmEJcTZ8FEj+vw8V9jgvpcHUw0O9vo7NnoEceHxlDeXhuw+8XCqs6qp3cs43qh9O/GJpJQUO2rCePJra3y30kGoS8Ot1gZrfZ0aNwGLpbSpmDYbGvVpONTRoVI/VevEl8Ji42xEckrPC/niMnsDs0eNxFrIrKrAbX1UP/xYhwarpgaylgZTHTXGMC12Iq22ldLmYjw2+I8FD9XaGQz10le1DoZOvVPwpJPYcQlc9ef3BnoYXRoVOYJvLLiX6XGz2HJ2E88VPILbBs8bri9MjSkL9BCC0ozIs4EewrAxLaI80EPo1oqpeTm+WE95hYctm8b2atkRE/NH+eI1JfhEjElm2s8+dcF9yXGNARpN18bF1QZ6CBdlMNwx8Vo+MPUWKppL+WvW/5HXmB3oYQ3YcK/Hqru+19v66qtaB6p3AuGjRjD+B5/v13Nj4pt9PJqLC5Y6HMz1NyE8ju8u/Dgz4+fyQsFjvFbyfKCH1C/Dqc6qpnbtzlmHtW8nPpE4ZgwZx0/wamYaX9j8Is1uHx0njPf/CYrBrrWBrrPBWF/nJ87kW/M/RbOnid+l/4SSpsJAD6lXhkMdHQr1U7VOfCkiOYVpn/hyj8u0xfsmGLFkymQevPN2Mqsq+cDzj1PV3DTwlfq5rg5WTQ1kLQ2mOro6ZQlfmftRipsK+H36T6hrC56xdWeo1s5A10tf1ToYOvVOU+2EkLKWSu478hueL3yTDWNu4D/mfIeE8MRAD8unchpHkdMYtP8vAZPZMibQQxg2sltHBnoIIgFTVR8T6CFcoLg+ODuQdWSxPF3wOt8+9mvCXRF8ee73WTPyykAPa8CGey3ObBmj2utjqq8Sahrrogb9NYOlDgdz/a1tq+frR3/Pvoqd3DLxLu6Z+knCTFigh9Vnw6nOqp6K+FdpXT3f3/wm75oxmz/f+G5iwyN8s+I6/1+rONi1NtB1Nhjr66maDL557Ne4jIsvzfkOU2NnBnpIvTIc6qjqp0jfhdcZn6xnd24en3r6OWYmp/DPW95LYqQP6pWf6+pg1dRA1tJgqqN7Ko7w41N/Ymz0eL4459skRfS90+pgG6q1U/Uy+Ch4EmLarJu/Zj3F/zv9AE3uRhrdDYEekl8M1TfBgdBJsMGT3TpSJ8hk2Kqqjwn4AbmOgmmnoienajP44qH/x4maQ1S2BG/npL5QLdbOi6+ptkqoUfgkOLXaNn586l88mvsScxMWERsWH+gh9ctwqrPalxXxr38cOsxXX97EmgmT+e11t/huxXXhQ+ZEWbtA19lgrK/ZDQV87cgvaXA3sHGcD/9+/Gw41FHVTpG+81X4ZGdOLp955nlmp4zkv9as98k6h0pNVfjEcbjqFN8/8XuSI1K4ZuxNgR5OrwzV2ql6GVwUPAlRu8sP8/Vjf6TNthETFsvGsbfiGmK/zqH6JjhQehMdPDpBJsNZoA/IdVRcnxBUOxbdqW2r53sn/sYbZ/MBWD/6OqbHzQnwqAZGnchUd31NtVVCzXAPnwRz/X0s72U+f/DH1LZVYzAkR1xkzvcgNNxqrGqqiP88e+IUn3/uBX69f5fvVz5ETpS1C3SdDcbaWtJczn1HfsU/sv8IgIvQ6CY2HOqoaqdI3/kqfLItO5t7H3+KH7+91SfrA4ZMTVX4xHGyJp37jvyC5woeCfRQem2o1k7Vy+AxtJIKw1B+YzLT4jZw68S7+PfZ3wyJlk59oRNeXdOb6ODRCTIZzgJ9QK6zYNqxuJizzaO5cvR1fHHOt9g49lYMvtnpDZThXotVd31LtVVCzXAOn0Bw198GdxP5jcncNOE9fG3+j5kRgoHP4VZjVVNF/GdzegYnMp157j+0cCmTEnw4PfcQOVHWLtB1Nhhra1VrDRn10US6ovjS3O9wzZjQuXp7qNdS1U6RvvNV+GRffgFNlW1Eh4fzzcuuIi7CB1PaDZGaqvCJI6+xiJyGBOLC4vny3O8zK35eoId0UUO1bqpeBgcFT4aA10t28svUvzE5dhpfm/cj5iYsCvSQfG6ovhEOhNoVDx6dIJPhLNAH5DoLph2LnrR4WvnS4Z9zpGoft068i8/Muo+EcB8e+A2A4V6LVXN9S7VVQk2gwifBUoeDvf4+W3CY+rZaPj/7G6wcsTbQw+mz4XDSrCPVVBH/GuuJ5yuXruOJ2+9mZrIPu0ENkRNl7QJdY4O1tuY2JFDRUsbtkz7AHRPvCZmLKIZ6HVXtFOk7X4VPAFYmTOSjlyznrzfdSWy4wiftFD45r6xlFDFhMXx21te45P+zd+bxMV3/H37uZLKvCAmCCGKtlqKqrSpVW6mtVEv35acrWrrve/vtvu+2omiLopbaWlW17zsRSQiJrJNMklnu748kGoTMJHfmnpk5z/fllW+SuXc+ivvknPM+nxPZSe9yqsRbvSl9qT8eETxRFKWvoij7FUU5pCjKU5V8f4KiKHsURdmhKMoKRVGaVPjeHYqiHCz7dUdV72Wx+wn3wHKEPzM28sT2d8i35vFg8yfpXPtqvUvSHF+bjHMU+SB1D0ctdeQimcSluNN1zqL3hNy5iN76v5xCWxGv7pnG54dm0CysFU+0fBV/RYPBqY74uoelc7VFetU3Edl3VWE2Bfp09xOR3ZtelMET2z8gqeAgdzR9iP71h+ldUrXwJc9Kp0q8Gb1dd7qwkFtnzsFPUZgz+BbaRcfU5LdzNl6yUFaO3o4VcWxrVa28tmc6i46vpmdMf8bEj8VPkUfviIB0p0Qk9Hado2gVPlmfksL43xbRMaYBPwwYSrBW4RMXelWGT9zL6ZIcJm3/iFTzMe5JeIxuda7Tu6Qq8VZvSl/qi/DBE0VR/IDPgH5AG2CUoihtznnZVqCTqqrtgbnAO2XX1gZeBK4AugAvKori0Fk0Ij2wHCXVfJIJ295n8Yk17M/bpXc5LsNbH4Y1QXY/cR9ykUziCvRynTOItOu6HE9x9fKTfzNx+7ssOjEHi2rRu5wa4+tBUOlbbZFe9S08wXeOIMMnYmKyFvLszi9ZcfIfesXcSN3AWL1Lqha+5FjpVIk3IorrDmRmMmrGbAotFmbeNIIu9eOq9xuqDBk+0RzR/Kqi8m3SHKYnL6Bz7asYHuf2vG+18XaPSndKREAU1zmKVuGT3w8c5IlFv9MptiHf9R9CkFEjH8rwSY0QyaH51gKe3vEZe/N2MKrJvVxZp4feJVWJt3pT+lI/hA+eUCqfQ6qqHlFVtQSYBdxU8QWqqq5SVbWw7NP1QPloqg+wXFXVLFVVs4HlQF9H31jE1HlVFNtL+C5pLnvyQUHh9vixHnGmmLN468OwpsiHqXuQi2QSF6Cb65xFhEm5iniKp48VHueXtF2kmqNoH3k5j7Z4lkh/XdZLNcOXXSwDn9oivepTeIzvqkKGT8TEqtr49NB0Jmx7i4zidAACDO7/s6opvuRY6VOJFyKM65Jzchj540+cMOXTKCKyurepHBk+0RwR/fpz6lI+PDCZP07+pncpTuHtHpXulAiAMK5zFK3CJwv37WfS70tpFlWb2NAwTe4JyPBJDRHJocX2El7c/R2/pC5jV+5WvctxCG/1pvSlPnhC8KQhkFLh89Syr12Ie4Dfq3ltpYj00HIGk60xjUMSeKTFs9wQM8hjzuR0FF/fcX0h5MPUPcijdyQao7vrnEGESbmKeFpQNNcaSeOQBJ5q/SZtIi7Vu5wa4esels7VDulUn8GjfFcVeoVPRPCw6O5NM58k1RzFFbW782ybd2gY3KTqiwTDlxwrA50SL0Mo1500mbjph+n8vH83AHWDQ2pyu7OR4RPNEdGtazI2sj3XgoLCwAYjqBNQV++SHMLbPSrdKdEZt7tOsTtZYSVoFT6Zv2cvvb/5gaO5OaX3NWi0zCrDJzVCJIfaVDvTkuezN1/BgB896/XHqLj256aa4q3elK50P54QPHEYRVFGA52Ad5287n5FUTYpirLJkltY6WtEemg5SnZJLuO3vcfW7H8Z2HAkj7R4hloB3vfw8NYHYk2Qgw/34YkLZcUqHLEqDv2SiEd1XVd27RnfWfMq952jiDApdy6e4uq/M7cwYfvb5JRk8UCzJ7iuXj+9S6oRvu5h6Vvt0Nqp0neejRZjO1t+gWuKq4Ae4RMQx8Oiu3dLThYKBsYnvkC7yA56l+M0vuZY6VTnka7zbDRxXUHVrrPY7RhNBi6tF8ua2+5jZOtLqllxJcjwieaI6tZiewJXRfdkfOKLxAbpmv11GF/wqC+4U7rOs9FqHtNWWIC/qeb1aBU+KbRYMJoMPHnFNXzddzABBj9N7ivDJzVDRIfWCbyCIXG3cX+zJ4Tvxumt3vQUV3qL7zwheJIGNKrweVzZ185CUZTrgWeBQaqqFjtzraqqX6uq2klV1U7+kRdO/ou+q6syzLYiXts7nY8PTiMupCn3JYzXuySX4K0PxJriKQ9UT8cTwycS4XC56+Bs3xkjar7TTYRJuXPxFE8fN5/i8e0fsD1nE0PjRpMYdu5RuJ6Fr3chk76VSBzGrWM7Py1bH18EGT4R171JBak8vv1/pBelcV/CBI8Me/qaX6VTJV6Am10X6nBhh49lseFEKm/36MOdl2gYxvPC8InejhXRraeKT/P0jo9AgccSnycuOF7vkhzCFzwq3SnRAbfPY/qFlPpOpPAJQGpGHj2bJPDZDQPxl51PziDDJ/+xNWcPHx+cRmJ4Gx5p8Qyhfu6Zp6gu3upN6Ur34QnBk41AC0VRmiqKEgDcAiyo+AJFUToAX1EqsFMVvrUUuEFRlFqKotQCbij7Wo0Q7cHlCKtOrWfc1jeZkfwNAEbFX/gHnLP4+qLXhZAPVPcgwyeSGiKc6xxFhEm5c/GUoGiJ3cKre6byxt4vOWDao3c5muDLHpa+lUgcwv2+c/FiWDlmU6BuR++IgMjezS7JZdKOT9hRFvaMD2mud0lO42tjXelUiYcj7NiuyGrlwbkLWHLkAC9d3YsHO3TR6tZuCZ/4WvcTEd2aYj7BUzs+othWxKOJz9I0NFHvkhzCFzwq3SlxM7q6TqTwyU87dvLSHyvo3bQ5H/e+UR67UwE954xFc+iqU+t5e983NAxuzLiWLxDlX1vvki6KtzpTutI9CB88UVXVCjxMqXz2ArNVVd2tKMoriqIMKnvZu0AYMEdRlG2KoiwouzYLeJVSEW4EXin7Wo0R7cHlCKeKT7M+K4dUcxQ3NriZp9u8RZuIS/UuS3O89aFYE+QD1T3I8ImkuojqOmfQe1KuMjzB1SoqG7N2kmqOomFwYya0fNljzsy+EL7sYXnUnURycXTznZvCJ6BP9xNRHCyyd0vsFl7ZM5WXdn3C0cJDACiI3Z62MnzJsdKnEk9F9LFdic3GY78s4tcDe5jUtTvXxzfT7uZu8K0vhk9E82t6UQZP7viQrOIMAgz+epfjFN7uUelOibsQwXUihU+mb93OqytW0S8hkfd6athh0QvCJ6CfS0Xz58asnby0+zMCDcGE+0fqXU6VeKszpStdj/DBEwBVVRerqpqoqmozVVVfL/vaC6qqlsvqelVVY1RVvazs16AK136vqmrzsl8/aFmXiD/8O8rC49spsJoY23wSIxrdKfzZYs7irQ/FmiAXw9yDL4ZPlFLuUxTlX0VRTIqiFJSdv/l/iqJU6hlFUW5UFGW1oii5Zdf8qyjKHe6uXSREdZ0z6D0pVxme5Ol8az1iAuvzeMuXiQ/1vN3YFfF1D0vfeifSd9qgm+9MRrd2P3E3ojhY5DGyisr23H2kmqNoGtqCSa1eJzowRu+ynMaXHCt96n6k67RB9LGdTVV5csFSJi5ewoqjh7W9uQyfuATR3Hq6JIdHt77L/vzdANTy95y5MG/3qHRn1UjXaYMIrhMpfDJly1ZeX7ma5UmHNLnfGWT4pEaINj7dk3eI+ze9TEphEgAR/lH6FlQF3upMX3GlXr7ziOCJ6Ij04HKUo4VpjNv6P+al/cFV0b14stXrxAU30bssTfGFNorVwVceqnpy1FLH1wIo04GvgXhgJvAtEAJ8AUw+98WKojwM/Aa0K7v2G6ABMFlRlP+5pWKJyxD16B1PYE/eISbteI8im5lHWzxLx1pd9S6pRvi6h6VvvRLpO2/Ay8MnojhYdPdmFkcQFVCbJ1q+TLOwVnqX4zS+5FfpU7cjXecj2FWVX3fvwc9kIC48golXXI1B0agTlAyfuATR3GpHJdUcRbvIDrzQ9n0ur3Wl3iU5jLd7VLqzSqTrvAiRwic/bN7Cku0HAejaoBFBRo18KMMnNUYkh1pVK6nmKLrWuZbn2/yPluFt9S7ponirM33Elbr4TgZPNEKkB5ejWFQrU47+you7P0ZFxaJa9C7JJXjrg7Em+MhDVXd8IXyiKMoQ4FYgCWirqup9qqo+BlwGLATGKIoytMLr44H/AVlAJ1VVH1JVdTzQHjgMPK4oiufMVkguiAgTcxXxFE+nmU/y+Pb3OVZ4hLuaPkK7yA56l1RjfNnD0rfeg/Sdl+HF4RMQx8Eiu3dv/mEmbn8PkzWfh5s/TZfa1+hdktP4kl+lT92DdJ3v0rdBCx7q2JX3evbDT4ZPLogIfhXRraszjnOk4AC3xz9ItzrX6V2Ow3i7R6U7K0e6zjsRKXwC0EANZ8qAYUzuP4xQf42OJJPhkxojmkNXnEzidEkGDzSbyGVRXfQu56J4qzO92ZV6+k4GTzREtLZNjrIr9yAPbn6DzdmFAPSrP5SYoAY6V6Ut3vpgrAny6B334APhkyFlH99TVTWz/IuqqpYAz5d9+nCF198NBAKfqqp6tMLrs4E3yj79P5dVK3ErIkzMVcRTPJ1vLeCpHV8wOekX9uTu0LscTfBlD0vfeg3Sd96GDJ+4BZG9m16UwePb3uewaR9j4v+PNhGX6l2S0/iSX6VP3YJ0nY8yefNW/vfnXwxJbMNnNwwkwOCnzY1l+MQliObWIlsxz+/6mr15OxjV5F6uq9dP75Icxts9Kr1ZKdJ1XopI4ZNTBQU8+ftSOtVvyLQbbyYiQCNXyfBJjRHJodmWPJ7c/vGZzYdXRffUu6SL4q3O9GJX6uY7GTxxASI9vBzFjgpAoa0J3evewKRWr3Nt3T4oaJf01Btfb/l/Ibz4wSoMXh4+iS37eKSS75V/7RpFUQLK/n/5T1BLKnn97+e8RuIFiDAxdy6e4GmramX+8RUcM4cT6hfGIy2epaGHH4nn6w6WvvV4pO+8ERk+cQsie7fAZubZXV/x5eGZ7M3zzLCnr41zpU9dinSdD/Plvxt5ZcUq+iYk8nW/wR5xPEA5eoRP9HasaG4tsVt4aff3bM3+l6Fxo4kPba53SQ7j7Q6V3jwP6TovRqTwycJ9+3lkwULa1Y3hx0E3ExUYpMl9Zfik5ojk0AKbmWd2fsGevO3c0vge6gfF6V3SRfFWZ3qpK3XznQyeuAhP2VV9LlkluTyy5Q0O5O9meKPbeaj5U0T519a7LE3x1odjTfDSB6tQeHH4pDwt2bSS7yWUfTRW+P8tyz4eOPfFqqqeAAqAOEVRQrQsUqIvIkzMnYsnObrI3pi6gbFMaPkinWp107ucGuHrDpa+9Wik77wVN4ZP9AigiOJfkcfHNtXO0vS1pJgjifKvzf0JE4jwj9K7LKfxJcdKn7oM6TpPxK7draZu2cozS5YRFRiEUdFwythkdLlvfdGxonnVqtp4bc903tz7FUcLDuldjlN4u0OlN89Cus7LESl8svzgIf7v1/m0qFWHYS3banJPQIZPNEAkh5aHN1/Z/RknilL1LqdKvNWZXuhK3XwngycuRqQHmKPkWPJ4fte3fH7oR+JDm/No4nMYvOyvirc+HGuCFz5YhcNLwyeLyj5OUBTlTEpNURR/4OUKr6tV9jGy7GPuBe6Xe87rJF6E3hNz5+Ipjk4uPM74be+QXHCEO5o+xNC40RjQqP21Dvjazuxzkb71WKTvvBk3LIiV44sLYxUR3b2K0pzE8LY80fIVj+w05kt+lT51CdJ1HopWC2QAs3fuYuS0WZgsJQQb/YkO1nAt1Q3hE187ekc0r9qxsyFrB6nmKBqFNGVIw9s8ppu2tztUevMM0nU+gEjhkz+TjjJoynS+27FZk/udwcXhE3f5VIZPSrGpdrbm7CHVHEViWBtubXyf0HO/3jq362Wu1M137pld83HSC8KJDc3XuwynWX5yHTty9tM2Mgg7dhQUAg1BFNnNepemCeUPxibBmVW80ncof7AmBJzSuRLvpTx8Eu9/WrcaSlSjEyGYlGhFUTZV+MLXqqp+XeHzWcAYoA+wR1GU+UARcD1QHzgGNEbTfVgSTyanIJioUHE8Uj7IEN3TuZZ8nt75OXfED2Fgg35Y7VYWHJ+ld1k1Itkc7bMOlr51D9J3EqcxGSHM6vK3MZsCCQ4rdvn7VKR8Qk8EB4s8Pt6Ze4Cndn7AC23uZ3ziC0xP/optORv0LsspfMmvR0rq+bxLpesk5RhNCtYwVZN72VQVo8nA/4b2pV10DHcsmsvR3BxN7u0O17rbs3qPcUUd0zYM7kLPmP7UDohmWvKXlNjd+7NPdfD2uWJP9aZ0naQ6+JvAElaze2jl1iNZ2Rgx0KhhJP/r2ZfH/lhMav6F1nadwMVOdZdP9fSoiGPTcP/WXBndg0j/WnyX9JHQ/vTGsaeervQW33lXGwuBEbm18MU4WXyalafSSDVHcV29fjzd+i2ahibqXZameGMyr6Z4WbJPSDyo+0mmqqqdKvyqKC9UVbUBA4GngAzgjrJfB4FuQPlPbuW2rioZWVWyUqIRdpt+PwLovSusMjzB0TbVzvdJP/Puvu9YdWoxAP6Kv85V1Qxfd7D0rVBI30lK8eLOJyCOg0X27tGCVB7f9j/SzMe4J+Exjzzmzpf8Kl3qFNJ1Xo6WnU8Avv9nM+EBAcwdcivt68ZWfYGjuMG1vtb5BMRz69zUpXx3ZC7tozrxRMtXqBuo4d8hF+PNHvUBb0rXSc4gUucTgEhrEM2iajNn8C0kRNWq+gJH8JKj7GTnk/+Yf3wFnx/6kVYRl/BYi+cIN4rdUMkbnekhrhTWdzJ44mZEe4g5wz+nj2PDxrjE5+kXO9Srjt/xxodjTfGQh6tH40Hhk4uiqqpFVdW3VVW9RFXVIFVVo1RVHQwcBVpQKsGkspfvL/t4XoJNUZT6QCiQqqpqoRtK93n0WvSC0gGFCJNzFfEUR687vYW9+QYUFB5s/hS3Nr6PAIN+f5Y1xdcdLH3rOUjf+RAyfOIWRPZutiWPJ3d8xqxji9iVu1XvcqqFt7Y/rgzpUu2QrvN8tFwg234inRE/zsJssTDrppH0aFzZEfHVRIZPXIJobl14YhUv7f6UcP9IJrZ6ldighnqX5DDe7FBf96Z0nW8hUvhk18mT3DZrDkaDgZ9uuoVWtTV6zsjwSY0RrXHA8pPreGPvl8QENeDxli/L8IkOeIMr9fKd9yQHPAjRHmKOctB0lMe2vsOfGZvo32AYjyY+Sy1/71g4B9+amHOUIyX1vOIBKzLeEj65ALcAAcDMCl9bWfaxbyWv73fOayRuQM/wCYgxOVcRT3J0mrkWW3KSuKJOd55s9TqNguP1Lqna+LqDpWs9Huk7b8SN4RM9XCyKf0X2rlW18lPKYg4VBOKvBHBX00c8asGsHF/xqxy7uhzpOg9Cy/DJ0ewcbp4+kyM5Wbx57Q0E+mnoRxk+cQmieXVn7n4mbHuHfzJXc7LouN7lOIU3O1Q6s1Kk67wUkcIn+zMyuXXGbCx2G7NuGkmiDJ+chd4eFcmhm7N38+yuj9iRu4l8q/hNlbzRmV7sSpf6TgZPdESkh5ijmG1FfHRwCh8emEz9oEbUCayrd0ma440PyJrixQ9YIThqqePRARRFUSIq+dplwLtANvBWhW/9ABQDDyuKEl/h9bWAZ8o+/dJVtUoqR4ZPzscTHG3HzsxjC3lh10f4GwKY0PJletUbgIK2LbbdiS87WC6YiY/0nQ9iMnp19xORuo+J7l2LmkDzsFZMaPky7SI76F2O0/iSX6VLa4Z0nfdgNCmaLZJlFhRy24+zGbNwLsU2qyb3PIOXhk/09qtoXs0ozuKTQ4tIMUcSYYzi1sb3EewXondZDuHNDvVVZ0rXeSaKrWbXixQ+ScrOZtSPs1mZfISUPA0DBTJ8ogkiOfSw6RgfH1wIQJ2AulwW1UXnii6ONzrTk12pl+9k8ERnRN7hdTHWZGzk3k0vcMi0D4BLozp5dJv/c/H1ndeV4ckPWE/Bg8MnyxVFWa0oyqeKorypKMo8YCMQCAxRVfXMlpay1l0TgdrAJkVRPlMU5QNgB9AMeE9V1X/c/1uQ6LXjuhwRJufOxVP8vDvvEI9ufYtduVu4qm4v/A0BepdUI3zdwdK3QiN956t4cfgE9J/YK0dk76aa05mw7V0yik5wX8IEescM0rskp/Elt0qX1gjpOi9Dq0WyAouFoynZGE0GnrziGl65phcGRaPAuxuCnr7YXUxUr4b7X0qXOlfzRMtXqR8Up3c5DuHNDvVRZ0rXeSg1DY+IFD5Jy8tj0oKlmK0WwvwD6BSrUWdFL/GpdOjZpJqj6BM7mHsSHqNXzI16l3NRvNGZHuxKXXwngyeCINqDzBHMtiJSzVEU2xO4q+mjTGr1GnEe3Oa/MrzxIVkT5G5s1+Oh4ZO5QDgwGpgAtAe+Btqoqrrm3BerqvoJMAjYDdwO3A+kA3eqqvqEu4qWVI7sfnI2nhIQNVkLeXnPFCZuf58SezFGxZ+W4W31LqtG+LKDpWuFRfrOl5HhE7cgsndPl+QwccfHbM1ez6CGIxnYYITeJTmNL4U7pUurjXSdF6Ll0TsAflYDt7frwOc3DPSoo3dk+EQM/s3aznM7PyLQL5DHW75Mh6gr9C7JIbzZnz7oTOk6D8abwiel9zLw9JXd+XHQzfSOb6bNTb3Ep3pvUhTNoR8c+JXNWesY3HAUIxrdiUHg5X1vdKaHulIX34n7N9MHEXmS7WJkFGfx0u5PCDAE8XjLl+lZr79Ht/k/F1+anHMUD33IegyeFj5RVfVdVVUvV1U1SlXVQFVVE1RVfUhV1dSLXPObqqrXqqoarqpqqKqqnVVVneLOuiUXRoZPzsdT/JxrMZFqjuK6ev14uMUzDIu7HaPir3dZ1caX/StdKx7SdxIZPnEfonq3xG7htb0/8kPSL/yTuVrvcqqNr/hVutR5pOu8Fy0Xyd5Z8xevrljFDU1bMH3gcCIDgzS7tzsWy/Q4ekdPRJxv3pd/hPHb3iXNfIy7Ex6lS+1r9C7JIbzZn7602VC6zvPxtvDJ+yvXsTczgy/63MSg5q20uamXhE9AX4+K5E+LauX1vTP4JXUZ19Ttzb0J44Q+hcIbnelpntTLdzJ4IiAiPcwcZVfuQR7b+ha787YyJO427k0Yr3dJmuOND8qa4GkPWU/jqKWOxwVQJN6FPHrnfDzJz9OT17Hg+Ep61OvDE61eITZIo5adOuDLAVBfmvyTSFxBTGgosaFh2t7UjeETX9yZXRGRvbvg+Aq25ZYAMCzudpqGttC5IufxFbdKl0ok/6HlItmULVsZ99si2teLZeagEfhpdewOuMW1vhY+AfG8ml2Sy5M7PmXWsUXszN2sdzkO4+3jU+lMiafgTeGT3KIi7pz1M5vT0/jw+gHc3KqdJveV4RNtEMmfKirTkufz5eFZhBkj9C6nSrzRmdKTVSODJ4Ii0sPMUfKtBby0ezJfHp7J3rztepfjErztIVlT5CSe65HhE4neyO4nZyPibrHKsKhWfkj6mVd3f0a4MZKJrV7jsqguepdVI3zZwdK1Ekn1iA4O5bfhY+gY00DbG5uMXt39RKTwp+jOzbPE0SbiUh5t8Rxd61yrdzlO40tulS6VSErRMnyyeP8B7p7zC59t+Rebqmp2X0CGT1yEaF61qjZ+SlnMQVMARsXI3U0fpZGHHOPuzQ6VzpR4Ct4UPjGVlHDPT7/yV8pRxnfuRohRo+7FLh67yvCJPixN/4sntn9Mib2YQEMQdQNj9S7ponibM6UnL44MngiMpyxuncvS9LXMSilNqneIuoJhcbfjrwToXJV2eGNKr6bIB61rkeETid7I8Mn5eIqft+Ts4dGtb7I3bzvHzSl6l1NjfNm/0rUSifMcPp1FgaWEmTeN0G7XWEW8OHwC4vhX5HFxntXEhG3/46BpL7c1uZ+hcaOFPmu7MnxpfCtdKpGUomX45N+UVJZuPwhA/4REujZopNm9ZfjENYjq1CJ7U5qENmN8yxfl0TsCIJ0p8RS8KXxSZLUydu4CRsybRaHVosk9zyDDJzVGNH/asZNqjmJEozt5vOXLJIQm6l3SRfE2Z0pPXhj3zJRJakR6QTixofl6l+E0qeYorqnbgh71+tA64hJ+TP6GpIIDepelGcnmaJoEZ+pdhjAcKalHQsApvcvwWkrDJ9osGher/lKMEqcpH0QEhxXr8v7lA4uoULMu718Z5QMO0R2da8nnpd2TAYgLhsENb2Vr9r8kFx7Wt7BqUj5Q8UUHS9c6h/SdpNhqZdjUmXw4uD/vXteXuPBIPtj4t7ZvYjJCmFXbe1aC2RSoi4NzCoKFca+o4+ICm5nndn7NHfGDGdSwH5H+tfgh6RO9y3IaXxnfeptLpesk1cVoUrCGadelJKDAjwc7XkGL2nWYsOJ3Fh3er82N3eBZdztWhLGtiE5NL8pg/NZ3eab1GMbE/x+NQ5ryS+p07Nj1Lu2ieLM/RXGmdJ2kKvxNYKnBCas1vR6082qJzUb6CROEwUtX98RUUsL/Nqyt8X0BlzrVnfPGeo5RRfTnd0nLeaVdMx5u8TRTj37BtpwNepd0QbzNmVq7yVt851lbcXwYkXd5XYyZxxbywq6PMSr+TGj5Ijc3upMgg/7pfq3wpd1hjiCP3pFIvB/Z/eR8PMnPeZY4LovqzLjEF7i2bh+9y6kRvupf6VqJxDlyi4q496df+W7jJv497qLOT27sfKLX0TuiIKpz7dj54egvfHxwGusyV+pdTrXxFbdKj0okpWjZ+cSuqtwx62e2n0rnsxsGck/7yzW7tzuOuPNFv4ro1DyriWd2fsX8tBVcW68PwxvdoXdJDuHN/pTOlHgKonQ+0cqt/iYDQXYjD1/elVeu6YVB0cjZXuJTPY+HFc2fp4pP88T2DzhWeIS7mj4i/HyvNztTUooMnpyDza7doMsViPZQc4Sduft5eMub/HZ8JVdH96JFeBu9S9Ic+bA8GzkokUi8GxHCJ3pP0p2Lp/g5z2risa3vsDdvO8Mb3c5dTR/x6ECoL/tXulYicRybqvLm6j/ZsD8VgNFtL6VNnbravombwieg3+KYKO4VeVPGqlPrWXEqDYDr6vWjfWQnnStyHl9xq/SoRFKKlotkuUVF3DnzZxYfPsDzV13Hs1f20OS+Z/CSxbKK6O1WEZ1qx87ko7/w6cHprD61RO9yHMab/SmdKfEU/E01C5BoET4BbYKdKvDcsj/4esNGbm/XgS9uGESQUSMPepFPZfikFJO1kGd2fsmOnE30jh1IiF+o3iVdFG92pkQGTypFpEm1yhBxUFAVRfZivk/6mUe2vsrO3M0AtIvsSJgxQufKtEN2PzkbOSiRSLwbvXZdV0Q0V3uKnwtsZl7Y/T1Tjv7KpVGdebDFU3qXVCN82b/StRKJ80QUBzK2wxX8PORWbmzWUtubu2FHdjl6OVgk94rs3BNFtekQdQX3NRtP39ghKIi9weVcfMWrsouYRPIfWoVPSmw2xv26iCmbt2K12zS551m4YbHM3Y4Vwa0iOnXFqX/YklN6lMLghrfSJCRB54qqxpv9KZ0p8SS8JXwC8M6av3hlxSp6N23Od/2GaHJPQIZPNEC0eeASu4VX90xl4vb3KbQVoKDgp/jpXdYF8WZn+joyeHIRRPjB/2KI9FBzlOPmU6Sao8gqieX2+LE82+YdOte+Wu+yNEU+MP9DDkokEu9Hhk/Ox1P8PC/tD57f9RGLj8/VuxRN8FX/StdKJM5RaLEwbNoMdmee4tMbBjLpimu0a1tcjgyfuA1RnWtT7Ty541NWnVrPgAbDuSfhMQINQXqX5RS+FOyUHpV4Mopdu3tptUhmV1VeXbmK91b8DUCLWnWICNDQWW7wrB7hE739KqpT8yxxtI/qxKOJz3NZVBe9y6kSb/endKbEU6hp+ESro3e0YOqWrTw8/zem7Nyqyf3OIMMnmiCSP+2oZBRnkWqOYlDDWxjbbJLQna692Ze+jAyeVIEIP/hfDNFSdY5SaCti0vb3OFV0gtvjxzK22SRqBXjPQ8bbBxnOIgclEol3I0L4RDRXe4qb9+Yd5o9TKQD0irmR0U0ewF8J0Lmq6uPL7pWulUgcJ7OgkNtnzmXW9h082PEKvupzk/Zv4sbwiTx6R0znWlQrHx+cxndH5nJJ5OU8lvg8BsTdcXYhfMWt0qMST0arBTLQbpGsnBCzP5MHDGPOkFE0CNPwee2F4RPQP9wpolPzrCae2PY+KYVJ3JPwGL1jBuldkkN4sz+lMyWeQk3dKFL4ZNnBQ6zcdQSAwS1ac1m9WE3uK8Mn2iCiP3flnqZ5eCvGJT5PhH+U3uVcELmW6n3I4ImD6P2Df1WI+GCrilRzOhO3f8w3R2bTLKwlT7d+kxC/ML3L0hT5wPwPuSNbIvFu5NE75+NJ4dBUcxSF1jA6176aJ1q9Qr3A+nqXVG18ecAiPSuROE6JzcZzy/7g+WV/sCblqGvexE3hE5DdT0R27sITq3h596eszfgDOy44dsIN+IpXpUclno6I4ZMSm40nFy+lfmg484eNpmOMhuMMGT5xCSL6NM9q4ukdn7Mx628GNRzJsLgxepfkEN7sT+lMiafgTeETgOBCI49c3pWZg0bSO76ZNjeV4RNNEM2fazI28OruL6gTWI/HE18mJqiB3iVdFG92pq8hgydOINKursoQebLtQthRWXxiDQ9veY1fUqdTaCs1ebgxQufKtMOXF8AqQw5MJBLvRoZPzsdT3DwndQmv7PmMcGMkE1u9SsdaXfUuqUb4qnulZyUS55i5fQez/t0BQK8mCVyv1eRdOSajPHrHjYjq3B25+5mdWtoau21EB66t20fnipzHV7wqPSrxdEQMn6w/lsLN02dSYClh5k0jGdayrWb3dodn9dhkobdbRZxjtqhW3tg7g5nHFrInb7ve5TiMN/tTOlPiTgw1OFrOm8InFrudUTNmsz8rky/73MSYtpdpcl9X+9SdLpXhk//YnruPZ3Z+iNFg5JEWzwjf5dqbnelLyOBJNdD7h/+qEO3h5giZJdnMTd1OqjmKZmGteLndR/SrPxSj4r5dgq5GPjT/Q3Y/kUi8GxHCJ6K5WsSJu8rYnrOP8dve5rg5hdvjHyQ6MEbvkmqEr7pXelYicR6jycA9l3bi235DeOTyrmh70AA+ET4Rxb0i+zbVHEWHWl0Y3uh2bmtyP0bFX++SnMJXNlVIh0o8HRHDJ4ezshg2dSabTqTRPyFRs/uewQu7n4jgVRGdOjvld5afPAbAlXV6EBfcROeKqsab/SmdKXEngfnVT594U/gkq9DMmJlzWHnsCK92v56nunbX5L6A13Q/0XN8KtoccFJBKhO3v8+sY99hUUv0LqdKvNWXvoQMnlQTkSbWKkO0h5szbM02sS1nI/3rD2NSq9eJD22ud0ma4c0DjeogBycSifcij96pHE9w8+mSHCbt+ISXd39KZvFJAIIM4v23dBRfdq/0rETiHPfNnse83Xt4vMvVfN5nEKH+GocC3Bg+8fXuJyL79u19s5l1bBFd61zLuMTnqR3geY7yBa9Kh0o8HS3DJ1otlOUWFXHPT78yft5iAOqGhBIeoOHOWy8Nn+jtVlGdeqq4Ln1iBzMu8QUui+qidzkO4a3+lM6UuBMZPinFbLHy0Nzf+HHrNgosGocJvCR8ArL7STmnik+zJD0JgE61utGzXn+nrldQSAzXsGNdFXirL30FGTypIXr/8F8VnhhAybOaeGPvDF7b8zmBfkGMT3yRgQ1G6l2WpsgH53/IXdkSiXcjwyfn4wletql2duYeINUcRZuIS3mp3Qe0i+ygd1k1wlfdKx0rkThOsdXKE4uX8Maq1dwQ35xfht6m7YIYuC18AvLoHVHHwioqP6Us5s29X1E3MJZJrV4nyr+23mU5jS94VTpU4uloFT4B7RbKrHY7hRYLRpOBr/rcxC9DbqNxRKQm9wa8MnwC+rtVRJ+W2C08vv090szHuCfhMfrXH4aifc86zfFWf0pnStyJDJ+UYldVXvxjJV+s2QBA2+h6RARo5CgZPtEE0fyZao6iTeSlDIm7jRGN7sTgYESgdkA0NzW4hVfafULdwFgXV1mKt/rSF/Cec0x0JKcgmKhQs95lXJT0gnBiQ/P1LsMpNmfv5qEtbzC68SDMthy9y9Gc8gdnk+BMnSsRgyMl9UgIOKV3GT5Bsd0oxS1xK+WDieCwYl3ev3yAIZKrywcenuDmHbkFZJVk8kCzJ/jj5EIWHp+NTbXpXVa1SDZH+6R3fdWx0neS6vL9pi3sz8ikW2IT8ktc0IrWZIQwq/b3rQSzKVAX/4o0RhZ1LLwhawePb3+HAfXbkWPJ0rucauELY9ryhTRRPSpdJ6mK8gUyS1jN72U0KVjD1JrfqIz3V//NJ4NvZP6w0Ty4bAH/pKVoc2M3eFaPMa7ebhVxDJtryefJHZ/xf81uoV/9ocQGNeT7pI8dvr52QDQda3Vl5cnfseO+Ma63jktdNe6UrpNURmC+neLw6u2r9zfVzIs1vR7+C59o5dXQogC+7z+UvOJi7lz0M2mmvJrf1MU+dedYVU+HijYefXPvLEY3KWRo3A3UDojmh6RPKbYXXfSaPEsOf2X+Qc96/Xmm9dvMOPYNG7PWurxWXxhvVsRbfCc7nmiECK0Pq0LUHV8Xo8hWzLdJc/jx2L8AdKp9FXfGP0S4MULnyrTDGx4kWiG7n0gkbsTu/p1AsvvJ+XiCl9OLMnl824csOfEX18fcyOMtXyY2qKHeZVUbXz16RzpWInGOv5OP8e7yvwBoWTuasR26aLuH1mR069E7eiDSGFlU36YXZfJd0mpSzVHEBjXkkRbPUiegrt5lOY0veFU6VOLpaHn0jlasT0lh2LQZZBYWMu3Gmxnd9jLN7u0uz+px9I7eiOZUq2rl00PT+e7IHJIKDjh1bZ2AetwQO5hXL/mEpqGJLqqwcrzVndKXEnfi6Z1PQDuvFlutTPhtMTGhofw69FbaRcdocl/Z+UQbRFqbVVGZljyfLw7NpFVEe8YlvoC/cvFOrxbVwvrTa/gp5Qf25G1jdJMHGNnobjdV7L3O9FZk8ERjRBgAVIUoDzhnSTVHYbXXpX1UZ55t8y6da1+td0ma4auLYBdCDlIkEjfhxkWvcmT45HxEGnxcCItq5asjs3hr79dE+dehWVhLvUuqMb7qXelYicQ5jCYDg1u05smu3fm672DtWheX48bwiTx6R2zfWtXGxAU34cnWb9KpVje9y3EaX/CqdKjE0xFtkQzgWE4uN0+byepjSdzRrgOBfhp70UvDJ3q7VUSfLjyxmmnJ6wFoFX4JiWFtqrzmaMFBZh37lszik4xLfJ7+9Ye5usyz8Nb5YLnpQeJOZPjkP/5NSWXEj7MosdmYPfgWejVJ0OS+7gifuMul0p//sezkWl7b8wXbczZiUR3r9HrYtI95aTNZcXIRV9TpzhMtXyHCGOXaQsvwRl96KzJ44gJEGABUheiTbhdi4YnVjN/2JieLjnN7/Fj+r9lEjzwP+0LIh+d/yAGK56Aoyp2KoqhV/LJVeH18Fa+dpefvxyfRIXyiZwBFVE97gpf/zdrOQ1te4+/MlUDphF4t/zo6V1V9vHWiryqkY51Hus63eW/F37yyYhU9Gjdl/rDRtK6jcUcKN3rY18MnIK5vt+XsZdy2tzhuPsYdTR9idJMHCDQE6V2WU/iCU73ZodJ1voFoi2QAppISHpy7gNEz51BssxLoZ6ROcIhm9/fG8Ano71ZRfZpqjqJf/aE82OIputftfdHXWlQLW7LXMydlCusyV9EndjDjEl9we6dtX/CnSEjfeR8yfPIfh05nMXz6TA5mn2ZIYtUBPIdxw+ZFd4ZP9O5+IgrbcvbybdIqp67JKE5nwfFZ/Hb8J2KDGvJMm7e4NKqTiyo8G+lLx9HTde5dafIx9D570xFEO1/MEdLMJ5m4/WP61b+WMU0G0jgkgZxczzwTuzJ87dyyiyH6edqSM2wDXr7A964BegK/V/K97cC8Sr6+S5OqJM5RPnhw8VnYFXHnWZ6VIaKnPcHL+dYC8q1RGBUjr7S7nyC/IH5Omca/WX/qXVq18dYzti+GdKzTbEO6zqeZumUru06e5ONBA/h16K2MWjCbrSdPaPcGbvSwXv4tn9wTwb2i+jajOItJ2z/l5kZ9GdGoHxnFJ1maPk/vspzCF8azXuzQbUjX+QT+JrCE1fw+5Ytk1jC1xveyqyqnCwsxYuCVG3txdVwT7l8yj92ZGv07Mxld7lg9/Kr3mFZUnz636yuebnUrCaEt+TNjeZWvTzUf5ff0X0gzJ9M7dhAvtv2AyUc/Y1fuFjdUW4ovjkl1ZBvSd15HefikONz5vfY19aKWXtXCqZkFhYyeMZuSkNL/JrWCgskpMlPzO+Nyn7rTpXo6VDR/ppqjiAvOuehrFBRUVBQUQvxCaRqaiIpKsF8overdyPacTW6pVfrSYbahk+tk8MTFiDS5diHKE3YiPeiqwo7KohOrWZu5mXBjCgAda3XlWGESmcUnda5OG3xhws5RjpTU88ZJPa9BVdVtlIrsPBRF+afs/35dybe3qar6kmuqklQbN0zIVaQ8za5XAEVET3uKl62qlad2fMgTLUcxOv4BOtTqwsxj35Fryda7tGrhq96VjnUM6ToPpfob3yplS9pxbpryI/dd3YldGS4ac7jJw3r6V+9FsnJE9a0dOz+lLGZL9h4s9tK5nSj/2uRaslG1mS52C74wIehtDpWu8y20WiQD7RbKypmxYTvXNGrC3MGjeGLVEhYd3q/Njd0Q8tTDr3p7VUSfmm1FvLj7B4yKHzFVNA8rX0jLs+SwNnMF4cZIesb0576E8azJWMovqdPdUzS+4U4RkL7zbgLz7bqFT6DmbtXKqWaLFXIh1BjAzBEjSMrNZvyKxRRZNXCgDJ9ogqeFT1RUAgyB9KjXly61r6ZuYCwnzCmsO72KDafXnnmdAQN2rSdjzkH6smr0dJ08asdN6N2+yRFEavHkKLmWfFLNUZwqrsuwuDE83fpNrqvXDwXtWn7qjWwfVYo8H9TzUBTlEqArkAYs0rkciTO4+egd0K/1fzkiOtoTvHyq+DRP7viMb4/MoUV4G55p/Tbhxki9y6oRvuhd6djqI10nPloeBQBwurCQt5b9iZoHUYFBfNNvME0iojR9D3n0jnsR1bcHTUc5WhhGgCGQcYnP82DzJ912frZW+IJTfcGf0nXei79JvCMCAHafOsWQKTPYnXmKz24YyONdrtJ2ls8Lj94RYd5ZNJ+qqFhUK6nmqCpfBxBhjOKq6F70qNcXP8WPE+YUDps0Cj05ga8eBysC0nfeQ3WP3tHCi1p4VUunFlutzN2+iz5NWzBz0AjtjrLzkmN3QN+xqWjuTDVHVepNo+JP24gOjG02iQH1hxNgCGJj1lo+PfQWf2Ysp8heGt5pGNyESa1fp3lYa5fXKn1ZPdzhOhk8cTN6DwKqIr0gXLiHnSOU2C2M3/YO+/N2MTRuNONbvkRsUEO9y9IM+RD9D1+Y2PMi7i/7+J2qqrZKvt9AUZQHFEV5puxje3cWJ6kCN5zdeS4ihE9E87QneFkt60L22NY3WZo+j3xrLgD+ir/OlVUfX3WudGy1kK7zALQOn5TTPKAOnWIb8tvwMfRp2lzbm7vRw3qGT0TxrsiuPVIQzKyU5SSEJfJU6zdpG3GZ3iU5hS+MZX3An9J1AqNU9ifiJCKGT04XFnL7zLnM3rGT29t1IDZU4+e0F4ZPQP95Z1F9WlX4pFX4JdzR9EFGNLqTIL9glpz4le+TPmF7zkYAAg1BtI3o4IZK/8Pb3Sko0ncC06R+baLCHX/GVTd8At4XPpm8eSsPzf+NVrXr8uvQW0mIqqXNjb0sfKKXQ0Wc+z1uLv07YsCPWv51GN3kAe5OeISmYc1JKjjIV4ffZXbKZEzWPAxlMYMgQzBD40ZTPyiOR1o8ww2xN7mlVulLp3G562TwRAdEmmC7EKI96BwhqySXF3Z/z3v7v6duYAwTW71KuDFC77I0RT5ES5E7s8VHUZRgYDRgA769wMt6A18Cr5d93K4oyipFURq7p0qJQ+gQPhEhgCIanuDl9KIMpiavI9UcRcPgJrzU7iM6175a77KqjS8slFWG9KvjSNd5FkaTonkAZduJE9w0ZTpHcrL4qu9gnr7yWowGjYfYbgyf+Hr3ExEn/Mr54+Q6Ht/2NrmWbP6v+USGxY3BT/HTuyyn8HaneusYVbrOM9BigUvE8EmJzcYzS5dz4w/TOFFQ2oq+bkioZveX4RPXIKpLKwufxAQ1YED94dzXbALNw1qzNftfvk/6mGUnF3Cq+MSZ1/WJHcz9zSZwb8I4Agzu+zP1dneKhPSd+AQFGvnqpVtoUM/xLrveED7RyqvLDx5i9E9zCPEP4LXuvTW5J+CW8InsfuI+DGU95uyopJqjGNBgGP/XfCIdal1BriWHGcnf8P2Rj0kzH6PEXlz22tJ/Z4MajqR5WCs2Zf3Nxqy1DKg/nPsTJmDA9eNW6UvHcJfrPCJ4oihKX0VR9iuKckhRlKcq+X53RVG2KIpiVRRl+Dnfe0dRlN2KouxVFOVjRVGEOYNF9ACKyBNvF2Nt5mYe3vIaM5K/Jd+aB0Ckv0YpTgHw1YWwyvDGiT0vYgQQBSxRVTXlnO8VAq8ClwO1yn5dC6wCegArFEXRcDbJMxDadbL7iRB4kpOTCwPIKE7n9vix3J8wweOOBqiILzrXWxfPXIB0XTXQ23dah0+O5+Vz64+zmbZlGw9c1pmJV1yj6f0BefSOmxHVt6nmk0zY9j4Lj6+iQXAj7Gr1J9LduWhWEV9wqhf6U7quGujhOm8NnwCcyM/HaDJwT/vLWT7yTro11HCN103hEz2O3tETUeeUj5trkWqOIsQvlJbhbbk/4XFuiL2JnJLTLDnxK5OPfnqmy0n5Me5tIzrQM6Y/WSWZNA9rzSvtPqJV+CVuq9kX3CkI0ndO4m7XpaRlExUezNcvjSKxieM/b3l6+AS08+r2E+ncPG0mE1YsBsCg1fSxG+aNZfjEPdjLjp+7Ovpynmv9INfHDCTCP4rVp5bw2cE32ZC1ljxrznnXda1zLVdF92J37lbmpc3g59RpLDw+m4SwRF5s9z6NQpq6vHbpS4dwi+vcu4pUDRRF8QM+ozRlkwpsVBRlgaqqeyq87BhwJ/DEOdd2A64CylvBrKX0P9Rq11btHDkFwUSFmvUu44KkF4QTG5qvdxlOkWsxMf/4HiCKHnVjeaj506w4tZAlJ+ZhVS16l6cJyeZomgRn6l2G7pRP7CUEnNK5Es/BYverss1oBaIVRdlU4fOvVVX92oHrylt2fXXuN1RVPQW8cM6X/1QU5QZKn9NXAPcCHzlapKfjMa4zGSHMqvltL0T5oCI4rNht73kuojm6fAAiupfTizKYtP0TbmxwHbc1Gcgzbd5mTsoUNmev07u0alE+ePE17x4pqefRfnWD76TrnEQU3xlNCtYw1dnLLkiJzcbLK1ayMTWVdVnHgNJJPLuq3Xucmchzg4fNpkBd3Fs+wSeCd0UdA1tUK98lzcWo+BEbpBJhjKJN5KWsP73G4XsYMNA7ZiDxoc2ZevTzM5s13IUvONWd/pSuEw89XedvAktYTarX5h7w3yKZlr5dufsII1pfwtQbh/PaulVM3rlVmxu7ybHu9qsIXhXFpwYU7KjYUYn0D2NI3CguiWxOsF8I23I28PuJX0gvSgNKAydq2f+i/Gtzc6PbybPkMD9tJkU2MzfE3sTY5pNYeWox89NmuqV+X3DnxZDzmOKhh+vM5hIefuJH3nltBJ8/P4I7nplG2qlch+oNzLdTHF69Pfg19aKWXtXCqSm5uZALShh8ccMgjuRk8c6/f6GJrV08b+xOj+o5H6ynOwMNAYxPvJPOtS+h2F7CztwDTE+eT5FtxwWvaRjcmJsb3YFVtbA1Z8OZ8eWajGVkFJ+kb+xgHmvxHF8ffo8Dpj0XvI8WePqaqbeM7Tyh40kX4JCqqkdUVS0BZgFnHQ6lqupRVVV3AOfGF1UgCAgAAgF/4KTrS3YeEXdWV0TUpLojbMzKZU3GRvrEDubJ1q/TNLSF3iVphux+8h9euLNMFDJVVe1U4VeVgzVFUdoC3SgdeCx29I1UVbXyX4uv7tWq1nPxHNfJ7idC4AlOtqOy4PhKxm97k5NFx4kJqq93STXGF53rQ351ynfSddVGGN+54uidxfsPkJNRhJ+iMO3G4TzY8Qo0b7cpj95xGyK71qraSDVH0b1eb25rcj93N32UYL8Qh641KH7kWLJoHJLAi20/oFud61xcbeV4u1MF9ad0nXvQ1XUidT4BbbufpOTmMmLqTFYmH+alq3vxdo8+BBg0bN8uj95xCSL41I6KUfFjQP0evNjmEbrUbs/JomxmJH/DlKTPz4ROANSy5VcDBobFjaFWQDRrM1ewLWcD+/J38kvqNNZkLKVnvf5MbOneY9693Z0aIecx3YMurktOyeKhCdOZumCDw6GTcvTufKLV0TtaEVDgx+m8QsZ2vIIv+9xEiNFfmxt7WecTvRyqlzuL7SXsyz9CvqWATw9O56MDUzhkOkaqOeqCgYhI/1psz9mETbVzbd0b6FT7KoL9QiixF7MtZwMzj33H0vT55FvdE6bxoTVTYcd2nhA8aQhUbPmSWva1KlFV9R9K28CcKPu1VFXVvZpXqCF6DwaqQoTBgrMU2Mx8emg6L+/+lAAlkHGJLzCwwUi9y9IUH3mQVok8GkAYypOT36mqanPy2oyyj77WotLzXOdj4RMQz9GeEgo9bj7FxO0f813SKgASw9rQPvJynauqPr7oXOnXSpGuqx7C+U7r8AlAsNmfTHMhk664hu/7DyUqMEjbN/CRo3dE8K7orv3q8B9MOfor7aMu56lWb9IkpFmV11hVC+tPr+H7pI/Zl7+TkY3v5sHmTxLmxoWzcrzdqV7gT+m66qG767RY4NJqkQy0dW2BxcJDc3/j03XrGdayLe3qavxvTB694xL09mmQIZDn2jzI3U2HERkQzopT//Dqns/5OW0HdmxnjtWpSOuI9rSNvKz0E7W0+wlAqjmZpSfmMTvlB0zWfJqGJrrxd+L97tQJ6Tvn0c11madNzJr2DwBNGtRm+A0dHL1U1/CJVvfQyqk2VeWF5St4ZcUqro9vxtwho2gQptFz2ovCJ6CfQ/Vy57y0P/i/LS+y7vRWsi1nd8esLHyyJ287U49+zocHXsbfEMDNcXdwSWTHM99PLjzM6lO/c6Lo3JNdXIv05Xm4zXWeEDypNoqiNAdaA3GUiq+noijnHbqtKMr9iqJsUhRlkzWv0N1lnocok2wXQu/BQnXZlrOXh7a+we8n/iTXkq13OZrjQ0m+KvHwyT2PRlGUIGAMYAO+q8YtupZ9PKJZUV6Oo64re+0Z39lMGox2dAif6B1AEdHRnuBkOyo21U6qOYoeMf24r9kERjW+lwCD/oGi6uCrzpV+LUW6Th+qM7azFRY4dG+twydFViuPz/udF5b/Qbe4xiy8+XYurRer6Xu4swOZnu4VxbmiulZFZV7aHzy14z1U7IxLfIG2EVVPvttUG/vzd/FL6nQWHP+J2KA4Xm73IV3rXOuGqs/GF3zqif6UrtOHas1jFl3YdaIskoG2rlWBD/9eR5/vJrPl5AkA7RbLwGv9KoJT9fJpkb2YZelrSSpI5X/7v+P7pJ/JseShoJBqjjrT5aQi6UVpfHn4f2zI+osBDW7mlsb3EB/aHIACm4m/M1cyK+V7duRuOu9aV+ML7nQX0nfup7rzmJaSs30XkGtjSK/2PH5nTx4b3QPFQc3UNHyiRbCzpmjZvXPqlq3c9/M84sIj+a7/UO06drohfOLu7id6oYc7i2wXPtLoQp1PjptTeH//S5wsOs5NDUfRILjRme9ZVIvWJTqE9GUp7nadJwRP0oBGFT6PK/uaIwwB1quqalJV1QT8Dlx57otUVf26vB2NIVSDA9c0QsTFrYp4YgClyFbMt0lzmHFsAwCdal/F6CYPEOLnPaFk+TAtxQt2l3kqNwO1gN9VVa00xqooSkdFUc7zj6IovYDxZZ9Od12JQuJy18HZvvML08h3Pnj0DogxaVcRT3LyK7sn80vqMrrWuZYnW71Bk5AEvUuqNr7oXOlWQLquJrh1bOcX4vjP+K44emfGth2M/PEnUFXe69kPg6Ozoc4gj95xGyJ79pDpGI9tfYd/Tq/mSMF+h6/LKslkzakl/JD0CUcLDjGq8b082PxJQv3cOy/iC4FOD/SndF31cavrjEEXd51o4RMtXZuck4PRZKBbw8asvvVe7r+ss2b3dtc4V4/wid5e1cun605vZdL2d9mbd5hiWwnw37E6lR0hcLokgwP5u/kx+WumHf2CZmEt6Rc7lEDDf53ssksy3Vb/ufiCO92E9F31cPs8pn/A+b774rMVzJm3iVv6X86rj9xIgL9jx6/VJHwCYoRPQLtQ559Hj3Lz9Jk8vXppJTG8GuAGl8rwifso7w4W5R/BcXOtSr9fbC9iY9ZawowRXFFbjBPIpCsBN7vOE4InG4EWiqI0VRQlALgFWODgtceAaxVFMSqK4g9cC1TZtkuEHdUV0XtAUBUiPPSqQ+mAoj6dal/Fs23e4bKoLnqXpBly8PEfHjjB5+mUt+y62Jly7wMpiqLMURTlg7JfK4A/KD3b83lVVde5ulDBcLvrNMdHu5+Ihic42aramJY8nxd2fYzRYGR8y5doFNJU77KqjS86V4Y7petqgPC+0zp8suvkSW6a8iMPLJmPXVUJMPgR6q/R+dnlyKN33IbIQc8Cm5n3D/zKQVOAU9dZVSvFdjNGxR8VlZbh7WgR3sZFVV4cb/eph7lTuq76COc6kcInoL1rdx85yfKjh3jmymv57IaB2nrWC8MnoP9YVi+f2ildbK6swwlceBf3pux1LEtfQKuIS+hff5iryqsW3u5ONyB9Vz2EcJ2qwqffrOSzb1fRq2tLPnxqOGEhjj1TA/Ptuh69I1r45HBWFruOnAJgQuerePKKa7TbOCHDJ5qg9zhURSXCGMZDzW+jfVSr85xZ7tZjhaVNMZqGtsAgSARButK9rhPjT/0iqKpqBR4GllIqoNmqqu5WFOUVRVEGASiK0llRlFRKUztfKYqyu+zyucBhYCewHdiuqupvjr633gtaFRFlou1CiDwBdzHmpf3BE9vfIceSzT0Jj/FAsyeoFVC9h9Dlta7kunr9NK6wZsgHaikeNsHnsSiK0hq4mtJzPRdf5KXTgK1AZ+A+4EGgBTAb6K6q6msuLlU49HSdpvhg9xMR/ewpTt6dd5BHt7zFgrRZpBQmAeCnOLY7RUR80bm+6FfpuprhKb7TekEst6iI5NTSXdnPdLuWBcNG06JWHU3fQx69415E9+yFFs7OJcQvlG51ruPBZk8SH9qc3bnb+O7IR2zLKe0QalT8CTK497+5t/vUE8Kb0nU1Q1TXaRU+EW2hDKDAYuGxXxbx5uo19G3agvnDRtMsqrZm93dX+EQevaMvLcKaEG4MJdUcdWZXd0W2Zq+n0FZAYwE7dnq7O12F9F31Ec11s3/dyMtvL8Bqs1FisTp1rQyfnHsvA7X9ghnb8Qq+7juYMH/nQu0XxMvCJ3o5VG9vRgVEUCsggvsTRtI2ovmZcaeB0vlcP8WPuJB4AE4VnzgT+BQBX3WlHq5z7+pQNVFVdTHn/AdRVfWFCv9/I6XtvM69zgY8UJP3Ln9gBYdd+Ewrd5JTEExUqFnvMi5I+YMvNjRf50oc52hBKuO3vs+NDXpwa+MBxAU3cbpVolHxp1ZAHQY2GEmPen35Le0nNmWLEXRONkfTJFi/1o+iUD65lxBwSudKvBdVVfdC1UdBqqr6HdU7S86rcbfrEqJq0SCqNodzsqpRbRWYjBDm3ECvJojgahH9nF4QLryPC2xmpiavA6JoH2nkscTnWXh8Dhuz1updWrXwReceKannU26Vrqs5eo7tnMFoUrCGadpoGIDluw8xoFlL5g8bzbN/LufXA3u0fQM3OVhP94riXNHHvqnmKOKCc876moKCioqCQqR/LUY0upO2kR0otBWwLnMVc1KmYMd25vV96w+md8wgZiR/w79Zf7qtdl/0qUhI19UcUV1XvsBlqeFpWv6mmt8DtHftdxs3szv9FB/eNIBuDRtrO9YtXzBzsWPNpkC3ulUEp4owbg03hnJX02EcN5/i00PTSTFHApzl0RBjGIGGIGoHRGPAINRiGkh3Vgfpu5ohmutW/rmPlX/uoyTSj/DQQOrWCuNI6mmHrg3Mt1McXr09+jV1olZu1tKpL/2xkoOZp3m+13X8PPRW7l38Kyn5uTW/sYvHqr7iUD3HoccKj/Pb8ZXc3XQ4oxoPZF7acjZl7wJKnRkb1JD2kZ0AOFHk6Olb7sMXXamH64TveCIKIrT0L0fE3dXnonfyzlns2FlwfCUPbH6RnbmbAbiidneHU+xW1cK/p/9kWvKXnCw6wZj4sUxq9TrNwlq5smyH8cVjAC6E6LvLJBJ3EehnZNHNY7i7fceqf/KoDrL7iRB4SvcTgDRzGFnFmdweP5a7mj5CsF+I3iVVC190rnSrxFsxmhTNu5/8m5LKoMnT2XEqnQ969eeN7r0J9NO425M8esetiOzZVHPUWd1Pytsfd697A/c3e5y2kR04VXyCbw9/wK9pP2LHdqYdcpOQZlxbtw8qKrc2uY+Hmj9FmDHCbbX7ok8lEnch0tE7Wrt2fUoKfb+dzLTd2wBoVTsaP62OCgCvPHpHBKeK4NKkglS6RXfktsYDifIv9V25Q42KPw2DG6OgcLTwEH6KsdKuKHojvSmRQECujSfuup6vXhrF5W0aOXydnp1PtLqHlj79cdt27przMzEhocwdMoogo0b+c/F8sbvXcX3x6J01GRv55OA04kMb8ECzW3iw+a10rXMZXeuM5OZGd9Iq4hIOm/axNXu9LvVVhRxnuh4ZPHESvRe1KqL3oKAqPGmxq5xci4lUcxTpRXXoW38Ij7d8mWFxYwg0BFV5bb41j01Zf/PTse+YmzIVUHm0xbPc3+xxalfz+B6tkQ/UUjyhvbFE4moOnT7N2tRjvHBVT2YMGkFcuIsWEnwsfAJi+tkTfHy6JIcnd3zK9OQFXBrViadbv03L8LZ6l1VtfM250q0Sb0br8ElGQQF3zJzLl+s3MKhFaxq6wsHy6B23IrpnyxfO4oLjGdTgFobGjaZ+UByrTy3h2yMfcrhgPyX20t2Bduz4K/4MjRuNisr8tBn8kjqdmKCGvNzuI7rVuc6ttfuaTyUSdyHKIlk5Wro2p6gIo8lAjD2MOUNGMW3gzdQJ1jDULo/ecQl6ziPnWwv46dhiduTsY2CDntzSeABtIpoDkFkSw2W1utAzpj+KAodN+7GoJWfCnKIhF9QkEvj6q1WcPJ3PB08N44Zujm8M9pbwiVZO/edYCsOmzeSltSspsmrcqcTLjt7RC728uSl7F+O3vUlK4Qmuie7ExJb3MKrxjTQNbcGOnE0sOvEzWU6eKuFupCtdhwyeVAPZ/cQ5RJ+EqwyrauOxrW+zNP0vute9gWfbvEv7yMsduvZ0SQZ/Z67guyMfseLkIlqFX8KLbT9gZKO7XVy1Y8gByH/IBTKJL2O12fm/OfN56veltKsbw/CW7Vz3Zm7ufiKCp0X0sycEQu2o/Jy6lEnb/0ex3czltbpV+15BhmASw9poWJ3z+KJvpVslIqFo2AFd6/CJTVX5319ruf7b7zmSkw1Al/rndaKuOW4Mn+jZ/UQERPRs+W7sAIM/tQKu4J6EJ+gVM4DTxaeYlvwFv6b9yMmi4+dd1zNmAPGhzfn39F+sOrWENRlLmXXsW/bkbmNk47t5sPmTbu1M5os+lUjcgVaLZFp2P9GS04WFvPLHKjrG1Oe34aO5rF6sdjf30nCnCE7Vy6V5VhNv7fuaxSfW0DumG5Na3ss77SfyxiUTGNPk/6gdEM2G02v5J3OVLvU5i3SnxJfJyMzn0cd/ZOeB47z88ABuu7GTw9fWNHxSUyeK5tTknByW7zgEwIBmLXnhquu06yQmwyeaoJc3M4qzeGXPZ7y172s+PjiN6ckLeGPvl7y7/ycOm/bpUpOzSFe6BvduQ/Yy3H1m2MUof7DpfS7nhRD9/OvKKLQV8fWR2azJ2MgjLUZwT8I4Xt3zBJnFJ6u81o6d0yUZBPuFYFdt2FUbNtX1Z607gy+eZ1YZR0rqkRBwSu8y3IrF7ifcpLhEP+bu2s26Y8c4Qenz+ZK6MaSb8skwF2r/Zi4+y/NcRPC0CGdmn4sIZ2hXxZGCFB7b+i4GDEQHQv2gRhgVP1LMRx2+R1xIE+5tNoE8SzbTjn5JcuFh1xV8EcoHMb7kXFHcKn0ngZqfuV2R8sk7rc7OBsgsKMSIgevaNeWbfkOYu28XL/y1gkKrRbP3cKd/9XKvSONhkTyrolLLP4Ihcb25tm4XwowhLEv/m83Zv5F+gTO3GwY3pl/9IdhUK6mFRzFgwI6dPXnbSS9K42jhYdpFdCAxvA3bcza57fcix6+VI10nMdhLF8mKw6u3t1ArT2p1H61d++vuPezPyODTIQOZPXgUL69dyY97tmty7zMLZi52rLvdKoJT9XTp1OR5bMzeyaAGPWkQVI/agVEkFx7n9xNr2JK9h2A/C0bFn1BjGLmWbF1qdBRvcad0nQRAsTnnBVNBMZOens1TT93I8Bs6MH/lTkyFjj1Ly8MnerlVS6dq5VOjycAlETHc3f5ymkfV5uHlC8kr0cBNLh6rutOhevpTz/XX7Tnnh0wKzVHEBedQP6gR3aJ78GvqDOzY3F6bI4jkSm/xnQye1JDy1JzeC1vliLjAVRGRJuEcZX9+Eo9tfZc2ES3ILrl46ERBQUWlYXATroq+jm7R15FryWZ+2iy25fx75nWDGowkvSiNDVlrXV3+RRHpoaon5buzRVgkk0j04Hhe6XM5QPHjo1EDiAoM4tk//+D3Iwe0fzMdwiegr6dFdLMnBEJL7KWLrqnmKMY2G0XLiLb8fuJXlqcvwE7VO1BOmFNZeHw2Xet0Z0LLl9iYtZbZKZPPHCXgbnzNudKtEpHQMnwC2k7glfPn7qN8EvkPD13ZlQ6xDXhk2W/sOZ2h3Ru4aXEM9HWvKM4VZdwbZAhkYqt7aRnelOSC48xJ+Z2FJ1YDEHeBTXmhfmH8lfEHbSIuY0jcbTQIbsT602s4UZRKVkkmq07+zo6cjWQ4sCFDa3wxzCmROEpNwydQc1dqHfbUyrV7TmUwZMoM/jeoL5fUi4E9mtz2P9wwxtUj2Km3U/Ucs+7NO8z+vCRCjEEEGgIosVvItxaUfTeKx1oM4vJaXfkh6VMOmrT+C6UtvjYOlXg3AbkWSiL9HX69xWrjtdfnU6d2GCZrMQZFwWg0UGJxbAFcz2CniOGT//21lmM5ObzUuxe/Dr2Ve3+fR1KuBgE8LwqfgL7+FGUcCqXzuR1rdaZHvb40CG7Md0c+otCm4TmNGiJdqS3yqB2NEKGtfzkitveviIgtiKvCptrZmbv/zNnY51LewlhFpW5gLHfEP8hV0b04YU5h6tEv2Jy9Dpta+gNN09AW9Iq5kduaPMCkVq/TQoBjAGRLqVLk8QASX8euqoz9eQEp+Xl80WcQH11fGkLRHDcfvQPub1F8LqK62VN8/MbeaWzN3sCNDW5mXMsXiQ1qWOU1BTYTf2YsY0byN6w4uZB2kR1545LPuSb6ejdUXDm+6FzpVokoaHkUALjm6J2P/v6HMT/NIdToz6/DbmNk60s0fQ/A7Uff6YEovhVh3FtkL+bzQzNYdepf3tz3FYtOrAFKx6+p5qhKx7cHTHv4OXUab+59is1Z6+hRry+9YwfirwQAYMemS+ikIr7mUonEUWpyPABod/SOVmjp2tyiIu6fPY+XF68EoHmtOjSOiNTs/u7wqx5zzyI4VS+X2rFjshZyuiSnQuiklJ9T/6XAZuKhFk/RO2bgmXlhUZHelHgTAbkWAnId7w6pqpB52kRAro0Jd/Tk42duJjLc8WdbTY/eqQmiHbsDMHvnLu6YPZeooGDmDbuNuiGh2tzYxXPF7naoLx69UxkLjq/kwwOTaRranCdavUL9oEZ6l3RBpCu1QwZPNEbvha2KiDA4uBgiTMRVh8om6FRKU6Nd61zL3U0fJSaoAdtyNvBt0kccMu0967X96g+lxF7MusxVWFUrD7d4mrHNJlE3UMOzZquBfLCWIhfIJL7O4awsRk6bxYdr19E/IZHlt9xFXHiEa95Mh/CJ3p4W0c2e4OMCm5nX9/7Ie/u/p25gDE+2eoNmYa0cujbVnMyS9HlMO/oFe/J2MLzRHYxLfJ7GIQkurvrC+JpzpVslIqH1gpjWAZR/U1IZOHkaf6Yc5bQrjr0DtwZA9QyfiOJcvR2bak7n00PTySjOOrMsVj5+Lf1+VKXXWVULc1KnsOj4HC6v1Y2BDUcACLO45msulUgcRYZPLowKWOx2jCYD717Xh4XDx3BDfHPN7u8uv+oRPtHbqXq79FxSzScZv+09tudsZFDDWxjb/EnCjS6aN9EIX9wEIfFunAmflLNzczKtmsbwzcujiIuNcvg6vcMnWnhVS59uTE1j2LQZfLblXzIKC6q+wBlc7FEZPnE/azI28tzOjwhQAni85Us0Cmmqd0kXRLpSG2TwxAWIsLBVjgiDg6oQ6SHoDKnmKNLMtQAINARxVXQvRjW+l7qBMazJWMqsY9+Rec5OsPaRnWgZ3o4t2ev5Ne1Hfkz+isUnfqZuYAzPtXmXEY3uxKg43i5Oa+SDtZQjJfXkIpnEp7Ha7Xz6z3qGTPuRhYf2kZqfB+CaZQbZ/UQYPMHHazM389Dm11h1ajFHCw46dI2CQom9mN152/g7cwUphUk0DU3k8ZYvE+lfy8UVXxhf8610q0QktFwQA+27n2Sbixg7ZwGrdyUBMLrtZfRs4oKwnBvDJ7L7iRiOtVN5m+2TRaVOqixUsjF7HbmWbFqGt8OoGM8KreiNHL9KJJUTmG/XdZGs/B5a7tTW2rXj5i0mKTeHr/sN5pkrr8Vo0HCK3AvDJ6C/U0XbMFFkK+bVPdP4/NCPNAxuTJCfGD9zVIX0psSbcDZ8smrtfsY/M4uwkEC+fflWOrSKc/jamnpVhO4nWvo0NTeP79duBqBtdD3e79mPEKNG61peFj7Ry58iefOA6Sjjt73DP6dXc9x8TO9yqkS6smbI4IkLkQEUxxHpIegMKioZxfUYFjeGIQ1vBWBu6lQWpM3CbCs8a+IuwBBI//pDMVnz2J27lRJ7MSeLjvPHyd+YlvwlazKWEhPUkIbBjfX67ZxBTuCVIhfJJL7OvoxMXl+6BqPJQIOwcP4YdTe945u55s1k9xMh8AQf51lNfH54KcmFVdepoKCiYlT8aRDciBsb3EyjkKYcNu1nRvI35FpKz6INMOjzd8EXfSu9KhEF0Y/eKSewwI/hLdvyff+hvHx1LwL9NPaljxy9I4JzRXVs75iruLZeF1LNUaSYI88Ln+RbcrHYLcQGNaR2QF2dqrw4vuZSicRR9F4kK7+PVmjp2rS8PEZN/4lpW7Zx/2WdmTVoJLWCNHSFPHrHZYjm0uUn13HfphfPHEPXufbV+Cl+Old1caQ3Jd6Es+GT3XuP8+D46eTkm3nr8ZsIDQ5w+Fq9g50iHr1jNBloHx7LTS1aM2/YbSREabTBy4vCJyC7nwBkW/L46OBvJBeGE+oXxugmDxDqF6Z3WRdEurL6yOCJG9B7YasiIgwQLoaok3EXIiG0EY+2uJ0r6nTnWGE63yd9zPrTa7CqVuDsFsatwttRP7gRYcYIWkW0p3VEe0L8QrGpNpIKDrLw+BxmHvuW5MLDev12zkM+XEuRi2QSCYSVBFBksfBNvyF80Ks/kYFB2r+J7H4iDJ7i4sqOv6tIuYd71OvD3U0fpXFIAvvydjIt+Uv+zfrzzOtua3I/z7Z5h7jgJq4uuVJ8zbfSqxKREP3oHZuqcuuPs/lu4ybuuKQDvw0fTes6Gi/+u/noHdn9RBzHBhkCSQyP5+6mw+kd0w0FhRRz5FmvqR8ch59iIL0oDYu9RKdKq8bXXCqROIo8eufClNhsvLxiJY8uWEiBpYT8kmLN7g3Io3dciEguBSixW0g1RxHufzm3x49lXOIL1BE0rFmO9KbEm3A2fHI8PYcHx03jqffmU2Au/fnWoDjuFxk+OZs5O3dx55yfqRMcwoJhY+iXkKjNjV3sUXePTWX45D9Cje3pWKsrE1u9RlxwvN7lXBDpyuohgyduQoSd1eWIMECoCtEehJVxRe1LGZd4B1fUac++vCO8f2Ayi05cuO3/qeJ0Pj/0NotP/Ezn2ldxZ/zDXBl9HQZKU/Al9uLzjuYRAV/cjV0ZsvuJxNc5kpXN8Kkz+ejvddzYrCV/3HKX7H6iISJ62ZPCoOeGT8p3a8cENWBA/eEMbDCSOgH1WHh8DtOTvyK7JPPMa1qGt+WyqC7UC6zPxFavcWvj+/BXHN/xohW+5lvpVYlIiH70TonNxpur/+TOOT8TFRjEz0NupU5wiKbvAcjuJ25EFMcW2YtZeXI96eYMhsX1YWCDntQJ+C/UWSegLp1rX0WtgGhOFh3HbCvUt+Aq8DWXSiSOIkr4RNSjdxbvP8A9P/2K1W4nKjCIBy7rjJ8TC5BVIrufuARRXFqRvXmHeWfft9QLrM+Trd+gY62uepd0UaQ3Jd6Es+ETU0Exu/9NJiDXxk09L+GTZ28mKtzx55re4RPRjt7551gKg6dM50BWJl/0GUQvLY+K9aLuJ3ofvSMKW3L28MzODzAoBsa3fJEuta/Wu6QLIj3pPDJ44mb0XtiqiCiTbhdCxAFEOf6KkZ4xXWkYHMOiE2v49NB00osyUFAuuPs6vSiN/fm7WJo+jxd3jWNv3nYGNRhJn/o3AZWfpy0ScjBSilwkk/gyFrudT9atZ+j0GWQUFtA7vrnr3kyn7id6elpUL4vq4nNJNUeRZi5t6amiEhPUgNubjOWG2JvIKE5nevKXLD+5gHxr7pnX+Cv+DGwwkkJbAfPSfmRZ+nwujerM6+0/56roXrr8PnzNtdKrElFwxdE7WgdQ1h5NZsAPU5m4agmnzaUBAM3O0S7Hzd1P9EIU34rg2L35h3l739dkFmdze/xgHm1xO7c1HshlUYMZ1eQxro6+ntMlGazLXEWR3ax3uQ7hay6VSBxBi+MBvLn7Sen9DAxp3Ianr7yWGYNGEBuqYft3L3WrCD4VwaUV+ef0VsZte4sT5lTuavoIg8uOZhcZ6U2JtxCQa3E6gAJgySqmbfP6fP/abTRr5Pi/Bz3DJ1rdA7Tz6Yl8E7f9OJtXVqxi9bEkTe55BjeET3yh+4lIa66HTMcYv+0djhYcYkz8WK7WaR7WEeTaqHPI4IkO6L2wdS4iDBIuhkgPw3IsqpU3937F87s+4oeknzlRlAGcfbTOhQIoCgqFNhOLTswlpySLq6OvJ9K/1lnXiox8wMpd2hLJ3lMZDJsyg9eWrgagTZ26XtP9BPQPiYoYQBHRxZWhopJqjuKa6N7c3fRR4kLi2Zm7mclJn7I5+5/zXn913V40CmnKhtN/serUEpamz2d68lccyN/FiEZ3MiHxJWoFuN97vjagkU6ViITo3U+yzUUs3X4Qo8nANXFN+HP0fVzvCgfLo3fchgiOPVl8mud2fci0o/OICarDkLje3NV0GC3CmrAv7wgL0maxL3+nrjU6iy95VCJxBlG6n2iF1p6dtnUbjy/6nbbRMfw+4g7td2zLo3dcgt4ePZeM4iwm7fiE2Sm/czB/r97lOIT0psSbcDZ88seavTw6aQZGPwPfvHwrPTq3cPhavUOdooVPLHY7U7dsRclXqBMcwsLhY7iyYSOn7vHo5VcS5l9JJ2I3eNQXwicgjjdzLSae3vEFM48tYlvOBr3LqRLpSseQwRMdESmAIsIgoSpEeRhWZE/eoYt+P8gvkFRz1FndTMoDJhnFJ9mXv5MwYzjtIju6tE6t8bUFsQshF8okvozFbqfQYsFoMnDfZZ35pt8QPuzVn1pBLnCJD3Y/AXEWxCoioosrUicgihGN+jO80e1E+Ndh9aklTD36Banm5EpeW5d+9YdjthWQVFB6VJ5VtbAzdzM/p0xjbspUskoyifKv7e7fxhl8ybUy1CkRCdHDJ+WcPGUi3ZTPt/2G8Fr36wkyauxKH+h+ItI4WATHzj++gqd2vMerez7n7X3f8MbeL3lz39csPHHgzKYKo+JPnYC6+hbqIHLcKvEFakWEMPyGDk5dI0r4RNSjd+bv2cvgqdNJy8/ju/5DGdn6Eqfv0adpc77uexMxlXVNkUfvuAQRgpwVsal2Zh5byNKTpbv+r4+5kYENRuCn+Olc2YWR3pR4E86GT/YdTOf+R6dyKCWD1x8bSJMGjs8FadFVrCaIFj4pJ8oSRKCfkek33syDHbo41O//4Y5dGde5G3/edi/9ExIrf5GXhU98/egdO3ZmpyxmX74fBvy4M/4h4kNc2GW9hkhPVo37txFLzsNsCiQ4rFjvMoD/BglRoWK20C1/GMaG5utcSdU0DI6hd8xVTD06jxRzJABxwTlnvh9g+E9gJXYx/vydpfwh2yQ4U+dK9KN8kSwh4JTOlTiOza7oPiEg8S6e+W0Zx7rmMLbrFVzdKJ4X/1rBosP7tX+j8oFFmFX7e18AvR0topdFdXGLsHhuaTyA9pEtybcU8PnhGWzK2kmD4GIUlPM6izUIbkxq4VEahcQzsMFIYoMasjXnX04WHSfbcpq/MpezLWfDmaN59EK6tvpI30lqQvnknUWjLvvlk3jWMO26HB7JymbEtFmMv/oq7uvSia4NGvHYH4vYnanxz6Umo1vcWz7Bp4d3cwqChXCtCI7NseSRk5NX6fdSzVGMbdaH7nV7MydlChuy/nJzddUj2RzttR6VrpOEBAXw+J09uaRFfd78dhlFxY49rwPz7RSHV38/olae9Ddp61qtPHs0O4cR02bx0JVX8MfRw05dG2Q08vDlXWkbHUOPxgm8++9ffLN909kvcqNb3elVEXyaXhAu3Fg11RxFoCGOG2KvoVVEe6Ye/ZyTRcf1LuuCiOZN6ToJgGJ3PtgRkGuhJNLxo0mzsguYMHEmXS5vSvLxrNL3VUB1UC01cWtNfaiVl7Uctybn5DBs6gxeG3A9k7p2p2NsQyasWExeSeVe6hjTgPsv68xpcyHJuTl8esNAFhzcy+Mrf8d27h+Ciz3q7rGpXv4UYfxZEbO9CU1Cm/NoYmfmpkxh3elVepdUKa7ypLf4TnY8ORe7a3ajVYUIO6srItLOr8oQLcVeGY1C6nN9zJU82+b/SAgtbSdW8fidWgF1iA1qiMVegsVeomOlNUem/GT3E4lvY7Hb+WTdegZP/ZG0/Dw+u2Egg5q3ct0byu4nQiCShwMM/twRP5jLolqxMWsH/zvwHRuydmAvO3qnPABakV25W/jy8Lu8tfdpThSl0K/+UPrEDibI8N9/a71DJxWRrpVI9EH07iclNhtvr/mT22fPJcw/gA4xDTS9/xnc6F7Z/UQsx57L7JR/SSlMYkz8/3Fn/MME+4XoXZJDyF3cEm/l+IlsvpnyJ72ubMm3r9xG4/q1HL62pju0wbuP3imx2fhg7TpyM4rwUxS+7z+UQS2qHueO69SNttExrEo+wvyDe3n6ymuZN/Q2mkWds3teHr3jMkScN/7qyCze3PsVtfzrMKnV63Sv21vvki6KdKZERPzynF+Yd7bzSYnFxtr1hwjItXFZq4Z8/9po6teNcPh6PTufaHUP0M6nhRYLE+b9zst/rKR7o3ievvLaSl/nbzDwbLdrCQsI4O31f3L/knl8unk9PZsk8M+YB7g6rsn5F8mjdzRDFGdmFGcxftu7HDLtZVSTe7m18X34K46Hx9yJ9OSFkcGTytChpX85IixuVUTvgUJViPJArIzduQdZmr6WluEJ3JtwM71juhEbVPowCvJrT++YgcSHNudowSH25+/SudqaIyfy5DEBEsmBzExGTJ3Js0uX8/uRAwDEVtZaWAt88PgdESbwzkWUCb0Su4Xndn3Il4dn8t6B79mVe/C815QHP8tRUSmxF3O6JINvj3zI/LSZXF7rSkY0vhMDfmcdkycK0rUSiXMoNm3uo+VxAKD9kQAA65KP0e/7qUzfvQ2Aqxo21t7Bbj56R88AigiI4thzOV2Sw1M7Pmd68gIuq9WZp1q9SZOQBL3LchjpUYnXocL02euZ+PwcakeG8O0rtxIRFuTULUQJn4h69A5ALUsw4QEBfHz9jbzdow/BxsoXQbrUj+P2dh1Iyctl3IpFvPL3Sl5au5LwgACW33IX4zt3O/8iefSOyxDNoxuydvDo1jc5mL+HoXFjiA1qqHdJF0WOPyUi4pdndjqA4mz4pJygImhQL5LvXxtNxzaNHL6upuETbzx6Z9rWbYya+RNvr/+z0u8/2bU7HWIasPjwAebu301WkZlvt2/iydXLSMrNZuqNw3m8y1WV31yGTzRBFGearIU8t/NrZqf8zpXRPRgT/6DeJV0Q6cjKkUftXAw3tT2sDL1b+1dExDb/FRGtHVQ5+dYCpiXPZ39+EnfFD+WBZreQUZxNka2Y+kF18TcYOWhKZtWpJZhthZUeA+CJiNaOUQ+OlNTzqKN3JD6KXdtWxOXYVJWfduwEIKxWAL8OvY0DWZk8s2Y5aabK27bXCB1crbejRfSyKC5efnIdwAWdWh4+qXj0XfnrN2T9RZc619AyvB0R/pHkWLJcXW61ka6VSBxHyyNztDwOALT3cH5xMcZiAwF+frzXsx8h/v68/Pcqft6/W7P3ANzqXr2cK5JrRTwywI7Kz6lL2ZGzj8cSb6HYw46OlR6VeCObtyVz3yNTaN81njxTkdPXy6N3Lk5OURGjf5zDo1ddydiuV9C+bgwDf56OtcLRDwZF4fmrehBkNPL9js3kl5R2N562ayu7M08yrGVbHrn8SnKLi/h+x5az38BNx8rKo3f0J8eSx3O7vqFZWGOKbWkAhBsjyLe6YL5EI6Q3JSLil2fGFuH4In15+MSZo3e2bD/G/42bxusvDuWjp4fz0bRVzF22zaFrtfCqtx29s/1Eeun/OaemxNrRdKkfV/p+BoVOsQ05mH2a3OIifj9ygMM5WQxv2ZZdGRdZb/Gio3f0HIuKMr9rR2XmsYUczD+Kn5Kiay0S55EdT6pCdj85g4g7rSsi6o6wDVk7GLvlJb45Mof0ogwsdgup5nSWpP/FhwemsPRkEqnmKFRUxjT5PzrXvlrvkmuMTMT7bvcTRVF6KYryq6Io6YqiFCuKclxRlKWKovSv5LXdFEVZrChKlqIoZkVRdiiKMk5RFD89avdVtN4JVpHibCtfr99Ip/oNWXbLndzeroNrekj4YPcTEGMH2bmI4uHKQidhxhBahjcFKu9+YrLmk1NymjBjBHEh8W6osmb4umf1RLrOM9Fq15crjt5xxfE7t86czd7TGbzXsx/f9BtM3WCNj0OR3U/ciqhj3YOmZB7Z8g6bsgsA6B0ziNoBnuEnOWa9ONJ1nklGZj4rFu4kINdGl0ua8MULI6lb2/EVJ3n0zsWxqSofrF3HnXN+5sc9288KnQB0bxRPsbW03VqvJgkMb9mWeiGhqMDm9OO8t+FvHlgyj8k7t174TeTROy5BRI8eNh0j1RxF24jLeKndh3SqfYGd/ILgrc6UvvNs3HH0TtrxbB4cN41/tiXx+J29uLpjM4evralXRXKqlj41mgwYTf8tTR/Ly+Gp1Ut5ctVSOtWP47v+Qxjesi3hAQEAHMjK5L0Nf7M06fzuxmchj97RDFGcuSl7F/9mlR6DPixuDFdF99K5Is/E3a6TwRNHkQGUM+g9WKgKUR6K57I0/S9e3f05L+3+lKd2vMd3ZUGUcrJK6lM3qD63x49lXOLzNAh2vH2bqHjroMQZfCl8oijKO8AfQCdgAfAesAioC/Q457U3AX8C3YFfgU+BAOADYJbbipYArln0AlApbaXY7/spbDqRxivX9GL24FuICHCR03TwtN5+FmES71xE9XDPelcyIfFuOtZqC5SGT1LNUWeO1Inwj0LBQJHNjMVeomepDiMXzdyPdJ1no2X4xBUBFC05lpPL6B/n8PrK1VwT14Rlt9xFtNbhE3Cre/UMn4jiWhEdWx72LLYn0Dt2IE+1fpOOtbrqXJXjSI+ej3SddxBu86NFk3pMeWMMnds1cepaUcInoh69sy75GLP+3Xne1/9OTWbssgXcsXAu9ULDeKtHH+64pAOh/qW760+bC1l+9DB2Vb34hgx59I7LENGj/5zO5GjBYe6If5AhDW/DIPByjbc5U/rOO3BH+KTQXMLzL/7Ma/9byN9bDzv9fjJ8cqH7lT7viqxW9pzOYM7+XXSf/g0bTqTyeJer6dXkv5BPsa20m4lBcaAGGT7RBJGcmV5Uh7qBsdzS+G5GNCo9Il3iGHq4TtyfZERFp/AJ6L+4VRGRJt8qQ8QkO4AdO4U2M1bViv2c3diFNjOPb/uQTw9OJyaoIZNavc6wuDEEGpw7m1c05KKYb4RPFEW5D5gITAGaqap6v6qqz6iqep+qqh2BZyu8NgL4BrABPVRVvUdV1YnAZcA/wHBFUW5x+29C4rLuJ8fz8rnnp195YvHvpJtM5JW4sC2h7H4iBCJ6OLXwBBbVwl3xQ7k+phvhxlAAUsyR+Cl+tAhrQ7OwluRZc8iz5OpcrXP4umfdhXSdd6DlQpbo4RMV+GHzFgZNmc7X2zaSaS4EwN+g8TSA7H7iVkTzazkZxVmM2/oWJ8yp3NX0EUY3ecBjxrLSo/8hXec9/PXPQR54bCpZuYV8+NQw7h7SFUfWasrRInwi0mIZuGbBrOKObYvdTkZhAX+lJtN39hRm7d3B2A5XcEe7DuddWz4j6G8wUDsomNjQczrTuMmtvtr9RCTyrCae2fkFC4+vpmdMf8Y2n0SIn4ZnO2qMt8zzSt95F+4In6gqLF+1B/8cG7HR4fzw+mhaNY1x+Pqahk9q6kPRwydQGioptFp4fd1qsovMvHDVdef50a6WGrRrg0bc0/5y7rqkI32atjj/xl4WPtHLnaI406raeGHXt/ySuoxr6vbmoRZPESqwK0VBL9fJ4El1kN1PziDCgOFiiLjwdTFUVFac+oexm19hWfrfXBrV+cxObE/HWwYm1cWbwyeKogQCrwPHgPtVVT2vVYCqqhV/mh9OaaJylqqqmyq8pgh4ruzTsa6rWHIxXNX9BGDe7r2Mn7cYgAZh4fx000jaRrvo34bsfiIEIjl4S84evjr8Eyoqdzcdxt1Nh9M7phuJYfH0rHcH/euPwqAY2Jz1DyeKPO/8UF/3rKuRrvM+RO1+4goPH8nK5pu/NmE0GWhdpy5rbruXPk2ba/oegM90PxEBUce5p4pPM3H7J8xOWUzn2lfzcIun9S7JYaRHpeu8kZS0LB58bBrLV+3mvpuv4tpOlSzMXAR59I6j9zx7er38Hb7etpGUvFzuvrTTeUfeGQ0GrmrYmK/6DmblqLv5ecit/Dr0Vno0bnr2zb0wfAL6+1Q0j9pUO98lzeGTg9NoFtaKSyI76l1SlXiyM6XvvBO/PLPTARRnwyflRFqNRIYF8eWLI7nhqlYOX6f30TtajV1rOmYN8POjeZ3aFe5X6tHyUMnR3Bw2p6cRFRRMi1p1zrq2Y0x9PuzVnxmDRvBstx48f9V1fNx7ADMGjaBuSOjZb+SG8IkvdD8RxZl2VKYlz+fDA5NpGtqcRxOf85q1U1egp+v0a9/hDZQ/uMKsbn/r8gdacJgLd407QU5BMFGhzidL3UV6QTixofl6l+EwJmshXx/5ialHA4kONGPAj1FN7mH1qSWkmY/pXV6NSDZH0yQ4U+8ydKE8fJIQcErnSjSnN6VS+hCwK4oyAGgHFAEbVFX955zX9yz7uKSSe/0JFALdFEUJVFVVjIecD2I0KVjD1KpfWK17G2gUGUnTyFrMHzaar7dt5KNN/5xpm6gZOnhaBD+L5uTywYkIHt6Zu59xW1/njvghXB/Tje51O535nk21MTd1GduzlwMQ5V+bPEsudmx6lVstfNmzLka6zgspn3SzaLBRxt+kzX3KcZWHVZNKltnMV30H8+uBPbz41wptO5GZjG7zrl7OLZ/wE8G1Io5z7diZeWwR23P2ExdcOo9VPiGo4pqfLbXExz0qXeeFFBVbeOP9xSxbtYd1R0rD1SFB/hQWOb7YFphvpzi8+nsXtXCkls4uXyyrqWdD/P1pGBHBwdOnMZoMWMNKFxPL73osL5fdmafom9CCFrWjyUgrnc8L9ffn7vaXc3f7y4kMDGJtylHySkqIC4/g+/5D+XzLv3yw8W9sZQtw7hjX6uFUEcatonl05an17M49iL+h9CiPcGMk+VZxu3F6sDOl77wYvzwztgjHF+jLwyclkf4OX5OUnMkDj07lpecH8/JDA2jRuB5fzPrrTHCiKmriVa2cqpVPq+PS3i2a80CXzjy37A92pKeX3avsv0cEWO128svGqH4VunV2a9iYJ7t2p210PTanp/HJ5vUUWEq4PLYh97a/nG/6DuaxPxaRnJfz35u5yaHu8qee7hTFmWsyNpJmPknzMD+PGF/qiG6uk8ETLXDj5Nq5uPOhVhUiTcBVhkgLX45SZC8m1RxF45AGtI3oQJfa1/BnxjIWHp9Dsb1I7/KqTXkq3kMHJzXmSEk9bwufdC77WARspVRgZ1AU5U9guKqqGWVfaln28cC5N1JV1aooShLQFkgA9rqkYolDaDUhVxmb047T97spPN3jWh7seAV9Elrw5KqlbEpP0/y99PC03n4W0cmiDFDsqPxw9BcWnVhD59qX4Kf4oQD78o+wPz8JMGKgNk+3noTFXsLUo19wqviE3mU7hQdPAIqMdJ0Xo9XEm5aLYuAaDx86ncXwqTMZ27ULD3a9onTybvVSVh9L0uw93B381Mu5IiyYgbjj3D15h9iTB3HBcF29frSJuJRpyV+Sa8nWu7Qq8WGPStd5MZu2HiUAqJdYh69fHsXkeeuZvWSrw9drET4BcRbLoOYhz/4tE7mvS2deWP4H/6akYjQZUABLmB0/RcGmqthVFavdToCf35nr7r+sC2M7dCE1P5cPNq5j6q7SP4fE2tFM7HI1o9teyt9pyfyTdk4XRjeMa93tVBHGraKMU8s5WXwaiKJjVDATW73KmoylLDo+V9jFNQ91pvSdl+Ns+ARKAyjOhE9y88w88dRPPHJ/T0YP7Ex+QRFTF2xw+Pqahk+gZj7UM3xSWFJCbHg4k669hs/X/8u65AobrfOgVnQw18eXduj0KzsnsF5IKM9160GrOnWZtmsbU3dt5XBOFgDbT6WjAE90uZpO9RueHTwpx8UOdXf4BPRxpyjOPGQ6xiFT6VjzquiehBsjWZo+T1hX6oRurpNH7WiFPH7nDHq3S6wKEdpCOcuxwuM8uOVVlqavpXvdG3im9du0Cr9E77JqjCe3ZawpHnT0TrSiKJsq/Lq/kteU/2YmUrq55xogHGgPLAO6A3MqvD6y7OOFtm2Ufz2qJoVLtMNVx+/kFRfz9NJl3D57LgEGP4a3aqv5e5xBB0+L4GfRnCxKe0YoPQ5g0YnVLDi+gvnHV5SFTkqxY2fa0d+JDqzHk61f5+roXjpWWj3kkQFOU5XvpOu8HC3b+Gt5L9D+WACr3c4n69YzfPpMsovMXB7bQNP7n8HNR+/odVSAKK4Vxa/nkmqOIsVsJz60OU+1fpNLIi/XuySH8FKPStdJKEwzsePAccbf3pPXH7uRkOAAh6+VR++cTWZhIeGBATzR/Wr6JLbAaDCgUrpr26aqtIuOoX+zRAAyCgsA6NkkgQc7dMFit/HmP2v4ae+OM/c7kJXJBxv/JiIwiIHNS49uMCjn1Oemo3fc7VS9XSqiQ3fkWvgrYwt9Ygdzf7PHCTKI8fNGZQjmSzmPKQFw+tgdcP7oHZvNzodf/MHLby9gzlLHw5zl6O1UrXzq7LzxqiNJvPfXWlrXq8vTPa7llksvoX54aQqmXUwMb3TrTd2QUP5KOcqK5CMAPNm1O63q1GVF8mE+3vzPmdCJn6JgtdtZmnQQP4OB/gmJF35jefSOJojkzFRzFNGBrRnQYDh3NX2EAIM46+RuQNixnex4ojXy+B1AjMT6xRB1V9jFKD9+Z03GBsYn3spNDUexf98uj0/x+XL3E72O3rHbDM78EJSpqmqnKl5THmK0AoNUVT1a9vlORVGGAPuBaxVFubKSFl4SD8JVbf/XJR9jwPdTSyfVAqBtdD2ig0NYk3JU8/fyxeN3RHSyKAn5i7E+azv7tx7licSRjGx8N+0iOzDl6OeYbYV6l+YUHroDTRM09p10nQ+g9dE7Wt0LXNP9ZPepUwydMgNVVSEEujZohJ+i8Heahkd7yu4nbkVUv646tZ79+Ud4qtXt3N9sAn9lLOfX1BlYzj9mWjhE96h0nUSxOecFU0ExL7z4C7cM7cJ9d3anWaO6PPvRbxxOcfzvuTx6p5TVR5J4beVqnu3Zg2evu5ZWdaP562gyh09ncV2TptzZuSMA03ZtZXfmKcL8AxjXqRt+BgOfbPib5UcPV3pfi91GnaAQgMqPbXCTW/XofqJ35xMQZ67Yqlr5/PAMjhSkcG/CcJ5o9QrfHH6fk4J243SlL+U8pgQAq/MhjfLwiauP3ln55z4AAusG8tHTw/np982s2nDQoWvLwyd6Hb2jtU8ddelPO3ay59Qp3hvQjxd6Xofp6qvILCgkvnYtjAYDabl5vPD7CgAGtWjFkMQ2nDYX8tY/f3La/N+8XPmxdKH+AVhstrO+Vyny6B1NEMmZnx36kdTCdG6PH0zdwFi+PvI+2SXijt8uhreM7WTHE1ehU/cTEGOHdTki7QCrDJF2XjvK/vwkHtnyNq/s+RYVlWC/ENpFdtC7rBojWDrerXhQ95MLkVP2cWsFgQGgqmohsLTs0y5lH8vTkZFUTvnXcy7wfYmOuKr7SaHFgqmkBKPJwNgOXZhy43De69mPqMAgzd8L0MXTertZNCd7goOzS3J5dtfXfH14NkF+IZTY9Q/3VgdfdqyG5JR9lK7zAXyp+0mJzYbFbsdoMvBgxyv4cdAIXr3mekKMjk+0OoSbu5/ogSieFdWvx82nmLDtQ+al/UG36J7EhTTRuySH8SGP5pR9lK7zMJzdoQ0w65cNjH96FqHBAQztfanT12uxS9sbup8s3n+AYdNmcCQrm7Fdr2DWqJH8+cC9vNOvLy1rR/P30WTe+GcNALe1vZR2dWPYeCL1zPE65ZR3Ngky+uNv8COnuAh/QxXT9m7qfuJORHCpaA5dkv4XL+z6hBC/UK4SvBOnB/kyp+yj9J2HoWTno2Q7v9Dtju4nAFEWP/wMCm+MG8TDt3bHz+C4V2riVZF86oxLd6af5IbvJvP+2nXsPnkKi93GyXwT32zYxIRFi0nLy6OePZQBCaUngHy86b9OJ+WUv1vXBo3w9/Mjt9jBeTs3dD9xF3p6UxRnzj++gtf3fEGdwLo80fIVgv1C9C5JBHLKPrrddbLjiSvRsfsJ6LfbqzL0Tq1Xhag7wy6ERbWSUZwFRHFfQk/61x/G5qx/mJs6FZM1T+/yqo2vdz9xd+cTDdlf9jHnAt8vP8i9/Keg/UAnIBHYXPGFiqIYgaaUJjGPaFqlRFNc1f0EYNKCpSR1zeH/ruhM90bxvLx2JQsP76/6Qmfxwe4nIJ6TPcHBv6evYUn6nzQMthHsF8INsTex5MSvFNuL9C7NYXzZsRohXedjaN39RKvOJ+Ca7icAY+cuYMLV3bir0+V0bxTPxFVL2HAiVbs3cKN39fStKJ4V0a9W1cqUo7+y5MSf+BtKd/o3DG5MmlnDLjsuwkc8Kl3nwQTkWpzanQ2wY3cq9z48mRyjDYDY6Aiycwsptjj2nK7pLm3QrvuJlh3GnPVrusnEnXN+plezBK5pGk94YCC1g4P5dfceNqWloeQrxMaE0rVBI2x2O99u30Sh9ewFzfLOJvdcWnocWVJONha7A4uQJqNbOp+Ae52qt0tFc+ievEOM2/Y24cbSn8vCjRHkCzr/6yG+lL7zcJTsfNRazi14++WZnep8As67NTunkHFPzOSh+67jths706ppDM9/sojsPMc659ako5gW41etfOrsePXbjZuYvWMnZouF8MBAssz/Pf8jA4NoXbsuOzNOsvpY0lnXGRQFu6rSMCyCey8tbfyw/Oghxwt1sUPd6U89u12L4swtOXuYuP1/dI9u5HHdql2Ebq6THU/cgcmoWwcU2f3EcUTdGVYVPyStYUbyb1wa1Znn2rzLVdE9UdC+G4E78aCEvKZ4cOeTFZSeE9dGUZTKvNKu7GP5T4cryz72reS13YEQYJ2qqmIk5yQXxFXdT0psNj76ex2Dp/3IcVM+n94wkEEtWmn+PmeQ3U90xxMcrKKSao6ibuAV9KzXn2fbvMOlUVV18BUPX3WsBkjXeSCGmm3GBrTb+aXVzu6KaO3gYquVN1f/ya0zZ6OiMuumkVwe20DT9wBk9xM3IqpbTxafJtUcRXxIcya1ep27mj5CqJ+G6SwX4uUela7zcKqzOzs7pxA1s5hgk8oHTw7l21dvpUmD2k7dQ4vuJzVFS89Wd5y74vARXl2xiscX/c5dc39hwd59HM8rW4gxQcta0ezISGfv6bMX5MvfqVeTBHrHNyeryMz03dscf2M3zTv7WvcT0caoWSW5JBeGE2QIZkLLl7k/YYLQ7hTcl9J3XoConU8sVhsffvEHr/9vIW2b1+fZB/o4db3eTtXap46SV1yMxW4/K3QC0CgykrjISA6ezOT0qbPDBOWhzQc6dCYmNIxFh/Y7v3HCDQ71he4novgyzXySmSmbAGge1oqRje7GX9G4m6vnoJvrZPDEncjjdwD9Bw5VIdrAoiqsqo05qUsYv+1NjpuPcUvjexjYYKTeZdWYZHO06IMUl3CkpJ7HBVBUVU0GfgMaA49V/J6iKDcAfShNVi4p+/JcIBO4RVGUThVeGwS8VvbpF66tWqIlrgifAOzPyGTE1Jk8s2QZvx8+AEBi7WgC/fy0fzMdQqIiuFk0H3uCf9ed3sozO9+n0Gri3oTxPNDsCWr519G7LKfwVcfWBOk6z6WmE3eg7eSbK8InWnt4U1oaA3+YxqsrVrE5/TgA0cEat6p1o3f19K0InhV5fLvudBYzji2kfWQnnmnztsccIeutDpWu8w4Cci3VCqDYbHY+/2oldaJC+eG10dzU8xKnrpdH75RiUyvf3d04KorY8HAy8gpJzc8983WDoqACUYFB3HtpJ4wGA99u24TJUuJ8wW4Kn+gRQNET0Rx6qCCQ+WlraBXRnqdbv0VieFu9S/I4pO+8B3eGT5x167JVexg7YTofTl0FQGCAEcVBrQTm233y6J2LsT8jkxKbjaDCUteV33VoYhtGtGpHZmEB3+3YVP03kOGTGiPSuDPVHEWkf1uurtuL8S1fIjrAs9bbtEBP18ngyTkEGY20rlPXdW+gY/cTEGORqxxPCKB4EqnmdCbt+IwPDkxmbeYfAET61/L488y8dVKvKjwtfAI8BKQA7yuK8oeiKO8qijIXWAzYgHtVVc0FUFU1D7gP8ANWK4ryraIo7wDbgCspldxPOvweJDXAVd1PbKrK7J27UPMgwODHlAHDWDryTno0bqr5ewG6dT/R082i+VikgcqF2J+fxKNb/8cPST/TIqw1g+Nu1bukauGrjq0B0nUeSk0n7srxpe4nZouVaVu3YTQZiA0NY+Wou3mrxw1EBGjsK9n9xG2I6FY7dn5OXcrE7e+Qb8nlgWZPcHOjO/UuyyG8OMQpXeclVCd88u/mJO5+8Ad2HjzOU/fewNsTbiI0OMDh67Xwrbd0PzmX7MLShc4csxmjqXQ6XuG/3doPX96VKxo04t/jKcw/uLf6b+TF3U/0RLQx6sITq5m0/V3M9kIeav4UgxqMxIALNuh4N9J3XkJ1wyfu6H5y5GgGpw6cBuCFsf14b+JQIsMdf57VNHyiRfcTLaiJS1Pzcimx2ujYsAEBfn5Y7XaMJgMq0Da6Hs9060GAn5FPt/zLrsxTNSvUDeETd/lTzzGoKL6cf3wFr+35nNoB0Uxq/TrtIz2va7UG6OI6GTw5h9jQMBbdfDvv9OhDTKgL29XJAMoZRJmIqwzRBhaO8GfGRnbkWkk1RzGq8T081+Z/dK59td5l1QgvntS7KJ7U/URV1VTgcuBToAWlKcoelKYqr1JV9edzXj8PuBb4ExgGPAJYgAnALap6gW1CEs1Q7NoveoHrAigA9jyViYuWYLPbmTxgGF/0GUT9UBc8o3VytN5eFs3FojvYjp0Fx1fyyNbX+SV1OgC1A6KJDWqoc2XO4Yt+rS7SdZ6P7H5SPfJPlzBz2w5ubtmOZSPv5Pr4ZtW+13WNE87/oux+4jZEdevRwjTGbXuPOSm/c7LouN7lOIW3eVS6zruozg7trOwCJj39E598vYLw0ECKSqxOv6/sfnI+aXl5/JV0lOubN+OKRnEYTQb8TAYC/fy4pfUl3N3+cgotFj7YuI4TBc4vop6HF3Y/EWH+WCSHHi1M47Gt77L85DqahDZHRYMzJn0I6TvvQsnOF/bondJrbGzdeJTL2zZi6htjaJ/o+HGmegc69Tp6p5yUnFwW7tvHNfFNGN3hUuIiIwjw82NIozZ8fv0gagcFM2XnVmbt2YHVrsFzUB69owmi+HJz9m7Gb3ubU0UnuK/ZeFqEtdG7JLeil+sU6cSzCWncWP34518Y0/EybHaVb7Zv5KutGym0Oi8UpwhzfiCnFcFh4hw/GBXqvOzdSWyoBoM/N5IQ2ohHWgwnPrQ5B/P3MjvlB9KL0vQuq8Y0Cc6s+kVeyMQ2yzarqlrjaGZg0zi1/isPO/Ta5Nuf1uQ9JeIRHNtIbX7bhDOfW1yQtbSGueZnDH+DgXs6X85DV3bFjsrAudM4kpPtkvcCdHG03m4Wzcee4t/n24yhfWQnlp2cz/L0BVhV/X6+qw4i+FUr14H0nQTC6jRSL+kzrtLvFYfXfA+Glu7U2sOucHC7mBjeHHADrevUZf7BvUxYsfiCRwpURt+EFnx+wyCScrN5Z/1fLE06eP6L3OxcvXwrgmdFdmtccA4da3WlWVgr5qfNpMQuzpzFhXDWoXJsJ9GKiLCG6hWXPVjl60oinT/fXlGgOMKPiLAgbh3QiR9+WU+xxbnndE19q4UfRXFsv8QWvNH3Bg6fzmL1kSRS83Lpl5hI96bx+BkMPPbHQn47tF/bYt3kVXf7VHr0bIyKkdigTMKNkbSMaMemrL/1LgmATy+fIV0n0YzIgBi1W8wtVb5OreX8grctwvnF+ep4tUWzGF569iZioyP48qe1zFi0EWeWZ2viVJF86qxHo4KCeP/G/lzVpDFZZjOqqhIdGoqpuJjf9x/kyXVLtSnsXFzsUHe6U09viuBLo2KkZ72u7M1bhIqKgoKKNnMmWrkOvMd3suPJOdjtKm+t/pO+301hxdHDPNapG6PatHf9G+vYAUV2P3EcUXeIXYgjBSlM2PYhnx+aQYPgRjzV+g3aRXbUu6wa4227yiQSEfCk7icWu50v/91I3++n8M22TWdCJ3WDXXS0mDx+R3c8xb8fHfiVrTnr6V9/GJNavUHT0BZ6l+QU0q8SX0J2P3GeXSdPMnTyj3y4dh0FlhKnQicA97bvhKqq2O0qX/QZxM9DRtE2+pzufm4eF8vuJ2K6NdUcRbBfAt3r9uapVm/QNDRR75KqxFe7dEo8h+rs0FbV0l3a17aM546bruCH10eT2MS5rqx679Quv4cI3U9+P3CQe3/+lWB/fx688gre6tuH65ol8HfyMcb9tuhM6ERTe3vx0Tt6u1Qkh1rV0s7XPer15Y74B7kj/iGCDPr/rCGR6IG7jt6pTlexg4dPcv/Dk/lz0yFuu7ETUU4cuwNiHL2jhU+dHavmFBVx99xfeGbpcjampLL9RDoL9+7n/l/m88aq1RhNBhSgfd1Ypt04XLvu2PLoHU0QwZdW1cqyk2tJMUdSOyCaZ9u8Q6vwS/Quy2vR76wXwUnJzWX8vMVMrr+FXYWlZ4Nd2ygegDUpR133xiajbt1Pyh9yeu+yhv8m4kRIsFdGekG4EEk9R1BRWX7yb/49vZ3hjfpwKL/0rNgQvzAKbS5YaXYT5ZN6IuzOlki8hfLBg9Y7wsoHE1rvvk7Ly+PT1esxYqBuTCjLb7mLpUkHefOfNWSaCzV9rzODDTc72mwK1NXLOQXBQrm4fLAiqoPzrCbe2DuTjlHbeaj5SMYlvsDkpE/ZmvOv3qU5jPSrxJcon7ir6W5sf5M27nSFh40mRVP/Wux2Pv1nfeknYdC6Tl3GderGi2tXkF5w4bHFmLaX0TG2AYsPH+C1dasY0Kwlt7Ruz2/DxzD/4F5eX7f6bHe7cVys1zhYlDGvqGPbn1IWsyv3IOMTb2Vc4vOsPLWYRcfnYlVd3I22hiSbo6VDJcJSvkDm7C7tpSt3czrLxFNPDODbV2/lq9l/M3PRJuwOBhAD8+01cq1WftTK11B9v25OO86AyVO5tmlTFKV0A+TfycnYVBUjBqxhdu67rDMdYurz7JrlZBVp5Ag3eFUPn+o9XhXNoV8dXkZGscItjfsTH9qcKUmfcbTwkN5lSSRupzx84mz3E788s9PdTwJyLU55taCwhJdfmUdM3QiyS8woCjRtWIcjqacdul4Lp4riU2dd+vOu3fy2dx8lNtt53/MzGWhYP4IOMQ34fcTtPLl6WeUdNp3FDfPB7pz71cubIs3nniquhVW1Mrb5JJalz2fxiZ81634iKUV2PKmC7SfSseXaMZoM3HdZZ6bcOJypA4bRqrYLd7Lo2P0E9N9lXREREuwXQuQdYpWRZzXxfdLPHCoIxIAf4xKf5/+aTSQ6wLndKqIhd5VVA7vy33Omql8Sn8QV3U+g5udiX4z808VM2byFgc1bsXLU3YxuexkGxQXvp8O/Db29LKKLRffvlpw9PLTlDX5OXcrevB0AHrfjzCv8Kn0ncRDZ/aS69zWQGBxN90bxLBt5F6NaV94pNCowiHsv7UR+STEfb/6H9AIT03Zt4/GVv/PF1n/p3iieJSPvJMw/4OwLZfcTtyHq2HZ33kEe3voWy0+u4/qYG2kZ3k7vkhzCrQ6VrpNUg+p0P9m0LZm7x37P2i2HefjW7tw7vJtT1wfm272u+0lN/LomKYnVR5L48+hRbKp6psuJ0WRANav0bJLA0pF30ju+mTbFgux+4iJEcqgdlbmpS3h25wcowLiWL3BplJBd+J1Duk5STarb/cRZquPVkxl5BOTauLlPB6a8MYZb+jnepb6mTtXKp1rgrEfLQyeVXbX84CFumjKd5Lxcvup7E29c25tgo/NHIlWKG7qfuAtf736SXpTBhG3vs/LUevrWH8LDLZ4m3Bipd1mleInvPCJ4oihKX0VR9iuKckhRlKcq+X53RVG2KIpiVRRl+Dnfa6woyjJFUfYqirJHUZT46tZx/+x5vLZyFe3rxbJ4xB283aMP9UJCq3u7qhEggCIKeg8iLoZIAwxHSTVHsvjEehLCWvJMm7fpFzsUf0UjCeuAbGss8QZEcV05WrcjLsdVi19FVisfrF3HgB+msv1UOq91v55fh95GoJ+f5u8F6Hb8jp6I5mLR/VtkL2bGsYUcKgjET/FjfMsXuTP+YcKNEXqX5jDSrxKtEc11FdFiQQy0DZ+4IoCiNYv27WfA5KnsyjzJmz1uYMagEUQFBp31mrvad6RRRCTpBSaMikLdkFBK7DZ2ZKTzyeb1jPtjEU+uWoLJUlL58QJuHBfrFfYUxbEierXIVsyXh2cyYdub7M7bCkDT0BYYcNHPeBohHerbuNN3it3utkWyvPwiXnp5Hm+8t4g5S0v/PQYGOPd81iJ8ItKCGWjj14r7bH/YvIUhU2dwqsDEN/2G8F7PfkQEaOgmN4VP9Aig6IlIDt2fn8SjW9/mr4w/OFjW+Voi0Rp3j+0i61SvxYbIR+8A/LFoJ39vS+KxMdfx5rhBhIU4/uz01aN3gAv2p0jOyeGWabP46t+N3NrmUka21jC47uJxqbvd6cvhkxK7hc8O/cjHB6cRH9qcPrE36V2SVyF88ERRFD/gM6Af0AYYpShKm3Nedgy4E5hRyS2mAu+qqtoa6AKcqm4tJTYbkzdvpdc3P/DDps0MSWxDl/px1b2d4+gYQNF7l/W5iDIhVxmiL4BVxI6dhSdW8dCWV9mes4n+DYbxdJu3qRNQV+/SaoSc2JN4KiK57lxcGUBxBUnZ2dw16xceXbCQtalHKS5LwrskgOKD3U9A/wm9c/EE96aZo1h9ahvtozrxbJt3uaJ2d71LcgrpV4kW6OG60KAAAvyde/6L2P1E68UxrR18LCeX22fM5dmly7Ha7eSX/NcmOMDgR2ZhIV9t3UB0cAg/Db6FiV2upk2d0nFHkdXKX6nJrEg+UvUbye4nbkHUcW1SQSqp5igKbU14pMUzPN7qZWKDGupdVpVIh/oeeo3tqhs+qc4i2dKVuylIycegKHzw5DBeGNuP0OCAqi8sQ3Y/qZoDmZkMnzqTT9at58bmLWleq7Zm9wZk9xMXIZJDC21FfHRwAQdM/vgpfoxtNsk7up9IhEAP19VrWIuxrw7DYHD+Watk5wvb/cRkKuaFF3/h029WcnXHBCa/MZqWTR3vUC+KT7VAK49a7Hbe/fMvbv5xJtN2bQMgLlzDTWBe1v1ED0Tx5apT63li27ssOP4TAGHGCJTKt8NInED44Aml4jmkquoRVVVLgFnAWfEjVVWPqqq6AzjrKVsmO6OqqsvLXmdSVbWQGpJbVMSbq//k+m+/Z+Hh/QDc1uZSRrRq55q2/uXI43fOIMKE3IUQ5aHpCNkluby+90de2PURKYVJZJeUniVoVMRu1XQx5M4yiYcinOvOxZO6nwAs3n+AD1asw2gycEndGNaOvp+bW7ZzzY+OPhhA0XtC71xEd69VtTEndQkTtr1JelEao+Mf4KHmTxHqp9Eh825A+lWiAW53XVxsLSa/Poa2zes7Vaho3U+0vhe4JgD6046d3D3rF2zqf/vPSuw2pu3extv//kW3aV/xw44tDElswzPdelAr6HyPlF9ZLySUjjH16dUkgboVu4zK7iduQ1SvZpXk8v7+ydTyr82kVq/Tq94A4ScHpT99Dt3GdtVZIIPqdT8BCMyzsXXzUXp3a8W0t27n0pbOhcFEWCzT8j6grV8tdjsf/b2O6776ji0nTwDQN6GFdscGgOx+4iJEc2iupSGhxjDuTRjPyEZ34684HhSTSC6A212XfTKXQXd259mv7yYwqHrPQVHDJwBz5m3ikUkzUO0qAUbnu4mJ0P1EC7ScK956/ARKvkJkYBC/Dr2N7/sPpU5wiCb39rbwiS93P0kxn+BIQTBGxZ9HWjzD/zWb6FHztSLiUPBEUZRARVEGKopyj6IovRVFceffwoZASoXPU8u+5giJQI6iKL8oirJVUZR3y9KYmnA8Lx+jyYDRZOCGps1557q+LLr5dno0bqrVW5yPAMfviBJAEWVC7kKI8NB0lJ25B3h1zzSOmSMI9gvh+TbvMaD+zQQagqq+WFDkApnEwxDWdRXxtON3yrHk2UjOzeHdnn2ZM3gUbaMd3zngFPL4Hd0RPYCSaj7JxO2f8OXhWRgUP8w2zTNiLke6VVID3O66tBPZhAQH8NVLt/DIbdcSFOje4wDA97qflN63dIx8LsU2G+9v/Ju31v9Jt4aNeeGq686LCwQb/RnXqRsLh49h7pBb+bLPTay45S6e69YDf0OFe8ruJ25BVK+uz9rOI1vfYE/eNgbH3cqjLZ6TR+9IRELXsV11jgeA6nU/sdlVpsxcxyMTf8RmV/ns+RH838irMfo5vs9RhMWy8vtohdZ+PVVQgNFkoKlfLT7rPZDFN99O5/oadnxyY6DTnejtUZEcmmPJ44ntH/FL6jKurtuLia1eo2FwY73Lkng2bndd5vEsPnt8Gl17t+PtOY9QJzayGmW7N3zirFf37D/B7fd/x86DxwHoe3VrwkPdc/QOiHP0Dmgb5Cw4XcJn69ZzVcPGLBlxh3brt/LoHU0QxZVHC0OZn7aWFuFteLL1GzQNbaF3SR5LlSMBRVGige3APOBrYAmQpSjKN4qixFZ43ThFUfYpinJCUZQNiqK8rihKgqsKdxAjcA3wBNAZSKC0vddZKIpyv6IomxRF2WQrKKjWG93z0688umAhwUYjkwcM46ebRtK6jguPLREggCIKoi16VUSkQYajnCquzeGC/fStP5gX2r5HtzrXYfCI5kiVIyf3JD6AQ66Dc3xXWD3fgecdv3PodBajpv/Ek78vJT6qFr8NH8MLV13nkvfyxe4noP+k3rmI7F4VlaXpf/Hkjs+wYyfYL4QHmj3hURN/cvFMogPVcl1OdhZ3PvAdC5fs4NYBnZj8+hinFsNAdj+pLgpUGj4BWHBoH8m5OfSOb079sP+e1w3DIni+Ww8e7XQlBoOB19at4tV1q1lwaB9j2l7GJ71vpG7FHWqy+4nbENGruRYTL+2ezIcHJrMvfyd2bHqX5BDSn5IqcHoes8RaeZDZnd1P9uw/wb0PTeb35bvodUUi/kbng2B6L5aV30PU7icAKbm5jPlpLn4GhZ9uuoUXr+qpXfcTNx694yu7uMsRxaFW1ca05Pm8uOtjQowh3Nbkfr1Lkvgu1RrblajFLPhyOa+M+hij0Q9zQXFllzhEdY7eqUmw0xlsNjsBuTYaBYTyzP19mPrm7bR3oqOYFoHOmiJi+GT61u0MmTaD0+ZCJg8Yxmvdr8do0GjNS3Y/qTGirKMuO7mWp3a8h1W18ljic/Ss11/vkjwSR/5lPUdpClEBDgCZQCBwN7BeUZQERVEmAu+VvS4G6AQ8BexTFOW1GtaYBjSq8Hlc2dccIRXYVtbyy0ppeKbjuS9SVfVrVVU7qarayS809NxvO8zi/Qfo++0UXly+gqaRtQj0c8OOGx0DKCIsdFVEhMHEhRDlwekIuRYTb+ydwaTt75BRnM6oJvfyZOs3CPbTqA2ZDsgFMokH4HLXwTm+C6m+78rxpO4nKvDzrt1c//X3/LBpM6cqBE39XHFMng8GUETzsCe4N9UcBSTSOCSBia1e5cYGN2NUNGyd7WKkWyVO4nbX+QeEUmgu4f3PlvHokzOZvWQLVlvpJFxggOx+UhEt/dswIoLEutFl9zXgXyGAYlAU8oqLSc3PRUUlLOA/b91/WWduadOeDcdTGf/HIr7fsYWpu7by7r9rmbFnB9c1TqBT/bjz39DNARQ9EMGxonp1TcZGvktaDUDzsFY83PxpagWI7SfpT6/HrfOYAcYLz9W4a5EMwGwu4d2Pl3D/I1MwF1sI9DcyuGd7p44k1yJ8ItKCGWg/vt2QmsqA76cybcs27mrfkV+H3qrtse9e6lMRHCoKO3L38+iWt5ic9BkAAYZAwozi1CfxGNw+tgtQSp8b/yzcwkNdn6Uwvwj/AD9ueaQ3AV549M7JjDwenvgjVquNz58fwf03X4Wfkx3Fqou3Hr1zIDOToVNm8N3GTfw/e2cdHsXVxtEzm40Rxd3d3d3dobhLjVJKaUtLjQqlhbZfvUWKu7sFdw2uQWLEfePZne+PJTRAgJXZnUmY8zw8LWTnzqWFOblzf/d9C7jmIcNg/Rr/MWr1E0lQgi/vJQYyxfcHrsb5Uj9fM7SCfAUgciqmPKm6YNyvGSWKYlWgCNANYwilJLACY8hEAPYBE4HPAF+M6cWPBUH4xYo5ngUqCoJQVhAEJ2AwsNWMa70FQcgsPdIOuG7FXF5KhsHAiouXaPPPQq7eCwfgm1Yd+L5NZ4q62fAvjRpAeYwSXso9D6W+rMuOOzp/pl36jdk35uGnu/m4HYC71lPmmVmO+oJPRcHkKNdlJae139GlpfHdoSMsOHYOMPbI3vXaKFqUKC35vYw3lCeAIidKc7DS3XtH94BJF77lcPg5Ohfpw/SqsyjvVlnuaZmMGu5UMQNZXXfpaiDbN1zAKU5Pk9plWP/zONo2Mq906qtS/cRa//aqWoVlrw2kW+VKgPFlQmYAxSCK5HF0pFahIjg7aHF91L+8XelyjKhRh6jkJOacOcqxIH/AGA6NS01hzY3LODo40LtileffOJdXPwFlOFapTg1K9ibNUJTSbuX5uOpsmuRvLfeUVF5dFLe2s1eLAIDEpDSc4vR0bFaFj8Z35I/PXqNYIdPbIUjh2txe/SQ5PYOvDxxk8Mo1/O17BoMoAuCilciDavUTm6CkdWl8ho4LsckEJXvTt/gwplf5jkoe1eWelkrOQlbXiaKIITqGuvVKMurDHvxv61RKVihszhCPUXLrnZu3Qxk/aQl7fK4ypm8Tfv14gFnXy+1TJbbeSdPr+e7QESZv3AFAKU8vpjVqgZNGoiICuaz6iRwowZVJ+hS+vLaYTy7/QYaYgYvGlRKuZeSeVo7BlOBJSSADWA4gGtmNsRzWPaAR4A2cALqIorhAFMVvRVFsAAwDkoFJgiA0sWSCj1KPk4A9wA1grSiK1wRB+EoQhF4AgiA0FAQhCBgI/CMIwrVH1+oxluzaLwjCFYzhmPmWzMNc0vTGMq9anYakxHT6VKrKoaHj+Lhpa7ycXWx3YzWA8hglvJR7Hkp4eJrK6ehL/Hx7M0HJ3uR3KsjMGr8wtNQEPB295Z6aRagbZCpKRA7XCXrpN75yUgDFOLaG5Lh0nB20LO85kH8696akh2U9Yl+KWv1EdpTs3oSMRH7zW8aX135DK2hpX7i73FMyG9WtKi9DSeu6uMB4ImMTmTWlF7Pf60UBb/OqgEm1KabU6idg3Yu9a2HhJKSmMqVFMyY2akgJL2NwXQTyZ+Rhcv2meDg5cyzIn0vhoQC8UbcRAH/7nuF86EPER2PpRREBSDcYSEpPJ/XROvu5s1Pb79gFJW2eZeVElC+TfWcRmHSfYaUnMrH8+3hqveWelsorhpJ8lxV7Vj8B2Lv1ErN+3EGFUgVZNnskvdrWNOt6pXhWydVPzgUHs933Flqdhl4Vq+AzaAzNi0vYvlOtfmITlObPDUGnSdIn8XaF6fQqNhgHwQ4V3FVyPEpx3amdvszoM4e8BT34dcf7dBrU2KLfj5Jb7yQnp/H9L7v59NtN7Dh81ez7SdF6JzdWPzGIIlqdhs5FKzKpfhO2DhhOtfwFX36hKeSi6ieveuudRL0xpNmr+CCmVv6SlgU6yD2lHIEgiuKLPyAIyUC6KIrPlDkQBGEC8A/Gd0jDRFFcnc1npgOzgEWiKI6TZNY2xKVESbHEu+9JPm4xTw+mNG9Gn+rV0KWl8q7PTg4G3JP8Pk/gnmHb8V+Cq7vlffakxtvNst629qCIm/mpWrlwc3BlQMku9CjaBr2Ywf6wHewP30GaQTn/r82ltGuk3FMwmQ+q7T3/KNRnFc6lS4pFp79r0mf93/pAknuqKI88BUuKVfpNBSDdXdqxpR4vkwz3F3/PYilODg6MbVCPN5s2Rito+OH0URZePm+TewGy+FluJyvNw0p2r7PGCRcHJzy0QRRwKkSJPGW4GHtG7mmZhTVulcp1oPpOBTy8Soj1WkzO9msOGoHX+jZkzPDmpGcY+HHxfnYfu2H2PVI9pOkLLZU7beFgS/xbs0hhZrRtQ73ixTgbGIRfdDQRukQ6VapAlYIFSUpPZ+DmVdyIiqBPxar81L4bt6IiGLRlDfFp/zlLIwgYRJFOZSrwd5febPO7ybs+O0ybhB19K5dnleJXpXlVQKB70daMKNObLcGrOBKxV+4pPcPv9VeqazsVSfDKU1RsWsG81616T8s2ENK8zG9lUKigBx9N60aDGqVZtvUMf64+avYY1rpWiY6Vem1bt1hRZnfrRPm8+Vl5/RKzThxGl54m3Q3s5FR7+1RujyrFn04aR8aW7U/nIi0JSLzHovu/EZkWbvW4qutUpMTLIb/YxCX7Qzr5injx4eJJ1G1ZmZW/7GHZ3J0W30fMa/5mtz29mublQJcWVenQpDLfzttLTHySydcqwadSuVRKj7YpV5ZZXTqS18WV/507wd++Z9C/ZN/cZGzsT3t6Uy5nKsGVHlo3PqoyhOpedTkffZJVAQtINaQA0rkOco/vTHnSBANugiCUy+Zrm7P8+/PeiGemFVuYMa9cx8P4BD7ctYcei5dxIjiAW9ERABRwzYNWI83LymeQsfoJyH/aOitKOBX2PJSS3jOFRH0ySx5sYtKFr7gWf5Fuxfozo+oPOAqW9VFUAuoJbRUV6U9K57TqJ2l6PX+fPkunBYvZffsOEcmJANL2ys6KTO135K6AoiSU7N5UQxpx6TqCkr1pXagz48q9y8Ty75PXMb/cUzMZ1a0qOQG9QWTVhjOMeWsRt+6H4ai17HSn0trvKKX6yZXQMAavWsNne33I75aH/tWr83bTxlQpWJAj9x8wY88+7vhH4aLV0qhoCQBWXL/8ROgEeNw+YFj12gAcDrhv+iTU6id2Q2lOFRHZHnKIdy58w9GIfQCUc6tEHgcbpaNVVHIYlpzQBsuqn4RHJDBt+hp++duHHUeuAeDsaN6zWa1+8nJ8H4bQa/EK/jl9lkFVarJ38Gjpq5/Yqf2OPZHboUrxZ5ohnb/vrmb2jXm4ad0Rsc2hHxUVWxEdGscn3Wax8PO1HNps3UEyS1vv2KuqmFOcnjwZGhrUKMXy70fRol5227bZI0X1E2tRYvWTQ/fu0+3fpey+d4cPGrdkQu2GkowL5LrWO69q9ZOEjEQ+u7qQZQ82UzdvYz6o8jWFXYrJOiclY8qf+oNAOeA7YFDWL4iiGCEIQirgBDzM7mJRFKMEQYgHils5V/tg/TvDF3I7MpJJG7Ybf+IOP7XvRgkPT+aePsbOe7dtc9PMh5tMFVAyH35yn7aG/xYUcifasyPz4amEBN/LCEuN4uvry6jscYwGeQuRLhq/SSruWorg5ACZZ2c+mRtkOan6iTUIemOLERWVp8n85l+q9LnU42WSubCQ+pRYmE7H+zt2GX/iDuNq1adj2QrMPHaAa5HWn/Z5Bp3W7m5O1jnL5mMlOjg00UPR3v3tzg7uJyYytFR3ZlT7gW0P13IkYm+OeBGoBLeqvlMxheCQWKZ++Khwp5cDPVrXwNvTlVU7zqE3mP53zTnBYPUJMim9KbWDLXXvmstXWHP5Co1LlkAUjaHO04GBj59imgSBUm5ehCXquBYZ9sS1AsbSqh3KlKdJ8ZIExMfi8+Cu+ZO343pYLs/GJrrK7lclrmfDU6MALxwFLd/UNFY/WuW/kGvxvvJOTEJU16mQYdmLzMwNMnNPaWdukplzSlsUYeO2C8afeDnw6RtdcHHWMnvBPqJiE00aI3OjzBrXOuqs96It/CrVujY1I4M5R46y984dvuvWCXcnG2xG2WENa+/3yHKvU5Xkz9PRlzgbfYVirtEAtCvUjZNRh0jWm15RwRaorlMxBYNBZO2Pxn03Tb68vDd3CA9uhbB5wWFe1vXhaYSYBIsqnzjEJ1vkVXMrn2zfc5mrN4KZ8VFP5kzryyafS/y64hApqaY9n61Zu0rhQSldKpVHY1NSeG/zTnZWvM3hCONhh4J53IhMSrT+DZyN16NyeFMOZ8r9DldEZGPwPm4l3Of18n1t4sbc4jtTfgc/AWnAAEEQdguC0Oipr1fF2GYnJbuLBUHQAm6YFnJRBLY6Uf3sfTSsOHuRdIOBPzv3Ykv/4TSTMo3+NAqogKIUlHIyLDuUkOAzlVsJ91kRcJqgZG/KulVketXveKP8BxRxyRk5s6dRT2irqBiR+qS0LSug2AqtTkNMTDLlvfOxbcAIZrXqSD4XG3jjFa1+oiQHK9m7BgxsfXiAd3y/5a7uFgNKjqR94exLyyoV1a0qOQmnOD21Kxfn7SGtWPj1MCqVLmTW9UqrfiL1WGD5Wvl0YBBngoI49Sh0kjmCVqOhRuEiGPQiEWH/bT5mhk5ctFpG1aiLo8aBldlURDELtfqJXVCiU9PFDL68+he69HjeqDCNoaUm4KKR/7+ViopUCDEJFp3QBvtWPzFep+fGlSAa1ijFyh9G0al5FbOuV1L1EyWe2ga4FBJKr0XL2X/FGNYcWq0WbUuZfiL+pajVT2yCUvxpwEBQsjdOmpr0Kj6I6VW+o7xbZbmnpaJiFg66BNy98jDx8758uWgCXvnczB7DUrdaWvnEXK8+CIjirXeXsXL9aXq3q0XdqiXNul4Kn1qLlB6Vin13/EiL1ePs4MCqXq+xvOdAirlL9HzOZdVP5EAJrrwW78dk3znEp8ciYPscQU7kpcETURRvAK9jrAXSETgpCMJDQRA2CYIwA6gC7H/BEAMAByDsBZ9RJPYIoOy/e4+e/y7jw527KeCah5W9XmNA5eo2vaecARS5N7ueRu5FxYtQwkPUHE5GxbDo/kbKulXk46qzGVRyLO5aT7mnZTb+yQXUTTIVFWwTFslJ7XcANl+/QYf5i1hy/gKvVa3JwaHj6F7eRi9cXtEAipJQsncjUqOZcXUec24u4FiE8dvuAk6FcBScZJ6ZaaheVclJzJmzk0+/3UR+bzcWfjOMNwe3tHtLAJDWw0ppv5OVzFNjBlHkfkw0GkEgVKd7fLons93dmJr1aF6iNJfDQ9nhd8uqewJ2b78jB0rwqxJDnQ+Sgply8SfWB+6mcf5WfFx1Nm5q6x2VXIY14RN7bZQBrN9ynrFvL8b/YQwz3+7O7Km9yWvGCXEpgp5SOlYqpFzXZhiM/32cEh0YVLUWi7r348d2XfFydpHsHvYKn7wKbQQyUZI77yUG8vHln9CLGUyu9CndivZHY9IZZhUV+UlPy2Bm/7n8/t4S6jSvxO97PqRmkwoWjaXk1jvpGXr+WXSYkW8s5ORFY5WOmhWL4aAxzSdStN6x1oNKDXHq40QWnblA7UJF2f3aaOn2bG28HrWnN1/l1jsAQcneuDrUlnsaisSk7xZEUVwCtACOYjx8VAToDXwF7ABCBUEIEARhoyAIMwRB6CIIQgFBEDoAf2J8r+Rjk9+BHbB1AMUgimy8dp2O8xcx0+cAux613KlbuCilPb1tdl81gGJE7kXFi1DKQ9QUMsQMtj7czxvnZ7Ij5BBNC7Tmg8pfo8FB7qlZhBpAUVExIvVmlS2rn9jC1QmpqXx78DA9Fi/lUlgIoYnGBaeDYKPvC2TwstzhEyU5WOnePRHli1+iMw+T8zKh/FQ+qfY9VTxqyj0tk1C9qiIHgt6yorhHT9xh1BsL2e1zlZG9GlG9YlGzx5Cy+okSN8dAGvemZGSw57YfhT3cmdCoAe5OTmh1GpyTtLQpVZYPGrcEYO6ZYwTr4qWYthG1+oldUJpTM8QMVgRsY/rlHzkfc4JEvQ2+KVVRkZmcUv0k6GEMk99fwZ8LD1K1bGGw4MSoWv3k5RhEkSHL1/DbiVP0qlCFfYNG07FMecnGV6ufSI+S1qR3dP5MvvgDh8LP0LVoP0aXfUfuKamomMW2eft5t+1MknWpzPh7DC55LDu8Y0+vWuLUwOBonOL0FCngyR+fvcZfnw+meCEvk6+XO8wptUelYs3lK/RYvJQbUeHMbdeV+V364OEk0QEwtfqJ1SjBlbd1D+SegiIxOaYqiuJpURTbAKWB0cAfwGkgGePqoATQh//CKGHAHsAbY/DkhCAIhSWbuQzYOoCSptezzPciqTF6tDoNM1u0x2fwGL5u2YHCbjY8iaMGUADlvJzLDiUtOl5GQkYi/97fwGTfb1kXtAQDegQEKrhXlXtqFqFuklmGIAjDBUEQH/0Y/9TX2mT5WnY/Zss1b5Xn86oHUPyiohm7ZhOX/EIB+KJFO/7u3ItSnqYv5EzmFa1+oiQHK927BkT+9NuAXszg7YrTGVnmzRxTZSw3eVV1Xc7A0tPYOl0qc37dzcg3FnLheiAArRpUwNPNvJPCSmu/o8TqJ3tu3+FySCgTGzXkozatmNioIT9178pfnXqRrtcz98wxjgX5SzTbLNi5+olcARS5UaJT7+ge8OfdPQQle1PEpTjTq8yirFtFuaelWFTf5UxyQvUTg0FkzcazDBs3j5j4JDSCwIQBzcjrmcfkMdTqJy8nTa/nl+Mn6L98JZHJSfzduTdlvLwlGx9Qq5/YAKW4M0Wfym9+y/jx1r8cjzSeK86NrQVU1+Ve7l0OYFKzT/m0zxxSktLQaAQKFPU2exx7etXSNWz03Rhm/7STMsXzseS7kfRsU8Pka6WofmItSgxxBsXFM3zFOmYdPISnszNJ6Za1GswWtfqJ1Shxralk7OU6s/9Ui6IYCCx99ANBEDRAVaAeUP/RP+sAWZMSGmDeo8+HAr6ZP0RR3GjuHOQm86GV4W7ZCTpTeX39FiY1bczgWjV5rUoNll+7yN++Z4hITrLNDTMfcu4Zthn/BWQ+AF3drejZLRGZD0hvN8tOetiSzIdoETfLvtGxJw+Tw3mYHA5407NoZcaWm8z1uEtsCl5BaEqw3NMzi8xNstKukTLPJGcgCEJJ4HdAx5MueJrDwKFsfv2YDaalIhGOOkiXMAsp9XiZaHWCzTyt1WkIiUqgf+Xq+JQuz9Krvvx2/hRxqSnS3kgGL8vt49hEV0X5NzTRQ7HOvRZ/h3cufM+Akp3pV7wT1T3r8sudr3mYHCj31F5KbvCq6rqch1NcOmlejmZf5x8YhROQ1zsPX03qTlJKGn+uOsqOI1cRzdCMc4KBVA/rypNnvoiTwptSjgXWrZED4+IYtXY909u2pnvlSmg1Dmg1AtfDI5h35iwH7t6DR3uQBfO44enkzN3YaGkmDnb1bbLO2e6OVcr6VqlOjU8viItDHqZU+pzD4XvY9nAt6WKa3NNSDKrvcjZCTAJiXstexjvEJ6M3o/1NJpb4Ni1dj1McVK5QhOE9GzKgUx3+t+wQu45eN3kMaz0rlRel9KvU75+vh0fQf8lKGpUswYO4WADqFS7KhbAQSca3l0/t7VI516hKeg98LPI8ACVcoWvRfhRyLsrawEUk6RNlnpn1qK7L/aQkpnL7grEVTa+pvRjzcU8Wzd7O9iVHMRhMf8Zmhk8scaslXrXEqT6Hb3D5ehDTP+jOJxM707xeeT7+eYvJa1drfCqFA6X2qBQOFYF/z11g0bkL6N1F8rq48kHjFsw9fYzoFAn8oNPa1J329KZczlTqWlNJ2NN1VsepRFE0ANce/VgGIAiCAFTivyBKfaAu4AkUffSjG2CQYg5yYesASkRiIl/4HGD+2XO83aQJo2rW43pUBBtuXbPJ/R6jBlAA5bygyw4lLTxMYVeoHyLrGVKqK9OrfseJyAPsDNmALiNnzD8T/+QCOXqTzB48ev4vAqKAjcC0F3z8kCiKX9pjXirSIvVmldTjZWJLT88/c47N124wpXkzxtSsx4DK1Xlv/y4OBtyT/F5yBVDkDJ+AcvyrZOemixmsCtjBkYhzDCrZjNBkY7DTSeNMmkH+76VeRk71quq6nEvmqTFLAigxsUm8OXUZU97pxIzXO9OrbU3mLtrPbf9wk8fIPEEmRQBFSgdL6V9L3ZuYns5ne3348+QpKuTPz8P4BKKTk4hJTnk0rvG/2dTWzRlQuToLL5/nt/MnSZT6xJmdwidg/zWvEsKdSnTqbd0DJvl+x8jSvelatCs1vOqyImA+d3U35Z6a7Ki+yx1Yu0kGWLRRBub79pZfKOMmLWba1K58/mZXOjWvyvcL9hEaaVqrNSk8K5UXpfSrlIcq0g0GjvsHoEVDlTIF2dhvGMeC/JlxeB/+8bGS3MMePrW3S+VeoyppQy0o2ZuIVA2dijSkvHtlVvjP42bCFbmnZTGq6149jq85Rv02VXnzq/406ViD795aTEKseQe9LQ12Who+AfOcGh6RwPsfraZfz/q45HMx68AEWO9TKRwolUelfDcsYlyXNipcnAGVa9C5bEU+O+LDznu3rR7b1u9+7elNNXyiPOztOuveeD0H0cgtURRXiqI4TRTFtqIoemMMowwG5gAHgDhb3N/e2LoFT1BcPB/v2UunhYvYfsH48mN49dp80LgFnk42LJUkU/sdkL/kf1aUUJ74eeSUUlIZop7tIQd54/xMdoccpVmBdrxZ/kO5p2UR/skFclWbABswGWgHjAFy/rEHlRfyqrffiUhMZMbeffRcspyzocHciYkCwNPJ2TaFZ9X2O7KiZOcGJ4fx0+1NBCR74uqQh8+r/Uj/EiNw0Sjnv9/zyKFeVV2Xw7G0dPHd+xFMfn8Fs37aQfHC3sybORgvD/P/nknVFiA3tt8JSdBx9IE/d6OjiU1+tpLYzwePs/naDd6o24gDQ8bRq2IVa6f6JHZuv2NvlOJWpfk0RZ/KvHtr+fTK/0AQqOFVV+4pKQXVd7kIISbBqjYBlmCJawOConn3/RX8/OdealYsxvdTe5s9hlIcK6VfbbGmvfUggs/2+lCrYBH2DBrFW3UbodVItFVgJ5/a26Vyt95Rij83Be/jw0tzSdYn83bF6QwoMRJHwUnuaVmK6rpXjIjgaD7r9T0/vrmA6g3L8cv29ylTpZjZ41jqVWta2pmDKMKGredZsfg4AA1rlOKrd7qbtX61tvWOtQ6U2qNSsf/uPXovXU5QQjx/du7Fn516UcDV9DaBL8TG7lRb77yy2NV1NgmePA9RFP1EUVwriuJHoih2EEUxvz3vb2tsHUAJiI1DL4podRoq5i3A2/WacGz4BCbVa4Kbo/mn9kzCji/eskPuTa9MlPKC7nnklIdpQkYiC+6vY4rvLDYELQPAUXCiumfOe7GXQzfKbIogCFWB2cAvoigeMeGSCoIgTBIE4RNBEMYKgqA2VrcjGoP1L+QyedUDKLcjI3lz3VZCQ4wLzl86dGfbgBE0L15K8nvJ4WW5Xaw0ByvduUHJnhyPukyrgp34tNoc6uVtIveUTCKnOFV1Xe7Ckg0xUYQ9+68xYsJ8vvxuK3EJxpeGDWuUQjBTMVJ4WMkBFGu9m92ZtOikZD7es5f+y1cSmqjj1w49eLteY8sn+TzsuGH2qgZQlPhS0NjGbjbz7/kAUM6tEpU8qss8K3lQfZd7sSZ8YulGmSWbZZt3XGT0mwv5dt4eAJydtJQuls/kMaQKeUqBktyaFRFYdekyXRYu5oD/fT5s0oo1vQdJMvZjcqFL5XaoUtx5LzGQKRfnsO3hAZoXaE8hl6JyT8lsVNe92uxdeoRpnb9F46DBw9vy0IA9Q52WHqBwitNT2tOLNg0rsuL7UbSoV87ka631qVRBTimQ0qF3IqN4bekq5hw+Svsy5fi4aWtJxgVsvha1pzfl8qVSXKkE5HBdjm1zo2Rs3YIH4OvdB1l7/gpTWjdjWuMWjKlVj8+P7mfH3Vu2uaGM7XdAOS145C6t+CKUWLb4eQQlhxKUDODNsFKNGVByJLfir7IxeDkPkwPlnp5ZZG6U5cRWAVIiCIIWY7u1AOATEy8b9uhH1nE2ABNEUYyRdoYqz8PaXthZkbpcf05swaPVadh65Sbvt2rOil6vcTjgPrNPHeFGVIS0N5Kp/Q7I52IltAjIRMnOTdanMP/eWg6Gn+KdCgMZU/YdmuZvw/x7Pyu+/Y7Swyeq63InlrYD0CWmcuLMXZyAKo1K8esnA7l8K5i5i/dzx9/0Z77afscyLoWEMmDJSgbWrMG+UD8ASnh4EZ+aQnyahM86tf2OzVGaU1MNaYCxlcAb5XtT3asOxyL2syV4FSkGZXwfYmtU3+V+rG2/Y26bADD61lzXhkckQEQCTsCo8c0Z1qMBSzafZunWM2ToTdsEs3a9K9WaVOq1rZTtd8ITE5m8cTtbK5THw/u/jSh3Ryd06WnW38BOa1d7t4uV06FKcWeaIZ1/729ga/ABXBz8AajuWZcb8ZcwIM1BJ1uhuk4F4Na5e4yrOQ29u/HvVM0mFbh6+i6imb1pLPWqNS3tzHXq1l0XuXYzmI8/6M6caX3Zfvgqvyw7hC7JtOemNT6VwoFSelQqh+pFkX/OnGX/3btEORj/X5bw8CTdYCAsUYK0jI3Xovbyplx7qkpxpZzI5Tq7Vjx51bB1BZRbEZG8uX4r/Zat5FpEGNHJxl507o5OOEpVGvFp1AoogPzp9hehxJNjL2J14Fnm3V1LiTyl+ajKLAaXGoeH1lPuaZmN0jfLrKCAIAjnsvyY+JzPfQ7UBUaLoviy7yIigOlATcADKAh0BXyB/sA2QRBUP9kRKU6DZWKLaiW2qH4C0pZZzMq2GzfptGAxsw4eonahIuwYOJJu5SrZ5F6vWgUUpflXyc710wXw3sWfmHd3LQkZcYoPnSgAU3ynui4XY+npMYBbZwP47uedlCiSl0XfDmfqqHa45zHvOZnbq5+A9N4VgbVXrhITkYJWp2Fu2y4cGDqO16rUkLblndp+xy4o0adfXV/MpqB9NCvQlo+rzaaKR025p2Qt6tpO5QlyQvWTTDavO8ehs35MGNicxd8Op1r5IiZfq6QWd0puv+Pjd5dN566j1WnoXbEqB4dK3NLODj5Vq5/IQ2RaDEHJ3pR0LcMbFaYxpdLnFHAuLNd0VNepmEV6WgaG6BjKlfLkh3Xv8PnCceRxt+w5ovTqJ3fvR/Dm5GUsW3OSLi2q0baReYUMsvpU66DBzcW8Flu5tfqJX1Q0MeHJaHUavmnVkX2DRvNalRqSjK1WP7EepbjSBij2PaYqRDtgy/AJwOXQUMau2cTZ28EAvN+oOQeHjGNg5Ro4mFvv2VTUAAog/wLjRSh5MywretHArtDDvH5uJtsfHqRJ/lYMK/28NYGyyTHtdwz/fXP1sh9ApCiKDbL8mPf0cIIgNMaYmPxRFMWTL7u9KIrXRFH8XhTFq6Io6kRRjBRFcTfQBrgPNAd6Svp7VjEJJQdQclr7nTS9nn/PXaDdvH/569RpjgY9AKBi3vx4OtnAXzIFUORCaf5Vqm8NiOwKPcysG6sAKOBcmOlVZlHJvZrMM7MTEvpOdd2rg6Xtd3b7XGXExPls2XGRfh1rM+/LIRa13sntARRbHs6Y5XOIB7Ex/NC2C5v6DaNmQYk3PNT2OzZHaWvYNEM6S/03M/3yj6TqU3m74nQqK631jrq2e+XJ4+FCzSblLb5eiEmw60YZWOba2Lgkvv12Kx/P3ICHmwvzZg6hb/taZo2Rm9vvSM294GiCE+L5tUMPlvUYQGlPb+kGt5NL7Ync4ROluPNkdCw/3vqXwi7FmF5llnQDq65TsQN+F/35Y+pSGratxs9bplK8bEGLxrHUq9aEOs0hPUPPgqVHGTdpEdsPXwWgTpXieLiZ/tzMr3fk/YGtmTW+K1MHtqJuheImXyvF+lLqEKeUfL3nINcjI/ihbReW9RhACQ+JDljbIbhpD+RacyrFky8ll7zHVIMnTyHYqAqcrauf/HcfDYdu3icqJZk57bqwb/AYelWoIu2Jr6yoARRAGS/pnoeSFiAvIlGfzKIHG5l84Vs2Ba8EwNPRmzrejWSemfnkmACKBDwq17UUuA18Zs1YoijGAysf/bSVlVNTsQI1gCId8amp/HzsBMnRGWh1Gn7t0J0jw8YzrlZ9nDQOkt/vVap+Asryr9J9G5TsTWJGEZw0zrxTaQYjSr+ZIyuMyYHqupyLYLDMZZaeyNbpUvnlbx9ef3cpf64+iiiCRhAoUzyfWeNIGUCRCltUP5Hau9fDIxi8fA3Tdu6iuIcnW/oPp31p0/uXm4wdq5/IFUCRG6X59I7uAe/6/sDfd1dzO+E6QK50qOq7nEm+Qp58t3oSI97visbB8te8OaX6yYkzdxn9xkK27rzIhRtBxjmY+ftWq5+8nBvhEby2bDVf+uyndqGi7B00mhHV60g2vlr9RHqU4s5jkeeZ7DuLA+E75Z5KtqiuU3kRW//x4eOeP+BVwJ3/bZtKgzZVLR5L6dVPHgRE4RirxyNFYNaUXqz4YTTN65q2dipSwBOHdJEKRfLTsX4lfnunD3+824+ShbxNvn9urX7iHxvLiJXr+GyvD3UKF2XPoNE0KlpCkrHV6ifWofT3tlIit+vU4Ek22GpTC+wTQDnuH0D/JSt5Y9MW0vR6fu3Yg4+a2Ph7HzWAAsi/yHgROeXB+jAlnPMxSQQle9OqYEfGlXuXdyt+RknXMnJPzWxekfCJO1AJqAqkCIIgZv4Avnj0mfmPfu1/JowX8eifbtJPVcVc1ACK9HywfTeXwkP5rHlb9g8ZS6+KNgiHvmLtd0AZm2SZKNm3d3T+TPKdzdrAndTL24RPq82laf42ck8rJ6C6Lgdj6YYYWHYiG+DOvXDOHLyNU5yeHm1qsPz7UUwZ2dai9jtK2RyTeqxMbOHczddu0HH+In47cZJjQQGAsde2RspqoGr7HZujNJ+mixnsCT1KYLIXHlovZlSbw/DSr+PqkEfuqUmJ6rscSLBfKPtXHmPolC78sO4dChXPa/FYOaX6SWJSGv/7y4eQG8Y/YjPf7sYXb3Ulr6fpz63cXP1EKrcaRJHlvpfosnAx+x74EZ6UKMm4T6BWP5EUpbgzKi2Weff2yz2N56G6TuWFXDpyg3eaf054cAyFS+W3aiw5Qp3mkpqawYefrSMuIZm5H/Tl8ze74Onm8sJr/AIi+HHxAfq9u4Avft3B4j3nKFM4L39P6U+TqqVMvndurX4iAqsuXabbv0vZ7neLqxFhANKtR9XqJ1ahBE/aAVldJ19SIAeQ+cBKd5d+7MyHWIa7KP3gj/Dxu8t+v7t0q1KZSwkhAJT3zkcxdw+OBvnb5qaZDz33DNuM/xKSdc64uqfKcu+sZD4wvd0sW3jbkswHaxE3y77xsSfz7u7nni6Z4aW7Ma3K15yJPsq24LXEZ8TKPTWTyQyflHaNlHkmNiMVWPicr9XD2EPuGHALeGlJL6DJo3/es35qKlLhnGAg1UOarKqjTlqv2srVWp1gE0ffjIhk3JpNNC9dig/btuTXDj0wGES2370lyfhOGgfSDHrjT2RwcubiRA4XK829oYkeinRtmiGdVQE7OBJxjskVB1DM1fSXAq8wqutyAQ7xyejN2JDKJPPlXZqXo0X3Pe5zk23FCjKwa106Nq3M7yuPsOvodbPGkMLDUvpSavfaYm2sS0vjtxOnAHD2cmBlr9eIT03l86M+XAgLkew+9nKtXH6NTXSV3atKXL/e0TmyM+Qo/Up0oopHTVYHLuRqnK/c05IC1Xc5ENFg4Mc3FnDhwDUm/zqGP/Z8yKSucwgLjLZ4TCEmATGv+S/lMzfJzPWtNa51jtcTcDeS4YOa0LR2WX5beZgdh6+Zfr2VjpXKibZwq1ReDU9MZMqmR9Ur3OGteo0p65WXWScPE5MigSPs4FJ7e1TutalS16IKQXWdyksJC4hkcvPP0LsbXVilbmnu33hIaor5wY7M8ImlXrWHU2/7hfH6O0sYMagpw19rQsMapRk+fSlxCc9/hgmCseXspVvBXPULITgyjq/HdKFfy1qcuhFg1pyleFcs1ftmKdemIQkJzNi+DwBnLy2b+g1l652bzL90Fr1o5fg2dqc9vSnHmlOJa0yJkdV1avDEBHJyAEUEdtw0bmZp0TCudX2GVqvN6YeB/HjmOGdCgmxyXzkDKHJuej2NEl7UPY+csAgxYGBv2DGORZ5jQIku9CjWBoNoYFXAArmnZjb+yQVyZfhEFMVkYHx2XxME4UuMElsiiuKCLL/eQBTFc9l8fjgwCEgD1tpkwioWk3kaTIoAii28KnWgBWzr6OP+AfRZvIJOlSqyO+Q2AG1LlSNEF8/NaMueFS5aLbsGjuJYkD//XDxDUEK88QsyBVDk8rDcL/myouSFTHByGB9d/gOtoKWIC5Rzq0Qt7wbsDNlAmkH+76GUhOq63IOlG2Jg+aZYgi6Fn//cx469l3l3Ukc+f7Mr9aqV5Nt/9pg1jlQeljqAIqV7beVdfZzI3EPH+Lhdazb2G8b6m1eZfeoIkclJ0t1Ep7WLZ+VY6yrFq0pav2aIGawM2M6pqEtMrTyU18tP40zUMVb4z8OAXu7pWYzqu5zNwbUnuXHGj47jOjwOnQiCgGjhxoa9N8rAMteKIixeeZyDR28wdUoXPn29C11aVOPbf3YTGmnaM0MKx0rlRCk9bQuvanUanNId6FOxKu1Ll+O7k0dYf+sqktwhlwZQ5AyfgDLXonKiuk7FVNLTMiA6Bo+8bnyz/E1C/CP5esJCwoNjLBpPjlCnOT7NyDCwaMVxjp68Q+NWFV8YOgGjfzPR6w0c2H+dHvWr0K5uBZpVL8OJaw/Mmq8U/pPaoVL60z3ViYD4WKY3bUW38pX44OBubln4/vcJbLwOtdf7XbnWnEpaY0qJ3K5TW+2Yga3a74B9WvAAfLP7EF/67KesV17W9hnMmt6DaFq8pO1uKGMLHrnL/meihDLFz0MpJRhfRpI+haX+m5ns+y3bH64DoLhrKep4N5J5Zubhn1zgVWm/8zLWC4LgJwjCakEQ5gqC8LsgCKeBZYAeeF0UxQfyTjH3U7pYPhrWKG32dWr7HekQgT237yAkCDjqNMxo1pqdr43i5/bdKOXpZfZ4/SpVo7SXN8Oq1+bIsAn807k3VfJleebY2clye1hJ7lWybzPEDIKSvSnoUpf2hbvzabU51PVuLPe0cgOq6xSMHO13bvuFMem95Xz/y67HFU/yuDji7WHes0oqD79q7Xd23rpN5wWL+fvUGXpVrMqhoeOpkNe60tnPoLbfsTlK8+m9xECm+M5lTcBOQMzRoRMrUH2nIEIfRLDss1UYomMoWjo/8w5+TLMutawa095tAsAy1/oHRjNl2krm/raHIgU8SU0zfxNGae3tpEJqr/5+8hS9li7nbmw0c9p1YV2fIVSU0qm5rP2O3P5UkjdzMKrrXmESYhKZPfpPipYuwF/7ptNrTCs0Gsueq/ZuaecUl262U/3uhbNi8XGc4kz7vjaze4ynmwvpGcZrNDr9E19rW6c8FYubticihf+kbL0jlUPjUlKYtGE772zZRlF3D7YPGMH7jZrjqJFgi97G61B7vt+Vq/WO6kpAQtepwRMzsdWmVia2DqCk6fUs971Eu3n/8vX+g5Ty9KZViTKPv26zO6sBFNkXGi8ipzxcQ1MiuJEgEJTsTcuCHRlX7l2mVPqc0nnKyT01s1ADKPwF3AeaA29jTF8WABYDDURRXCzbzF4htA4afv1kAL99MoBq5YuYfb0aQJEWEXht2WrmnT5Ll7IV2T94LF+37EDBPKa3CR5evQ6pGRl8dGgP/zt7gmoFCrHztVGs6DmQxkVL/PfBVyiAojT3Ktm3G4L2MP3yXHQZ8YwtN5lJFT6miEtxuaeVk1FdlwOwZkPMkk0xUYSde69w9eQDnOL0jOrThLU/jWVw13poHcxbmksVPlFqAMUWzk1KT2fu0WN0W7SE5dcucjcmCoCyXnklvY+9PCuXX5XgVSW5NEPUszpwB9/dXA1AEZfijC83hbyOEgeblIvqO4XinJFKepqez+aP46slr1O0tOV/Jq3dKLPXZpkowrbdlxg5fj6JgToEAb5+pwfN65r+zkiKda5UTpTSrVJ79U5kFEOXr2X6rj2U9vLGRSux9+zgUnt7VO7wiZLcmQNRXfeKc2bPJd5q+inXz9/nza/689OW93B0tvwZVdTDyaLrrHGqJZgSPsmsfFK3aglKFc1LWFQ86RkZOCcYEEUonNed7yf2YNWnw/l8REc88rz8uSuF/5Tq0F2379B14RK23rhJyxJlrG+5kxU7eNMeyPUuV/WkdK4TLC25mFtxLVJSrDBsqlnX2KIFTya2asGTiZODA44aDXHOqbQpVZbpTVrxx4VT7Lh7G4Mt/2zI0IInEyW04AH5SxW/iJxQXkqDQLvCTRlRujuejt6cjT7OtuA1xKRHyT01szCn/c4H1faeF0WxgbX3dClWUiwzwbTn3K2vpkpyTxXl4eldUvxm9iKGD2lGPq887Dl+gy//2GnxeFK04MlEaq/aytO2cnRBNzcmNW3Ma7VqMmbnRo4F+b/0mt4Vq/Jz+25s87vJuz47cNFqqZKvAM1LlKZHhSpUzleA0Ts2cCTwwZMXyuBjOT2sJPcq1bUaBDoVacGI0j3ZHbqRg+G77Xr/3+uvlMw7qu9UvPIUFZtWGGfSZy1pCZCJue13MildMj9vvdmOJrXL4v8wmt9XHubYBfPbw0vlYCl9KbV7beVczwLOHBo6ntvRkXx94iCXwkOlvYEdPSuHX5XgVaX5tFn+ukyuOByAbQ/XciRiL2I2TSik8p3qOhUvh/xiE5fu2X5N46Ch95sdGflZf7RaB1b9tpfVv+61+p6WtAoAy11rqWcL5Hdn7qxBlC2RnyPn/Ph56UFCI+PNGsNax0rlQyV71cnBgTS9ngx3A9ObtOJebDTrbkrUfgfs4lJ7O1ROf9rTm5tb/Km6TkUyXuQ7e9J6QGPKN6zE4u+3A+Cg1aDPMD2w6OzqxKLjnxMVGsufn63n+j3L9jLs7dQ0L4dsf71i6YI0qVWW0X0b46h1YMPei/y99tjjymPvj25Hvw51OH87kPLFCuDu6sT8nadZuPOMSfdVmkel9KezVkuiSxp5XVyZXL8p/zt3grjUFGkGt7E77eVNuXxpjiulch3kHt+pFU8kwJZVUOxRASUxPR2tToOYKKIRBH7r2JN9g8fQv3J1tFKUesoOtQKK4k5hZyUnJOENiPiEneD181+xLnAXtb0b0iR/a7mnZTZq9RMVuRBFkQ3bLjB03Dz+XX6MwND/epTm9cxj9nhSV0CRkpxWASUiMZEvfA7Qdt5CTt0MBOCd+k14vU7D554kG1urPqIosufeHQBSMjK4GB7KX75neM9nB+8f2PVs6ARk8bFaAcWIUl1rQGR36FHeOD+TlQHGFwG1vRvSIF9zmWemomJbrG0JYFlbgCg++mQdH36xHoA50/oycaD5f9eU1n5H6rHAds7VRaXx3cHDlPbyZkv/4fyvfTeKuUv4bLZz+x17+1UJXlWaT09E+fKO77fc1d1iQMmRTK38JcVcbdjeWEXlBRj0Bjb9vodxtT/kxJ7LeOeXZtclJ1Q/AYiM0jH+7UX8vegQDWuUZtWc0Yzq3cisKmO5sfoJSNt+J01vPBHvmqSlbuGi/NC2Cxv7DaVmwcLS3ECtfiIpSvOmikpO4/D60/z70TIM0TFUrFWShUc+pWnnmiZfP+CNdnjld6NEhcLM3fgu07/ui7sFIRK5qp94urlQq1IxhnSrzw/v9+HPzwbxxqAWGAwif685xortZx+HTqqULUyfdrU4c+UBs1cf5KP5O/C5cIcJ3Rqz5/sJdKhX8aX3ldKjUiDlujQ1IwOtTkOLfKUYUaMOPoPH0LNCZUnGVqufWIfqSeuQZ+c/l5L58LLFyeqsDzNbnfY67h9A94VL6VSpIm81a8SP7bryWpUaDNqyxib3A/57AMpw4jrz4Sh3BZTMB6cSTos9TeYDVmmnyLKSok9lZcB29oYeJyEjkYLOUNWzFt6O+TgVdTjb02VKJDN8Yk4FFBUVKUhOTmPJqhMAOAG1m5djzrQ+bNh3kaVbzxCXYN6zyTnBIMnJa1s41VaeznS01H4O1ekeja+hWoFCdC1XiTE16/He/p2cfBj4+HP1ChfDw8kJQRB4u15j6hYuxr4HflwMCyHNoOdmdCQ3o43PFgGeeCo6CIKxrKMMPpbTw0pyr1Jdm5CRCEBQsjcTyrWklnd9WhRox7rAJQQnB8g8OxUV2+EQn2zx6TGnuHSLTpCdPnePc74P6N2tDqfvBgGQ39sNg8FAjBkvEzM3x6zxsJSutIV3pXauQRRZd+UqO2/eYmLjhoxrWJ/OZSvSZtVCwhIl3OGzo2eTdc52d6sSvBqa6KEYl0akRjPj6jxaFKjPxHL9aZC3GVuTbfheRUXlJUSHxvLd0F/QaATw9qZ6o3L0GdeGeTM3EfEw5uUDZENm+MSS6ieWujZzs8wc12ZkGFi1/gz7D9/grbfa0711DVbtPG/sVG8iSvKrlG6V2qnpBgPDVqyjT7WqfNS2FVv6D2f19ct8f/qoNCe47eBSe65R5XankrypopJT0cfGkZiQwucLxnNiz2X++mwDkSGxz/18qYqF6T22FTERCfzywWrK1yxBn7GtWdG5Jsvm7mT92nNmz8ESp5rjUydHB0qXKkD1KsWoXrUY1SoXo1gRbwB0yamcvx7IhesBHD1/l5CI/6qKCQKM79+UtAw9+07e4uHtSB4C/qExHLtyn/6tavHewFYkJKdy+sbL3zFJ4T+pHSqVP3fdvsP9pcv5pmtHfuvYk76VqvPpkX081Fn5jLaxN+3tTHv7UvWk5ajBExtgywAK2G6DC4ybUXtu32HP7Tu0LVcWB3fjospRo+G1KjXZcPsaKRk2eFCpARTZFxwvIic8ZCPTjC9LgpK9GVG6GY3yt6R1wU5sCl7BrYRrMs/OdNQAiorchNyOYP+h6wzqWo/ebWuyYsc5Vu88T3Kq6Yl4KV7MZaIGUIy8s2E7DUsUZ3KrpgQmxD3xtYD4WCbv20EeR0eGV6/N2Fr1aFe6LLNOHma//5MtGzJnVilfATqXrUCV/AXxcnbheJA//16+QGrmHpudAyhyOVhJ7lVqAAXgy2uLaF/4JqPK9OLDKt9yNGIfO0LWk6xPkntqKio2IfPkmL02xQD0egMbt10wjgFMntSGZnXKsnjzKdbu9iU9w/QdMilCoDkhgCKlbxPT0/n52AlWX7pC2+rlHodO6hUuysXwUOna0Oq0dgufgP3XuHK8EMyK0lx6LPI8l2JvkmpIo5AzlHerjEZw4I7uutxTU3lFMRhEiI6hcH5XGrSpQoM2H7Pyl71smn+QjHQzkhhZsDSAYq1rzfVseEQCX87cjLu7M2kOelyctbw9pBVLNp8mMjbRpDGkCqBI5VapvCr1Onbz9Rv4+N3l3eZN6V+zOr+dPyVd6wCwi0vtuUaVc02qNG+qqOQ0/C76M6npp/Sd1JkRM/oy7+AnLPx2CzuWHc/288Pe64qbpysrZm7m7MHrXDt3jzuXA2jfvxGjP+pJrzGtmdbvF8KTzXvGWerUl/lUEGDcyJb06lIHFxdH0tIyuHglAJ9D17l0NZBzgSFk6LOvDFavakma1ilHekYGeT1dKV0sH0FhsSQEJ7A7/hZX7odSvIAXF24HmTVnKfwnlUOl9OfNiEheW7aakfXq8F7L5nzYuCVT9u+0elzALgGU3BrYVD1pGWrwxIbk5AAKwMF79433QUPbGuX4tnVHpjRsxvxL51hx7SKJ6ZaV5XohagBFUZtgWclJD9nvbq6maf5bjC3bi0kVP+FqnC+bg1YQlhoi99RMxj+5gM3CJ4LBdu3BVHI+4REJfP/LblZvPMvYsa2YOLA5bRtVZOTHy8we61UOoNjCzWeDghmx0tiOgSxzjkxOIjLZGAA4ExLEvIvn+K1jD+Z37cuIbes4HvzfyQFHjYbGxUryZ6deuDs5EZmUSFRyEm/WbcyIGnWYeewAe+772d3HcjtY7o2yrCjRt+Kj9nanoi4ytFQPOhfpyK2Ea1yJOy/31F6I6jsVMgwIMQkWncYGeQIomSxdfIw8bzgyaWhr+rSvze8rDnP4nJ/J10vlYCUHUGyxHg5JSGDlqUto0VCiuCdr+wzhVlQEX584xKks1casws7VT8C+flXCelZJLs1aQeytCn2p6lmTYxH7+Z2Vkoyvuk7FEnxWHOPS4Ru8+b/RjP24Jx0GNOSPGeu4fNJ0zzyNpb61ZrMMzPesTpeKE1CndnF6tqlJ1xbVWLDhBOv2+KI3mOYTawOeSqx+AtKuY3VpaXx78DC/njhFjKPx//EXzduy5c4NLoaHSnCD3FX9BORdkyr9wKHqOhUlo8/Qs/5/Ozm68Qzv/DEed6/s25c36VSDFt1rExuRwNXTfmgdHUhKSOH8oZvcu/6QS8dvU6NReSIexhirFdupotiLfOqodcBR64CLiyNJyWmsWHeKbbsukaAzhgk1gEM+R/TZhE/S0vX8vPQgdauW4K3BrejcvCpLNp/mwJnbOCcYiEyIITgy7pnrTCG3Vj8xiCKLz/uy944fyc7G/y9lvLxxc3TiWmS41ePbMripVj+RhtziO+t3gnIZgmUB/xcidR/Op7FVv+usHLh6jyGr1nAjKoJPmrbm2PCJvFO/CY4aG/0Ryuzfaad+2FmRozd2diihX3Z25JReoCejfHnr/LcsebCJ8u6VKe1WXu4pmY1/coHHFVBUVOyNf2AUX8zcxJtTlzF/vbEVj4NGoG2jighmKsc5wWB1b+xMbOFUWzja1m7W6jRodc862EEQuBoZxvxLZwF4rWpNNFn+h3UsU4GZLdvjoBH47fwpmi77h9E7N/LJ4b04CBrerNuYvC5Z3GNnF8vpYKV5V4mu1WUkMe/eWiZd+Opx6KRB3mYUdSkh88xUVF6MEJPw+ES2JVjaPxuML/Is6aMdGBzNjM828P6MNaSmpjN7am+GdKtv9jhSOVhK/0rtXVs5Nyg4nmk7duHl4srq3oP4p3NvSnl6SXcDOzpWDr8qwatKc+mX1xayMWgvKQZlhF1VXm0igqL4asCPzOg7F62jA+WqW//9nDW+tbdnL1wKYPSb/3LxVjDvjmjL4lkjqFW5uMnXS+FXqdwqpaOldmpCaipanYYSeNK1XCU29x/O9206k89FIj/YwaX2dKic7swp73tVVJRKWEAkn/aczeqv12X79fxFvDm46Tx5PFyY8c9YRn7QnRLlCwEQEx7PntWn+ONT47WCIFjsVEvXrtn5NC1dz6//7GfqjDXExCYybngLPprSlRZNKuCWx8l4v+h0nOKe3VS9cuch6/f6MuOXbbz19RoEQeCDsR1oVb/C489Y61IlOVRKfz6MTyAmIgWtTsMHjVuypf9wPmnaGletZYdansDG3szNvlQ9aTpq8CQbbBUUyekBlLNBwYxdvZEBy1dxPvQhncpWIN1gFIPNAiigBlAU8MIuO3LCgzZdzGBzsA+vn/uSTcHGdjvN8relXaFuaIWcU/BJDaCoyMn1WyGcPnAbpzg9bRpVYtaUXiyZNYJmdcqaPZaSAyi2crSt3JwZJtHqNDhmCaDoH7UCOP0wiPBEHZXyFcDN0bgw8XZ24fW6jSjrlZffz59i/sWz6EWRsEQd2+/e4s8Lp6lVqAhDq9V69oZ29rAaQDGiVNeGpEQQlOxNWEoBehUfwkdVv6VXsUE4aeT/vklF5UVYEz4ByzfFAIs2xQDOXfRnwtuLmfvbHnYdNbbnKF0sH/m93cwaR8oAihTYwru2cO72m7fovGAxPx45RouSpdk+YCTujk6S3sPeARR7I7dTleTSNEM6y/y38IffLrmnoqLymHN7LzOx3nQ2/7gFgJY96tBnfBs0Dpa/57MmfGJNAMVcHobG8smn6/n0m02453Fm4oBmZo8hVQBFCqT0qtRODdXp6LxgMfPOnKVfpWocHDqOEdXr4GDuqZbnYQeP2tOhcgdQVFRULEcURQzRMc/8+o6lx/hp6gomtJnFtbP36P96W4a91wVnl//CBClJaY/HyESOQOfT+F4OYPjEBfy54CD1apfik/e7M25ES2pWK47Do+8XsgufZHLpVjDfzd+Ls5OWycPb4Onu8sTXrQ2fSBVAkQKp/fnZDh/WX7nKxDoN2Td4DG1LlZNmYBuHT+wZQLE3qidfjho8eQFqACV7LoaE8Ma6LQxdsRYAL2cXjg2fyPQmrSjomn0pMUlQAyiyv7TLDiW9yHseCRmJiIgEJXtT3r0KfUsMY0a1OdTxbiT31FRUchTH9l5n5vdbcXV25McP+/HX54PMOhWWiRpAsZ5GJUtQu2gRAESeDaAkpafj6exCXmcXvJyNC7rXqtakZsHCHA18wOobV9Clpz0x5oM448LYw8novGdmK4OH1QCKEaW6Nl3M4F3f2RwMP0PHIr34pOr3VPesK/e0VFReSE6sfqI3iGzbfYmkIB1OcXqmj+/I2p/GMqp3I5wdzXsuK7H6iS0CKFKSmpHBX6fP0HH+Ij7evfexP1uXLPOsK61BrX5iU5TqUhUVJZCemo5Bb8AQHUPDdtV4/Yu+/LzlPcpUKWrxmNZWP5HqtLYpHD15h1ETF/Ld99sBKJjXnX4daj9ROfJl5MbqJyCtUxPT0/nh8FG6L17K5fBQ3qrXGGcHCd2Xy6qfgHzhTdWZKirWY4iOeSaAYjCIRIbE8uN7K1g2dyetetbl7VkDXxr2VEL1E4AN2y7QZ+jv7Nl/jb496vHJ+91p27IKGo3RFW6JxgBKdv68fjeUoxfuUqSAJ/WrlXzm61JUP7HWf1JXEJOC+NRUPt3rw6CVq0lKT2NR9370q1RNkrHtUf3EHs5Uq58oDzV4YgJqACV7ktMz0Oo0uKU4cso/kAm1G3B0+AS+aN6Oom42/EunBlBkf2mXHTnlYfvdzVV8efU3Ug0pjCv3LlMqfU5x11JyT0tFJUcginDgyE1GTlzIj7/vpURhbz4c097s1juZqAEUy3AQBNqXL8fqIYMYXb8u+VyNTsgMoGh1GvpWroaLVsu1yHCCEuLJo3XktSo1AFh05QIxKf8tPDMXhCkZxj6f+Vxcjf1knzcBNYAiG0p0bUJGIn/4reCTKz+RakhhYvmpFHAuLPe0VFReSk4MoGTyw9ydnLnszxuDWrJq7hg6NK1s1vVKbb+j5JPaAOGJiey5fQetTkPDosVZ0mMAW/oPp0ER80O4z0Vtv2NzlOZRFRWlMXf0H3w74ncKFc/LrzumMey9LmgdHSweL6e030lJTSciyhjw7NaqGh+M7cCib4dTs2Ixk8fI6tf8nnmoW6E4tcoVxdmM/35KDKBI7dR70TGMWb2RActWkZSRjpPGQdrDjHYKoNgLtfqJikrORRCEZ8InwqN3cD7rzvDwfgSN2lcnX0HT/q7JEeh8mrR0Pb/87cPw1xdw604oDg4CBoNI0SJe9O5eBwBtbMYz12kdNOj1RkcKL3iRrIQqYkr05/ngh/RetJxvDxxi1707ABRxc5fmIEQucaZa/UQ5qMETM1ADKNkTnpjI+zt20WnhYrZdv8Xw6rU5NGwchfKYV/7ZbNQAiuwv7bJDiZtiT3Mp7iaTL/zAn34rKOhcGBcHG1bqUVFRKIJetHjTS683sHXXRYaNm8/nX29GFMHN1YlPJnSidLF8Zo+nBlDMwyCK7Pe7x7WwcN5q0oTP2relW+VKlMnrTf48eXi3eTPeqN0QgAWXzgHQuVxFynnn42xIECeCAp4ZD6Br+UqAsfKJSDYVT55GDaDIhhI9eyP+Lu/6zuGra38QmRoGQBWPmmjU5YaKwsmJ7XeCQ2L5YuYmpny8moTEFL5+pwddW5p/6kkNoFiO750Q3tu+kwJ58rC+7xD+6NSTMl7e0t1Abb9jU3LCmlVFRU6ObDzDhHofcXT7RYZP7UrdFuYFHLMjp7TfAVi19CRffLcFLw9X5s0cwudvdqVgXneTr8+TBMOa12b++wP56c1eLP14KO/0bYGbi+mt2l6F9jthOh1anYYGnsUZW6s++4eMY0LtBtK1c1ern0iC6kwVFcvwLuRJ6WrGgLohOuZx2CKzjU50RALB9yPx8M6DR17T99HsHeh8Xpgz+GEMX87eyp791wCoW7MU40e2ZOpbHcmX1w2nOP0T1U8K5nMnv7cbyanpL33fqFY/yZ50g4FF5y+QHqvHSePAyl6vsaHvUKoXKGT94Gr1ExUJsf+ufS4g84GTbvqaQ9ZxM8l8wGW4P/cMs1X4x8by8Z69/HbyJO3KlyM8KRGAMTXrcTE8BN+wEJvc9/ED0f3ZJKUtyXxQurqn2vW+T5P5IPV2s/ylty0ITfSgiJt1L/JtiQGRfWEnOBxxljRDOiVcoXfxIYiiyL6wrSTrk+SeooqKXchcPKR5Ob7kk8+SkppO0MMYnICaZQrSvmllureuwb6TN/l340kCQp7tafoiMhcVqR7Wv2iS2qm2crQlbhaBU4GBjFm/kfEN6zO6fj06VqhAhmhAg4CLo5aIxERWXLzE2dvB4A5V8xcEYOfd26QZ/uu7mlnZpKBrHlqWLAPA3gd3H9/HJGTwcLLOWTb/KsW7mS//lOTaDFHPpbibgDcN87rzdsXpBCX5szpgIf5Jd+WenorKc8l8cSfmteyleuYLPL2n+S84rPGw7+UAXn9nCR1aV2X/FT8A6lcrSXRcEveDo0weRyr/Ouqk9a6UzrXFWnjbjZv4+PkxvmEDxjdqQL3CxWixfB56UcL1tp0cK8faVgk+VfqaVUVFTuKjdHw/4lc2/VwWvwexALTqWZc7lwMI8TfdMVmxxrcO8cl29eyhY7c4fe4ew19rwsC+DUlNS+f7hT4mXavVaDh24R73AqMoU6EgpQrlpV/LmvRuVp2fNxxh1+mbj8P/L0KqNajUa1mtTpDUp2eCguj67xJmdGrDjGZtGFKtFt+cOMQB/3vWD24Hj9rToXK6U4nrTxUVJdP2taYMnNKNXycv5tROX/RR0QA45M/3OHxSqEReRBG88pn/gBZiEiz2KZi/dnWKS3+hS32vBHDspB/t21TDzc2ZXT5XuXw1EGIzKFLIk65ta1K/Wimu3w3h8u2HJt3TOcFg1RpVCv9J5VCp16OGeJE/j5/mo7at2Np/OEuvXuSns8dISEt7+cUvwsbetNf73NhEV9nf277KqMETK1ADKNnzMD6B5b6X0KLB1VHLW/UaUzCPG6eCA/nL9zSHAx/Y5L5yB1BA3hCKEl7cPU1OWJSkGYwvIYKSvRHFvLQv3JSmBdqwK2QjxyP3oxf1LxlBRSV3YM3GF8CFSwEMHvMPg/s3om+PenRoWpm9x28ya94eMvTmpdTVAIppJKSm8vOxE/x56gyv1axBnWJFSdPrMRhE1l+9yvXwCACcEh3w1riQlJ5OQHxctmN1K1+Zsl55ORRwnxBdvGW/CTt7WO4AqFIWMUp17dkYHd/fmMfE8gOYWvlLjkceYNvDNWqwU0XR5MQAisEgsvfgdeMYwLsj2lCuZAF2Hr7Ggg0nCY82/dkghX+l9KQtnCv1Wjg5PYPfTpxi1aXLlMubD70o4iAIjKpZlzU3rpCYbnlLpSdQAyg2Q6keVVFRCrcv3AfA2dWJt74eQB53FzbOP8ia3/eRnGjZs8JS39rbs8kp6cxfepRtey6T4GR0ZKUyhShdNB/7Tt587nWp6Rlc8wvhml8InLyJl4crNeqW5L0BrXi7d3P8Q2O4+iDU5HkoMYAitU/9Y2OZuHYzrcqWYUb71kxp0IyD/vdMPwzxMuwUQLGXP+UOoKjOVFF5OYG3Q8hI1zPqi/4UKJaXo5vPEheZ8DiA0uv93pSuVISAO2FcPH7bonvYO9D5IpeGhMYx66cdjBzcjKEDG1O/Tmnu+0cSEZlAlUpFKVk8Hw/DY9mw7yIRMaaXEpFqjSqFQ5Xmz83Xb7D/7j2mtmjOqLp16VGhMgM3r+JBXKzVY6PT2jR8ArZfc8q9znyVUWtfS4CtW/DYqg2PrVvwgPFFXPt5//LNgYOU8vJiSY8B7Bw4khoFCtvupjK14AFltOFRYjmpnFKW8c+7K5l26XuCk/wZWHIUn1T9njJuFeSeloqKXbGkH3YmcfHJ/LPoMIPH/sPajWdxdtI+Dp14uruYPZ7agsc0UjMyWOZ7kfd37OLTPfuYsXcfvg9DSM0wLhAMokhxL0/yODqiizV+U68RhMfVTjydnHmtSg0ANt66Zv0mmZ09rLbfMaJE156KvsRbF75he8ghmhdoxweVv1Zb76jkCKwpXwzWtwWw1MMAUz9azfrN5+ncoiprfxrD20Na4elmnoNze/sdkK7ccSaRiUmcCQpCq9PQrHgpPm/ejkNDxzO8eh20UrUMALu237G3W+X2qRI9qqKiJFKT03ij0SccWn+KQZM6Mv/wDNr3b/i4dYAl5JT2O6FhcSQGJuAUp6dvh9p89U535n05hGrli7zwusz2AnEJyRw/cpvv5+2lkLc7Y7s2Qutgvhuk8qHU7QOkdOqR+w/o/u8y3t64FRHI6+LK9Cat8HSSyElq+x1JUJ2povJyzu29zI+vz0cQBCZ8N4S3fxzJgHe70qxnfT5Z8jZjpvcAYNF326y+l6XrV0t9+rw1q8EgsnjlcUZMXMCpc/coUtiLZo0rUKigBxcu+/PDz7s5vOva4883rFEaDzfTnplq+53sSUhNZeb+A/RdtgKfB3cfHzjMo7XsYOkT2KH9jj2Qe535KqJWPJGQrA+dnFQFJetDzhZVUJLS01l83pcVvpfoWbUKYxrVIzLZ2IanqJsH0SlJpOptUFVCpgooIP8pbFBmoi8nnCa7nxjE9Ct/US9vdUaX6YYuwzhXB8FBrX6i8kphTQWU2Lgk/l502DgOkL9CXlbNGY3PyVss3nyaoLBYs8ZTK6CYzvPK+58LCqZpqVJoBAGtTvPE2ONqN6BqgUIcCXzA4cD7Vsz6KV6hCihKcq7SXJuiT2XR/Q0cDj9DDS83DBj/Pns75iM2PVrm2amovJicWAElPiGFPxceZMPW84wZ3pyh3RtwzS+EQ2fvmDWOlO13QJkVUGxVCfTkzUD6x63kw3Yt+aZVB8bVqs+c00fZec+yk4zPYEe/2tutSvCp0jyqoqIkokNjmTtxHtvn7+fNn0fx3o9DuX0pgEC/MIvHtPa0NljWLgAsW+v+8vMebl0OZvzoViz8ehhLt5zmrzXHsv1sZjsdjSBgEEViE5LRGwwU837y9+qodaBP8+rsPnuLhKSXP2+lOnUtZVs7KZ2aYTAQkqBDi4bWJcowsU5DBlapwY9njrH6xhWT2hS9ELX9jmSo1U9UVF7MpSM3eKPRDEZ/MYBuY9vSsl/Dx18LvPmQA2tOcmrdMTT58gIgCAJOzlpSUyw7iKCU9jthEfHM/nkXhQp64Kh1QG8QiY9PJik57dF1elxdnZg1pSfpGXr+XHWUHUeuYsrj3dp1am5tv3MtLJzPdvigQcCrgAt7B41m0+3r/HLuhDQHDNXqJypmoAZPbITahudZ0g0GNl67zsZrxlLQuMOcdl2onK8A/14+z/JrF63vQZYdagAFUNZDNSe8zLsQc40LMcYEbglXGFduCmn6VLY9XENUWoTMs7MOQW+7SkoquQ9rW/AAZISnsHn7Rfr0qEPnFtXYfew6izedIjg8+7Yvz0MNoFjOvjt+jKhbh4/btGbWocNcehiCoNMwsl5d3qjbEFEU+eHUkVzhYTWAYkRpLwHvJQZyLxHAm97FqjGs9AT2hG5hf9h2MkTb/NlQfadSoIgXlWuX5tZFf6vGsfQFXiZyBFAyX/atWHuauzqjb/t3rEN6hp4dh6+iN5jmFTWAYhmXQkIZtmIdbcqVZVqbFkxu0JTd9+9Yv1mWFTsHUF6l9jugPI8+D9V1KnJw8+xdprT8gsoNyhF41xgk7jKkKaf3XyMm3LK2nfZuFwCWOdZgENm57woHj91i+GtNuBb08tBN5rO/WCEvklPTiY5PojDOBGN8xrWtU56PBrdj2mttWH/kMn9vO/nSAIoS2++A0alS+nT7zVvcjY5mRoc2zGrdiRHV6zDz+EFOPQy0fnC1/Y4k2OMdr+o6lZzO4pnr2fL3Puq3r4FG64CrmzPHt5wjNsLoTEN0DBGp0mEAAQAASURBVADd3u7G4MmdmDdzEyd2X7boXkpqvxMe8fznQnJyGpM/XMm773Rixuud6dWuJnMX7ef2g3CT7uucYFDb7zwHjU7goN89JtZpSM8KVfj6+EHrD0HY2Jn2DKAo4Z3t88gtvlNrXT+FRpqK/o+xdRseW2GPNjxanYa/j53hemQ4HzVpxfHhr/NR45YUzONmmxuqLXgUV1Yqp5RmDE7Oy/W4UGp41eXTanPpW3wYeRxs9OdURUWhWNuC58+FBxk8dh4bt12gY9PKLP9+lMnlFJ9GbcFjPrcjo/jn9FkqFSjAnK5dWDZoIDvHjGRGuzaExCfw+b79XI8yhuq0Gg39K1fHUcrWAKC24JEBpXr2SEQQl2PP06PYQKZX/Y6K7tXknpJKLsUjnxv/2zaVaf8bToEiXlaNZW37HcDitgBguYcDg6NxitPjFKenRf3yfDyhEyt+GE3rhua1k5TKvUpuwWOLNfChe/fptWg549duwiCKeDo582uH7lTOV0C6m9jJr3K135HTp0r1qIqKEhBFkZtn72KIjiGvM7z5VX8WHJ7BwDfb4+jkYPG49m4XAJY5Njk5jflLjnBq/61sv/50B6JmdcoyYUBzXJy0XPcLITI2EecEA+7JAsM61CM+KYUT1x7QpVEV9s99g48Gt8Uzz8ufua9C+50b4REMX7mOSVu24eHkzKiadSUbG7BLK4FXpf2OiorK84kJi8Nn5XH2Lj3Clr/2Efkwhoz0J6urPzh/h6SEFD6bP45vlr9B8bIFLb6fUtrvvIi79yN49/0VzPppB8ULebHwq6EUzm/6s0Rtv5M9cSkpfLJnHwNXrCI6OYk/O/diaY8BODtI4Lpc0H5H7jXmq4AaPMmGzAeWVJtaoAZQnsepwEDGrdlEryXLOBxwn4l1GtKvko03HzIfjjKEUNQASvYo/YWeiMjqwB28deErDkWcoU2hLnxe/SfKu1eRe2oqKnbHmgBKdEwif8w/wOCx85j98y4SEo0p5kFd6lG0oKfZ46kBFNMxiCILz52n//KVXAwJwcvFheikZLbfuMXbW7ax9vIVtDoNWp2GDmXK82O7rhwYMo6BlWvgYEXf9mxRAyh2R2mejUmP55sby5l57Xc0ggOTK82gV7HBck9LJRfy4FoQq37YSsvudZh/5FOGT+2Ks4t1vY6tDaBYszEGWOxggI8+XsuMrzciiiKz3+vN/JlDqFy2kFljqAEU8zGIIqE6HVqdhup5CtG6VFl2vTaKOW27UNRNwmdzLg+gyImSHKqiokSiQmJ4veHHXDx+m7Gf9OLv/R/TpFMNq8a01LXWBlAsu84Y8My6bBJF8PZwpXndcnw3pRczJ3WnbPH8rNvjy/bDVx9/rnXDClQvXYQ9R68zc+k+Pp6/ky3Hr9KpYWUO/Pgm7/RtgaP25UGeVyGAsvv2HTovXMIXO/cDUN47H1MbNsdVa933do+xsUPt6U+51qFKW3eqqOQ0rp+6w1uNP+GvD5ZTpW4Z/vKZTq/RLa0a094+Ndelogh79l9jxIQFzPppJ2FRxvnWrFjsmRDn88hNARQp3en7MIR+S1Yy0+cAIboEUvXGaiWSjJ4LAptKeWebG8kRwRNBELoIgnBLEAQ/QRCmZ/P1VoIgXBAEIUMQhAHZfN1TEIQgQRB+N/feagDFiK0DKNfDI3hv8046LVzMmjNXAOhVsQq/dehB9QLmvQw1CzWAIuscnkbpC5TotDj+8FvB1IvfcSvhKg+TAwDw0Jq/Ya6i8jT2dp1gMMhy8hqMAZSDR2/iFKenuGMe3hzckrU/jmX6+I4UKaAGUEzBUi/fiYri/R27GLRyNRM3bmbqjp3ciogkazFHn8t3Gbt+I9Epycxp1wWfwWPpU7GqNAuTrLxiARQloDTPXoy9waQL37EucBcPEu/IPR0VO2FP34kGA4tnrmd83Y847XOV7iOa4+Bo+SnsJ+YhYwDFGgcfO+XH2Df+5ftfdlEwnweaR2/zzM0YShlAkQqpfWuLNfDZoGDazfuXhWfP06tCFQ4NHcf0Jq0e/3+QhFwaQJF7/ao0h6ooGznfY8rFw3vhzOw3l497fk96agYf/DICD+88Vo1pjWvteWI7E8dYPUUdjGGT90e3458vB/PD+31o1aACt+6H8d38vfy+8gghj1orODk6MKRbfSJjdBz3vUdySCKXzj7gl43HmLFgJxuPXqF04bzk8zD92SdlAEUqpPRpakYG0cnJxgMTRcozuUFTDgwZS6+KEh0Ms4NDX5XqJ6ozcz+vouvsgUFvYPOfexlX50MOrTtF8IMIq8e01qfmYolLdYmp7D98A6c4PeVLFuDvLwbzzxdDqFTG9L1Ba9enSvKnVO7UiyLLfC8yY/s+tDoN5b3z4TNkLO1Ll7N+krmg+gko551tbkKeviNmIAiCA/AH0BEIAs4KgrBVFMXrWT4WAIwGpj1nmK+BI9bMQ6re1plkfQBJ1UfTluNmYov+11nxj4013idNQ37y0KZ0WXpWrMLhgPv85XtGmh6e2WHH/thZsVfvshehhP7ZT2OP/qDW4J/0kK+vLwOcKOmazNsVPybNkMbmoBXcS7SyX14ORRCE74EGQCWgAJAM+AObgd9FUYzK5ppmwKdAE8AVuAP8C/wmiqL+6c/nZuR0XeYCwpKe2GBZX+ysREQmMHT8PIYNbEyPLrXp3qo6249c4581x4hNMO+5JKWrpe51LfV4mVjqZV1a2gu/fuT+A47cf0CHCuV5t2VTJtZpyJY7Nyye54sno7Wrf+Vyr5J8G5rooRjHphnSWRmwHYASCl/rqa6zHrl8FxYQyazB/8MzvztJoiMaBw3vzR3CzuXHuXH+gdm/j6xY00MbrPOwpQ7WG0R27r3Cnv3X0OsN4OXA9PGdcNRqmLfuBKGR8SaPJYV7pXSkLXyr1QmSrn/jUlL4/vARll7wZUqLZlTImx+DaBxfACS7k538mqxztqtT5fap0teq1qK6znqU8h5TLi4cuMabjT6hbI2SJMQmAdBnfBt81p1GF2fZ31trXOsQn2xzx3q4u1C/TmmaNChHzeolKFbEG4A7/uH8ufooh87cJjQyHr3h0bNeMJ7wblijNFXKFiE2IYkSRfISE59MQEg0aeHJnEoO4PK9EDzyOBMWY/4ulqPOehdK7VSpfbrw3Hl8Q0L4tEMbfu3Qg5HV6zLz+AGuRIRZP7iN3xPbc00qtzeViuo763jVXWcPYsLimDtxHgCafHkZ9VEPChTxYt7MTY/9ai6W+tTSNaul69Wga2HM/t9O3hjXln+/GcZmn8vMW3ec+MSUl15r7fpUCvdJ6U+p3eme5oTeYGBht37su+/HzOMHCEowff2fLTZ0pr18mVtdKZfrckLFk0aAnyiK90RRTANWA72zfkAUxQeiKF4Gnom0CYJQHygM7JViMlJXQIGcWQXF1hVQAJb5XqTV3wuYc/go1QoUYnXvQcxt28Wm91QroChrxycnJOSDk73ZGHSYvI75eK/yF4wr+y4FnQvLPS05eA9wA/YBvwArgAzgS+CyIAgls35YEITeGBcXrYBNwO+AE/Azxuf8q4bsrpOi9L+lJ8Mio3T88vd+ho6bx9Zdl2hRtxzpGcbvYxw05rtGrYAiHT5+d+m1aDnj1m5CBDydnFnTexCdylSQ9kYy+Fcu9yrFt0p0bFCyt9xTeBmq66xHVt/FR+kwRMdQ2MuROi0q8dPm95j+xygKFc9ryXBPzi0HVkDR6w2PrtcTF66jXeNKrPlxDFNGtMHbjNPVYP3pMni12u8AhCQk8NGuPby9fisAJT282D9kLH0rVZOuylgurX4C8p9MU5pDJUR1nfXIvraTG32GHr+LDzBEx1C+tDfjP+3NgiOf0nVYMzQWrO8ysdS1tnSsVqvh3Tc68MVHvejcvgbJyWl8/8suhoyfx+vvLGHF9rMEh8chZtkzyvx3QRBYsuUUdwMjmTa6PV+/050OTasgCEav6iNTLQqdZPIqtN+5EPyQ/ktWMn3XHkp7edOjgsQtsdX2O7kZ1XfW8cq7zp4YomNIi4mnTe/6zDv4Ca171bVqvJzTfmc+m7f70qdDLRbPGoHWwfTtbLX9TvZcCw+n16LlzD50hOYlSrNv0Bgm1mkoydi5pf1OLkMW1+WE4ElxIGuZi6BHv/ZSBEHQAD/y/FSlxagBFCOZDz1bhVASUlP558xZ2vyzkM/2+rD7vrEMu7ujEwOr1MDZQZpS2T0rVKZC3vz//YIaQJF1Dk+jxM2xTAyIHAg/xRvnv2al/zaqetZiRrUfKO9WWe6p2RtPURSbiKI4VhTF6aIoviOKYkNgFlAM+Djzg4IgeALzAT3QRhTFcaIofgDUAU4CAwRBGGz/34KsKMZ1cgZQIqJ0/PK3D0PGziM9NBlBgPlfDeXDsR0oXsjL7PGU3CovJwVQRCAiMRGtTkMpBy8K5nFjXtc+bO0/nA5lykvbgkemAIocKMW3SnasAlFdZz2K8F2wXyhja05j+axNNO5Yg3mHPmHURz1wcrGsetcT87QifALyteBZsPQow8bPZ+/+awzoXJf1P4+jaZ2yZo0hZfudVymAohdFtDoNXhnO6NLS+Ll9N3YMHEnbUhKUP84klwZQ5HZpLnWo6jrrUYTrlMId3/tMav4ZAbdDmTx7ED9vnUr1htY93+y9YQbP3zTTCALhjyqFpaVlcOrcPe7cDSMuLglRNIY7neL0jytbZeXYhbvMW3eCybPW88bM1cTrUpg+viODu9Z/PLYUbs3tARQRWH/1Gh3nL+LPQ6cAaFq8JG/UaYSzgwTuU9vv5FZU31mH6jo7s2L2Fia1+JywoGim/zGaL/+dYNUBipzSfufXf/YzYfIS/l57lIxHByfKlyxg8hhq+51nyTAYWHD2HJ0XLuJgwH3yu1rXGvEJckH7HbnXmBIji+tyQvDEGt4CdoqiGPSiDwmCMFEQhHOCIJxLT0k06wZqAOU/bBlASc3IYNWlyxy6eh+tTkP3CpWZ07YLx0e8znsNm1HQioejp5MzP7Xrxt5Bo9k7aDQja9T9L9CiBlBkncPTKPnFXqohjXVBu3nj/Ew2BO3lfqIfAKXzlMNFo6z/jrZAFMXn1bpb++ifFbP82gCgILBaFMVzT43x6aOfvin5JHMvJrkOnvRdWsaLyzLKGUBJSzOW5vNI1nD7RgjdW1dnzU9jmTmpOxVLFzRrLKk9/SoHUABuRkTSdcESPty5G28XFxZ07cvOgSPJo7V+s/YJ7OxfOb2rFN8q1a9KQnWd7Ji9tksTn1+ONTUpjWXfbmJ87Q85tvksTTrWICNdmgrZ1lY/AXkCKBFROub8tocxb/3LqcsPuBsQCUCRAp7kcXUyeZznuVcjmOelVy2AcjMikv5LVjJl2w7cHJ1Y1L0fa3oPMvu/2wtRAyg2ITc5VHWd7EjqOqVw70og09p/xXej/yRfYU8++GU4DlrrXklnulaw4BlpzYntp/2alq5n3uIjDBn3DyfP3mXYwCbM+WogI4c0o0bV4jg5aR9dawygPI8rdx4ya/5eYuOTGNW7McUKeT0RVlFaAEUqpPSpLi2NxPR0tDoN7UuXZ3rTVhwYMpZ+UlUSs8Nmmlr9xH6ovpMVy95j5gDf2Zr7VwOZ0vJz/pm+kmoNy+Fsxhrtedi7mpgla9W79yM4tPMaTnF6WtQrz/LvR/HVO90pWtDTpOtzU/UTQDJvhiTomLxxOz/6HAOgefFSLOjal3Le1leEzQ2+zA2elMt19t9RN59gIGu5lxKPfs0UmgItBUF4C3AHnARB0ImiOD3rh0RRnAfMA8hfpKxYqXQhbvuHmzXJrA8ua/pbZ8UW/altOW4mmQ8+KXuPPc2Gs9cIDo1nVJO6vFO/KW/UbcS2OzeZfngvGQbzJNK5bEUcNBrOhQaTmJbGp83a8FmzNux7cJd/L5/nXGiwzXt7Pg979vx8Hkrsb6bkvtqx6fGsCtgBeFA6TwITyk/FQdCyN3QzRyP2kyFathGfg+n56J+Xs/xau0f/3J3N548ASUAzQRCcRfGVWVXY3HXwpO+88hQ16SFtaR/PTCzt5wmQkprOT3/uY8nqkwzsXZ9e3erQqVkV3v1uPWeu+Js1ltSeltqltnKzLZysF0U2XrvOlus36Fm1CtVLFiYpw/j/uX6RYlwMC0GfzYk+i7Czf+X0rhJ8q2S/KhzVdaZj17Wdl0P+lz6MIoKj+WHcPzi7OmFwdSOPuzOzVr3Nln+PcGjzeUQrnmeW9tDOikN8st0d7B8YzVdfbzH+xMuBjyd0onKZQqzaeY51e3xJSjHt+9lM9zoVdqVfi5p4ubsQp0vhxPUH3AmKNHk+UjpSat9K7VkR2H7zFrtv36F/jeoUyJfn8cZjlXwFuBlt+n+3F2InvybrnO3qVDld+go4VHWd6SjOdUrh0LpTnNxxgaJlC6HPMODorGXI5E5sXnCY+BjzDgJmMu3zXsRGJrBq2Ul0Zm6AWerY7PwaGh7Pl7O3UqyINxNGtWJwv0Z0aF2V/YdvcOj4LW77hWEwiI/DJ+neDk+03xEE8H8Yze5jNxjWoyFdWlTj340nn7l3plutWdM66qz3oNJ9OnvvEQ7duM+H7VryU/tujK/dgK+OH+TUw8CXX/wybOxQe65JlbAGVSCq70zD/u8xc5DvbInBILLxt93sWHiA1KQ0NPny8tY3A7h7NYh9a09jMFj2n0mISbBo3Wrpe2NL16pXTt5n8crjDO7fiNYNKrBm1wWWbDlNYnLaS6+11qFSuE8qf0rpTcOjCpxFHTxoXKwEe14bzZKrvvx67iTxaVY80uzgS1u7Mhd70qauywnBk7NARUEQymKU12BgqCkXiqI4LPPfBUEYDTTITmBZKZDXjSXfjcD3RiBrdl3g6Pm72ZZDfBFSLAKykjUJJ+WGlK3GzcTWAZRTgYGcCgyktLc3o+rXpWg+j8ehkxoFCnM9Ktyk/3e9K1UFYPbJw9yJiaJZ8dI0LlaC1qXKsrbPYFZcu8RnR32MH86a1LNjCEVJARRQzoNW6S/3/JM8+Pr6PMaV7Ua/EiNoU6grOx9u4Gz0MQzPtrfMFQiCMA3josELaAC0wCiw2Vk+ltmH6PbT14uimCEIwn2gOlAOuGHTCSsHu7rOEuQMoERF6/h70WGWrz1Flw41OH/d+LKoY9MqpGVkcOScH+aoWkpPv+oBlM3Xb7D5+g20aChcxJ01vQcTnBDPX76n2XDrGulmhkGfixpAsStK96vcqK6zCsX6LjU5DZLT8MpXBI1Gw4e/jqD/621ZNHsb5w/dtGpsawMocjrYKU7PggWHGTmqOW8Masngbg1Ysf0sG/ZeJDnVtABK3cKF6FKvEuVKFiA2MYXXezbFLziSv7ed5MS1BybP5VUKoGQYDKy5fMU4NhpqlS/Cxn5DORL4gJ/OHONieKgk97GHX+Vwamyiq2wezS0OVV1nFYp1nRJITUrjwTXjIfcaHWry2tsd6TW6Fat/28uWRUdITzX9eVSuWnFa96qHxkGgz/g27F55kqULjxAXbXqIxRrHZufXh6GxzPx+K/OWeDFxVGsG9WuEm5szN2/vBaBurVL4Xg7AMVb/6FpjxWVRNIZPUtONv3/9o3YCgkC2a13nBIPV4RNQZgBFKpeeCgyk/5KVdKtSmWmtW1Ahbz5pgieZ6LS5xp9yelNuVN9ZjOo6mUlNMgYttEk6ylUtTs9RLek5qiX/zNzIlVN3LRrTmnWrNWFOc9apySnpLFpxnO27LzFuZEuG9WhI41plGPXJMpPHUIJDlRhA2Xz9Bkcf+PNei2aMrVWffpWq8c2JQ2y8fd26gW245rSXK5XwrtYa7O06xQdPHv2GJgF7AAfgX1EUrwmC8BVwThTFrYIgNAQ2AXmBnoIgzBRFsbol9wsNi+ePBQfp17ses6f25mF4HIs2nWL74atmjyV1AAVyZhWUrKWfbBFC8Y+N5av9B433QkO+Qq5s6jeUYF08iy5fYP2tqySmZ/9StGr+gtQvbDydfSEsBIA99+9wOPA+625epX6R4viGPQSMpaGfCLLIUAUlawkpJYRQlPKgVfLLvbu6AD658jc1vSozrmx3hpd5nei0SO7orBS2cpkGFM7y893AaFEUI7L8mtejf8Y9Z4zMX/eWdmrKxd6uswY5N790iams33IeB4z/kfp1rE2dKiXwfxjN8m1n2XP8BukZprdJUAMo0hIWquPtzVt5q0UTZrfpzOQGTfnb9yxrblwhVS+RK1+xAIrcnlWyX2VGdZ2F5ATfBfuF8k6zT2nVvxFjvhrEN8ve5OLx23w5Zr4xnGIFOTWAcssvlBmfbaBKxSKMGt2Ct4e0QhRFVmw/9/KLgat3Qnjv+w2kpeupVbk4ZSoUomvjKvwwsTs/bzjKhiOXXz5IFl6lAEomt/0j+e7gYSY2acjm/sPxeXCXn88e51qkeZVan4uNN8/A/k6Ve80amuiR0/2pus5CcoLrlMJ5nyu80egTxs8ezrgZvekxqiVLvt/Owc3nTbq+z/jWaBwEtv57hIwMPZ0GNaHrsKbsXXOapQuOEB1h+t9BqQMoIaFxzPx+K3//64nnozErVyjCh+924dCxW6xYdwqdLhWnOD0OGoFkDw2e7q4Uymf8HiHjUfDkRQcspKp+AsoKoEjpUhHYcfMW++74Gd/p5oHBVWvSsGgJ5p4+Rkiilc9pOwU41eonNkX1nQWorlMOaSnpTG37Ja37N2bcrCH8sG4yx3Ze4q/P1xMdFm/RmJauW+1Z/SQiSsfsn3exYesF3Iu5AaB10FC/eklOX355lWwlVRBTkjujkpL4dK8PKy5e4pMOrcnjaH07p8fkkgBKDvWkXV0nWFM6ODfi4VVCrNdiMg4agWZNKtC/X0POXvVn0aZTOGgEihb0Iigs1uLxpQyhgO3a5dhq3Exs2YZHq9HQqWIFRjeqR70ixYhPTWH1jSvMu3iWyOSkJz77SdPWjK/dgBPBAfx27iS69DRuR0e+9IS2h5MTZbzyEpqoIyLp0UkKO7fhyUTOAEomSnvYKvkFXw2visSmnQWgeYH2RKSEcNvEEMrv9VeeF0WxgbVzcM9fUqzZeYpJnz21apo/kLWm97xHZQ5fiCAIhYFmGFOTHkAPURQvPPrabYz94yqKouiXzbXHH13bTBTFZ2vLqkiCl1NhsWmVCVaPY+nmVyaWBFAycdAItGpemSGDGlO5bGEiohP4aclBDp29Y9F4Ujpaao/aysu28nHLMqV5q0VjahcqSusVC6x/ofci7OhfuZyrFM/aw6+bW/wpievA9r5TXad8vBzyi01cult8vdbRgW7j2lGlWRXmTlkOgJunK4kW9LLODmta8IB8Dq5WuSi3Y2NISkmnRb3ylCjizSafS6Smmf48rlWpGDMn9yBdr+ejf3ZwM9DyAIWUjpTat1J71s3RkZH16jK+cQMMokiTpf9IF+7MxE5etbdT5XRpdv6Uyneq61SsdZ1SqN26KhO/H056WgZT+/zvpZ8vU6UoP295j9DAaN7sMBsnF0dqNCpHo/bVadm9Dt4FPfj1qy3s3mhaiOVprHHs8/xavKg340a0pEnDchw9cYd9h65z7UYwySnpODlp6dqnDuP7NyUxOY1P/reNm/fDzLqvFOtZqTwopU+ldunERg15t3lTAP69fJ6/fM9Y10ogKzZ2qD3dKYU3T3X+Pke4DlTf5QRyi+9siZOLI/0nd6XHxA5M6vKDWRXAXoSl61ZLXWrpOrVr3zpMH9+Js1f9+XX5YfwCIl5+0SOsdagU3pPKnVJ6UwDS3Q0MrlqT9mXKM+vEYe7HxVg/sA19aS9XZnpSKtdB7lnbKb7iiVzoDSJHT9zh6Ik7aDQCTgaRFp2q8vU7PTjue4+1uy9w7lqA2ePaqg2PrSqg2GJssO2J6wyDgZ23brPz1m3qFC3K6Pp1GVWjLkuu+D7xOQEQENjhd4vmJUqxqvcgbkZFsOjyBdbdyr7CjZezC8Or12Zotdp4OjsjIHAjKpw/LpzmUMB944fsHEBRUhsepWyMKfmE9tW4O4A3DoKGtoW6UNilGDfir7Dt4RoCk+7LPb3siLREnKIohgGbBEG4gLE811KgxqMvZ6YjvbK7Nsuvx5p7XxXzsPbkNUh3+hrMX1joDSIHj97k4NGbNKhbhiGDGz/u6enl4YpGgBgzNgfVCijScfSBP0cf+FMmrzch6cY/Z7906M6t6EiWXvFFl25d1YAnsGMVFLmcqxTPKtmvEmC271TX5X4y0vVs/XsfW//eB0CRWuX522c6h7acZ9Uve4l4aN0Ll5xaAeX6LWOlSCegWZ2y9O1Qm2HdG7Bs2xm27L/yuE3Ai7h8+yF7j15neM9GNK5aipuB4Y/bCZQq5E0hb3fO3Q4yaT6vUgWUxPR0/jp9huUXL1G5QAFS9RloBIEPG7dkzY0r0rwEtJNXX6UKKAryp+o6FcVy6fANJjX9FM8C7oAW7wIevPFVP36bvjbbwGf/19vh5OLIwU3G6ltpKelcOHKLG+cfcHzXZRq2q8aV/dcQYhIsbhkA0lVAAQgOieV/f+1jWFQT+nSrS6P6ZbntF0pkdCJFCnlSr3ZpdIkpbDlwxezQCagVUExl3pmzbL95k/daNOf1uo0YVLUmXxzbzza/W9YPbmOH2rv9Dsi/BrUA9T2myitLWko6q37Yyrqfd5CRrschfz4+XziOk3uu4rPuNAaDZc9Re7vU0nXq3q2Xcc3QMHpYc5bMGsGOw1f5Z91xomJfHsCx1qG5tf2OCGh1GrRpGpoUK8neQaNZctWXX8+dtC60qVY/kQLFru2kLb+RS8l8IF8748+SVSeoXqEov80YyPLZI+nZpgYOGuElIzyLc4Lh8cNMChx1//2QGluNC8aHYNZWPFJzMSSEKdt30uyveYSFPvmbEIGfzx7n3f07qL/4T8bt3ESqPoPv23ZmZov2OGkcnvh8UTcPZrZox/uNWpCQlsrq61dYetUXDydnfunQnb6Vqhk/qNP+98OOJOucn2jFIwexia6PFyZKIDTR4/FLPqWhFw28c+F7/r2/npJ5yvBhlW8YW3Yy+Z0Kyj01SRFF0R+4DlQXBKHAo1/OXM1XevrzgiBogbJABnDPLpNUQYhJeLz5ZSkO8cmPFxSW4hSX/kQQxRzO+T7g/Y/WcOm48Y/NyF6N2PTrBKaNbk+xQs/7fil7pHS01A61lZNt5eMHMbFodRrcUpzwdHLmw8YtOT5iIlMaNMPL2UXam9nRvXI5VymeVapb5UJ13atD8sNwdi85RIf+jVh45FPe/Ko/eQt5Wj2utR621sHW+Pd/P+/hnQ9X4v8wmvdGtqNdk2f+yGeLo9aB5FTjPV30Aq468XE7gYk9mvD3ewNY/8VIBrauZfJcpHSk1L6V2rMJqamcCw5Gq9NQ1aUgI2vUZd/gMfzQpjMlPMz7vue52Mmr9naqnC5V8vr0ZaiuU7EHoigSF5GAITqGMiU98crrTlJCyjOfK1QiH5VqlQKgcYca9H+9HZXrlEbjoCE5MZUrp/xYOmcHDx8YTzpb41lrHJudW+MTUvhr4SFGvrGQE2fuUqZ0AVo3r0TN6iW4ez+cn37fy+a1Z3GKM7aQLVbIi8XfDqdFvfIm31eK9axUHpTSp1K69GF8Ah/s3E2fpcu5FhlOQprxYISjRqKtEhs71J7uVML6056ovlPJDWSkGx3iLqTjmded9+YO4Zft71OjsekueRprXWoJ5q5R9XoDG7ddYOj4eazbfI4uLavx1STzquRY61ApvCeVO6X05upLV+gwfxEbr15nbK36HBw6jm7lTFv7vxAb+tIersypjrS169TgiRnExCaxeOVxBo36i+9+3oneIDKsR0Njj0jA2dH8vyCZDzJbhFCkxh4BFFuFUOJSUh7dR4NW998f+6SMdAyiiAgcDLjHFJ+dXI0IY0SNOtQvWuyJMcbXrk/38pU5HuTPxN2b+fbkIeaeOcb7B3aRkJrKO/WbUM4775M3VgMoikCpL/jSxQy2PTzIxHNfsiZgJ1U8a+KokbBvnnLI/Mukf/TPA4/+2SWbz7YC8gAnRFGUv4/UK0ZuCKAYr9ezc8tF9h28Qa92NVn701hmvt2NiqXNC3blhACK1F62lYtTMzKYsHYzfZau4FRwIFMaNuPY8Ak0KVZS8nupART7oFS3yojquleAuMgE/v5gBWNqTmPv8iN0G96cBYdmkMdDmiCdEgIolnD5WhBTP1zNOx+uZO+Jm8/9nEb4zy8VSxekXrWSpKZlEJeQjEEUcU4wUNrZnRY1yhIQHkN0QhLTXmvD0V/eZkr/luT3zGPSfF6lAArArYhI2s5byNILvvSuWJUDQ8byVcv2uDla3s7wCXJxAEUucrA/Vdep2I0L+6/yUedv0EdFP/O11OQ0/vpiA19PWEhKUhpjP+nJ298OpFaTCo8/k7nplhU5AijPW9uGRcQz59fdTJi8hPc+WcPwifOZ9tk6Dh67RXJK+qNr9RTSOOPq4sicaX2Y9+UQ6lQpbvK91QDKy7keHsGY1Rs5dt0fgKkNm7Ox71DqFyn2kitNxMb+tGf4RO71p51RfaeSK4iLTOC91l/w3eg/8cznzpz1k/nkr9F4eJu2rsoOS10qtUdfhE6Xyp8LDzLy9YX8vPQgYKyM3bVlNQQT9SGFP61FaQGUqKQkZuzdR5+ly/GLiSIpw/j/RRIj2zisqZItNnOdGjyxgLR0Pbt9rjLh7cW8+8FKRBFcnLVs+GU8X7zVlarlCls0rhpAMWLrKijGezwZQMnEPz6WTbevA9CxzH8L1mbFSzGoai0e6hL45dxJAuLjEACDKHItMpx9D/wo45WXsl55nxkTUAMoCkGpm2TJ+hRWB+5gzJkZnIsxln4bXvp1+hQfSh4HG/S6khhBECoJgvDM0UpBEDSCIHwLFMIopcza3+sx9p8bLAhCgyyfdwG+efTTv2w8bZUXIGUARa4T2IHB0cz5dTeDx/7D2k3naF6vPEO7W9ZuUckBFFuNaSsXXw0L4+0N2+i2aCm7793haoSxhHSNAoUpmMdN2pupARS7oFS3So3qOpWsRARH8+vkxYyv+xF/fbDs8Wns9v0b4u5l/d9JOQMo1rj38rUgHKLTH5/SBp54sWcQRZwcHWjdsAKfvt6F+tVKcfDMbU5dfvD4M91aVcfd1ZlNey/y4bwdfPrvbg74+tGrWXX2fD+R9wa0wtnxycqUz+NVCqBEJSUx6+Bh2s3/l3VXrtKkWElS9cb/DxpT366+DDsGUOyFEjyqJFTXqSgVQ/STbcTionRcPHabU3uvMmPYn0wf9Aee+dz4bP44amYJnzwPuU5tP68Cyp27YYRHJBAbl/TM16/fCmH06/8y97c9FCngwV+fD2b21N4mb5yB8gIoUiH1elWr03A3JJpi7h5s6DuUvzr3ooyXt/UD57LqJ7nBm6rvVF41Dq07xfjaH7D06w0UL1eIlCTrM1I5IYDyMDSWgCuhOMXp6dG6Bp+/2ZV/vxlO3aolTLpeCdVPMseRAqnWoNfDIxi2Yt3j0Oa0xi2Y36UPpT29rRs4h1c/URpyus6+O+G5kJjYJJwAN3dH9h+6QfdONenSohqXbwWzZvcFDp+9g97M3mlS9OTMStYHk1S9qm05biZS9/DMxEWrJSXD2DvMUachc3S9uwERuBkVgSiKVM5X4PFnB1SuTh5HR/65eJULYQ8BY6se4dE/r0aGA1DWOx/4v6DSkJ36ZWcl6wPVXv2zn0ZpfUEV1GP7CVINxtKeD5PzostwpG2hrjQr0Jb9YTs4GL5L5tm9kG7Ad4IgHAPuA1FAYaA1UA4IBSZkflgUxXhBECZglNkhQRBWA9FAL6Dyo19fY9ffgUq2ZC4gLOnjmRVr+mOD5b09ASKjdPz97yGWrz2Js5Px+gqlCvLh2A4s3XqG4753H5f2fxlZFxvWOtoWDpWyj3YmtnLx7chIPt6213gPNPwwsDPlvfOx5uYV/vY9w0OdhM9nO7rXnj23s6IEzyrVrRKiuk7lGULuhxNy37gOKF63AlN/GkpSQgob5x9ky8LDJOmsexZY62Fr/GuNe43X6x9dbwyJFMzrTpPaZWnXuCKNapYhJS2dvSdu8OuKw8QlGOfpnseZ7q2q4xcQwenL/iSHJHIk5Canb/iz49QNGlQuQWp6BqnZnGJ/EVL6UWrXSu3ZMJ2OL/btN7YKMICrtyPbB45g8+3r/Hv5PInplleUe4wdvGpvnyrBowpBdZ2KYskMn2jyPXngS6MRuHLKj3V/7eetr/vT6bXGXD19F9GERZ6lnrW3X/V6A9t2X2LvwWv061GPPPldH69h83rmISb+2cBKdkjxvtlRZ70DpXSp1B7dfP0Ge+7cYWyD+kxo1JAOg8rz2VEfVt+4Yv3gNvanPd0Zm+ia052p+k7llSM1OY0Vs7ew6oetGAwirsUL892qt9i6+CiHNp+3eFx7u9TSder6laeICYpj/JjW/PnZIA6f8+OPlUcIDI156bXW+lMK70ntTqm8qdVpiI1NoWmNUuwbPIZ/L5/n9/On0KWnWT6oDX0p13tbmZDNdWrwRCISdCn8Mf8Ai5Yfo2vHmvTrU59v3+3JuM9WcP1uqEVjSh1AAdtsStlyXHgywS7FA7F3taoEx8dz7IH/49CJgPEhKQI1ChZGEATCEnWkZGRQ3N2TdqXLkZqRwTa/m49bKwEIgoAoilTMm9+8ScgQQAH5H6xKe6Gn1E0yAyK/+y1n68P9jCvbhR7FBtKqYCd+YpHcU3sePkAFoAVQF/AGEoHbwDLgV1EUn6iPK4riZkEQWgMzgP6AC+AHTH30eWl3uVWsImuC3ZoQipwBFJ0uFR2pOAGFHJzJ7+3GnGl9uBsQwbJtZ/E5dQu93vQUu5SOltqhOSmAksmkTdt4vVEjBteoxZCqtdhw+xp/nD9NYEKcdDdRAyh2QalulQDVdSovJNjXj7eafsbIrwYxclp3eo9tzbo/97NtyVHSUqzb7M+pARRXF0caVShBs0blada0IgXzupOcms62Q1fYcuAyN++HIYrGqiiiCM3rlqNIAU9uPQijZBHjxmJgSAypYclcCnvAhTtBOGpNq3aSHa9SACXdYPw+xTvdBb/oKN5v1ILRNevx54XTLL92iVS9BC5UAyi5EdV1Kool8/2bGBOLKIqPAyiGR4f9Lhy+QXR4PDUal8fZ1ZGUJNM3PISYBIsca2+/pqZmsGrDGeP1QJWGJfn909fY5HOJxZtPmxVAsTZ8Ark3gJKcnsEfJ0+z+tIVJjdvyoWwEADyuriSnJH++DCjxeSSAEoOd6bqO5VXlkxvemr1OGgd+Oi3kfQY0YK/vtjA3atBFo9rb5ea61FRBJ/DNzhy8g4De9dn2GtNeP215nz663aT75mbAihSenPR+Qtsv3mL91s25426jehfuTrTDuzicOAD6wa2cQDlFQifyOY6NXjyFIJexCku3eKTXUnJaWzYep5N2y9Qu0ZJrvsbQyfTRrfH3c2ZzfsvcfFmsFljSnnCOpOcGEAB6x+ITg4O1ClWlK87dWD+mXPsuX2HmxERpD0qQ1ynaFGGVa0N8LjlTquSZfB0dmHP/Ts8iIt9YrzMEEqjosbyXKcfBpo3oVc8gALKWKAodZMsICmEL64topL7QXoWqyf3dJ6LKIpXgUkWXHccY/JSJQchRRUUOQMoAOcu+jN83Dzata7KkEFN+PLtbozq3ZhhHy02ufpJJmoARRoCYuOYsXcfv588xcRGDXmtVg1OBQcSmBCHRhCeCH1ajRpAsQtKdaulqK5TMYX7VwOZ2W8uleqVZeRXgxj8Tkf2rD5pdfAkk5wSQClS2It2rarQulklKpQrhEaj4dLVQJatPMG+y37EJ6Y88XlRNLaD8XBz4cg5PyqXLcTsqb24eS+MlTvOceD0beP8YzMwkAESVR17FQIoEYmJvL1hG7WKFOG9Ns34rHlbXq/TkO7rlxGRlCjJPdQASu5BdZ2KkqnSsDwZ6Xru+N4Hnq2AootLRjSIOLk4kr+IN8H3ws0a3xrHyhXwjLgbw869V+jXuQ692tZko88lVmw/S3Q2LXueRqrqJ5B7AyhRSUl8sW+/8Sfu8FWL9jQqVoLfz59izY0rpBnMq8D2DDptrnBnTnSm6jsVFQjzj2Ry88/oNKIlY78exO+7PuDAxnP8+tFqUi1cv1rrUks9ao5D09IyWLHuNDv3XUHrYHRgmeL5aN+kMqt2nicp+eXBVTWA8iwRiYlM372XFRcvMb19K0ITjRN0dnB43ALWYmzkS7n3SG2NnK5TgyfPIWu/MEu++TcYRHwvB+D06OcpcSl0al6Fzs2r8iA4ii0HLrPzyPVnXrq9DLUNjxFLH4jpej1rL18hv6srw+vWpkOF8pwJCiI2ORk3Jyf616iOq6MjPn53OXkzEC0ayngbF7HHgwKeGCuzzU69wsWolK8A9+NiLG8NkLV3mQxteOR8uCppgaLUTbLbugf8ePuB3NNQUXkCJQVQwHxX6w0i+w5ex+fQdRrVL0e+kp6PQyfj+jdl/6lbPAiOfvEgWXhVAyggbQglJCGBmfsP8MepU8Qlp0AeGF+7AV3KVmTepbPsve8nXQhFDaDYBaW6VUXFlty+cJ9Pe8wmXxFvdGkCgiDw6fyxnN53FZ/1ZzGYUWErO5QQQIHs3evi7Mj7b3eiQd0ypKSm43PoBguXHyU84r9ngKtGIPkpXxpEkU37L7F+ry8A7RpXYkzfJnz+ZleKF/Jm2bYzjyujSOXcVymAcjk0lDGrN9K4ZAnaVi33OHTSrHgpLoaFkJShtuDJDiV4VEVFxdhOp1mv+vR5syP/TF/FgTUnSHrksswASuMBDclfxIs7lwPNDp1kJScFUKKidfz0x17WbDrLiEFNGNS1Hh2aVKbfu/NNbvuuBlBMHU/DirOXKNTGja9bdeD1Og355fxJNt66ht6a9amd3Gmv9jugOlNFJSchiiJ7lh7h6OazDHq/BxUbVngcOtE4aCxet+aE9jsxscaQphPQtFtZxvdvxoBOdVm65TQb910iNf3lz2UpKohJ4U4lefNKaBjDVqwz/sQd5rTtgrezC9+dOsKNqAjLB1bb7+QopOvhkotxikt/4gWbJcxbfIT+I/7ku593kpCUyrsj2jK2f1OLx3NOMDz+IRWOuicDI0ofF4wPxMwfpiACvg9DmLhpC69v2oJ/bCw9qlRmfMMGDKhRA73BwF+nTvP9oSPG8TUaHNIF0vV64mNTnhkLoEu5iuRxdOSQ/z3iU80LEmWLTvtkEMUOJOucH/+Qi9hE1ycqochJaKLH440yFRWVFyPEJDzRiscSHOKTHy8uLMVSV4sinD53j12bLuIUp6e8uxcjejZi1Zwx/PxRP5rUKoNgmmIAJHWz1P60lY/N8bCpRCYmkW4woNVpiIpOIr9rHv7u3JuDQ8YxqkZd8mgtq3aTLXb0rlyuVYJjVa+qvIpEh8ZiiI7BXZNO/sJevDd3KP8c+JjWveshmCOX52Ctg631b3buNRgMXLluLNHs7KRFq9VQp0ZJSpf8rzWp3iDiFKfHKe7Jk09ZW94dOH2bL37fQVxCMsN7NaRM8XzPVCWTyrlS+lFq10rt2NOBQczeewStToOXswv/duvLseETeKtuI9wdnV4+gCnYwav29qkSPKqi8iojinDe5wr+Nx8y7OPeTJw1hAYda+JV0AONg4ZOI1sx7N3OAGycd1CSe1rjWGv8asm6NvhhDLN/3sWI1xfy8+970RtEBAFG9W5EPq88Jo0hhVOlcqCULpXSo+eCgxm2Yh2j120gKiWZOW27MKl+E0nGtrU77elN1ZcqKjmPpPhkFn2xjk+6fYchOoZ8hT1ZfOJz+k1si6Oz5c8mS11qqUctfTe8YdUZJr67lJv3Qpk8vA3rfh5Lt1bVTbrWWn9K4TypvSkVWp2Gy4Gh1CpUhB0DRzK3bReKuFmZkrGhL+XcG81tqBVPzMDaKiipqRns9rnKbp+rlCtTkMREY4KqbpUSfDC2A1sOXGbXUbUKiqWYm8o7FRDIqYBA3J2cqFW0CGkZevxjY4lI/K/kcIbBQB5HRxwdHDCIIlqdBq1GQ2qeDESggGse+lSsSobBwK57dx730paEV7wNjxIS8uopbZXcTOnKRejVtxU+606TpLP+77uUFVBAvjY8gcHRDBz1Fz271qZPz3r8PL0/D4Kj+ODHzQSFxpo8jpRt8mxVAUXKMcF2bXi2XL/Bths36VChPOOa1Gdmy/Y0LV6SN/ZslfQ+clRAAfv6Vm7Hql5VeVWJi0hgcvPPaNq9HqNmvsb030cxaFJHZo6dT1ig6dW1noecFVDgSfempetZuvokK9efZmCfBgzo1YB2rapy43YIR47fxvdyAPf9I0hL1z+6Vv/oWocnxtQIAveCoti0/xITBjSnT/va/G9p9puJagUUy0iMSmP46vW81bIxHzZpxcQ6DVl4+TyLr1wgIe3lJaZfSi6tgKKEdaqKyquGKIpcPHSdz/r9yNAPe9F9fDta9GlIyqP3p/mL5SU1KY39q05waMkBwNiCp1DxvLTr35DNCw6RkmTZcy0nVUAJfhhD8MMYnIAqlYowfkBzxvRtwiafyyzffpao2Je3WFMroLycYw/8OfbAnw4VynMuLhiAuoWLUsA1D/se3LVucBu7097td1RUVHIm2uQkHtwKYcJnfeg1uiVLftjBoS0XEC2s8JQTKqDc8gvlo0/WUbtGScaPbUXZ4v8dnMisvPki1PY72bP4vC8br17njSaNGFWvLj0qVGbSvu34KNSXcu+N5hbU4ImFWLuxde+BsayQE6BNNJCYnMqUkW15c3BLDpy+zZYDl7l0K9isMaUOoIBtyvPbclww/8GoS0vjhP9/bXQ0gvBEGf+T/gEMrl2LEl5exnENBhx0GjSCwIR6DSmQx41tfjc5F2re/y+TeUXb8Mi9OZaV3LZRJmSIz5wwVXn1MOgNvPlVf0Z92B2f9WfYtvgoQXctL0mcSdYkuxLa8Fji6bj4ZJavOcXqDWdo06IK7TtWIyzS+PuqU6U4weFxRESbHiV/kZ+dHR3I55GHtAw9UfEv7sVti8CILdvwSLk5ZhBF9t7xY+8dP+oWK0qyk9GHRd08eK9hMxZePs+t6Ehpbmbn4KccvpXbsfbyquo7FaVxcscFTu30pVX/RnQa046o0DgA8hX2JDos3urxpQqggPWbZBkZBlatP8Oq9Wdo2awiwwc24fUxrQkLj2PLrots3nGR5OQ0vL3yEBuXhFOc/vELvTQvh8frMV3So2ejCS851QCK+VwMCWHi2s3UKFyYt1s1ZkqDZuy8e5uENOsDUY/JZQEUuR36NKrrVF4lYsLi+OP9Zayas40+b3WiUr2y6PUGgvxC2bnwIJeO3nj8WUN0DI1HtWTUB93pNboVq3/by64Vx0lPs+zvS04KoADcvB3KyNcXMGJQUwZ0rkvfDrXY5HOZeeuOk5z68tPgagDl5fj4GTfNtGgY1b4ufSpV42JYCHPPHONYkL91g+eSAIpUqK5TUbEvoQ8i+LTHbOq2rc7474by4W8j6TOhDdP6/UJ6quXPpZwQQLl0NZB3pq5Aq9WAm0CjmqWZNLQV89Yd59iFey+9Xg2gPEt8aio/HD7Kct+LvNO0KRfDQgAo4uZOZHISGdYc5tdpc1X7ndziOzV4YiXWVkEB48Ps7SnLKV+2ID271KZju+o0rlWG3pP+IUNvwEEjmNybE6Q9ZZ2Jraug2DKAAuY9HA1PvdQ8FRjI2cAg3m7amKT0NM4GBhOemMi7zZsypHYtQuIT+PvYmcefX95zIDciI1hx/SIP4mKt/n08gQxVUORejGRNycv9ci+3BVBUXm0Cbz1kUosv6P1mR7oOaUaHAY0YWu8zUpMlOOH6CCmroFgbQAHzPZ2RYcDn0HV8Dl1HAPByYMbrXShSwJMDp2+zZtd5rt8NNXm8pxcfjloH3unbgja1y6NLTiMuKYWT1/zZfPwqsboXP+9sVQVF6QEUMLbMA+MLvtpFitCjQhVeq1qTIwH3mXfpnPUv+TKxc/BTDaCoqLwaiKLI4fWnObz+NACuxQrx645pRIXFse7P/ZzYdQmDGWu/7BBiEihSKj/peVyJDIuzaAwpN8mOnrjD0RN3qFiuEEMHNqF40bwkJ6fhoBGY/Hp7jp/2Y//hG4+zJa46Eb3egKaQM94exhYBlqyH1QCK6VwNC+PNdVsp5ulBgMH4Z+aHNp0JS9Kx8NJ5YqVqKQtqAEVFRcVqokNj+ffztTi5OCJoBFKfU81ky89buXn0KmNnDeXNr/rTb0IblszZycFN5yy+d04KoASHxDL7f7tYtuYkIwY1pUntMvy+8jAAWgcNGfqXb/SoARTT+GjrHk7XCOLt5k1Y3nMgpx8G8v2pI1x4tLlmMWoARUVFRUZ8D15jUtNPaTOwCWXrV3gcOilQxIvIUMvWmWB0qT09aolDMzIMOMUZiwa4ODsyZ1pfrtx+yF9rjuJ7I+il16sBlGd5GJ/Ax3v2Gn/iDn926oW3iws/nD7K7nt3LB/Yhq5UPWkZgqXlkXIrnu7FxcZ13rJqDEsDKJm4ODtSumR+rkREoBEEVs4ZzfW7IWw+cIXLZlZByUTKKiiZ2CIsYstxwfKHY4Pixfm8Q1sq5M9PVGIS7s5OuDk5cT0snDlHjnL8UcUUV0ct3/XoTOeyFXB0cOBIwH0WXfHlUMA9bPI3zc5teDKR+0GrlJd79t4o29ziz/OiKDawdhwPrxJivRaTTfrskZ0fSXJPFeXh5ZBfbOLS3fjvBT2oWLcsF84FAjDjn7FcP3ePvWtOk2hhb+rssCaAkomlAZSsWOPpIoU86duzHj261MY9j7Nx0bH6KL43X77oeBqP4h50a1KFaqULo3VwwM3FiWplCmMwiPy76wzrDl8iJc2057zU7rSVi6UOoQB4ubgwtHYtRtSvQyE3d65EhNJ340rrEvPPw47elcO1cvu1iFuCZK4D1XcqT7pOqWgdHegwtAUDp/WkRLlCBN+PYMPf+/HZcNaq02SfLxxP4w7VuXzSj81rznDq0E2r5mmNf5/2bubBiiKFvZj6dkdqVivBtl2XOHDkBnfuhaN/tBHWunklxo9sRb58bsz8cxfHLlhejleK9bCUbpTas1L71UEQmNu7K90rVCY5I52lV3yZd+kcMSkSesIOTrWnS8116KnO36trOxVJyAmusweCILywBUC9dtUZ880Q/K4E8tvHayW7rzVrXCndagqOWgfSM/QIBZ1ZNWc0R875sWybaS14MlGST6V0qVQedXJwYFCtmrzZtBHzLp1jwSXLQ07ZYmN3Su3Nm/2+VF2nIhmq75RBxZY1+D97dx0e1Zn2cfx74gQJ7u4a3N1dChQKpaXuW9+t7b5LdbXt1lsKFaBIcXcnJBB3D0kgIcTdk/P+MUlKKURmzmRmkvtzXblKIPOcBwrzyznnPvf92aHXOL3Hg83/PULyzXSD1jNFjtY0Q62trZg7YyBrHxxP6xaNOXoxiPe+OVqjNQzNTy0yT6vc1CozJ3frxl+mTaRP85Z43IzjI9fzeN2KN3xhI2XlvTJSq6yDupN3UnhyByfHdurYno9rcmMLDC9CcWxgx1OPTGLmtAE0crQn6kYy+8/4cfRiEFk5+n0zqHURiiUWoIB+b5BzevdiWo/uFJaUEHDrFq4x14lJT//D17Vq2JCVzoN4YMgg2jZqzIsnD3EgwrCLvVUyQRGKFKDo1FYBihSeCC3d64StoZMj7+37MwNH9SA/t4DTuz04+PNFYkINfFLnNloUoIDhRSiGZHSDBnbMnTGQZUuG88mms7j6XKNxQ3tU9baxANVgbW1FSUkpBY2taNW0IYO6tePZReNo3awRH2w+xUnPsD+MgKuMMbLTGGsaowDFztqahf360qFVEz5xdwFgUc++nIu9RmahxnklBShGo9WNOJC8E5Z1cdLKSmHcwuGsfGMJvQd35q0HvsLHJUyvtVq0deLzQ69hY2dDUnwa3fq1Jz05m0M73dm/1ZWcLP27V2h9k6xXjzY8tHIMY0b2IOFWBv5BcaSkZtOooT2L5g7B2tqK/Ue8+er7s+Q01L3miWXjCIy8iavPtRrvwZxumGm9Fmifr71atOC5saOZ31dXgPLc8QOcvx6t6THqawGKFJ4IrVhS1pkD+wZ2FDVoSN+hXXjmvWX89K9D+FzSL29vZ0kFKE5NGvD0o5OZPX0gJSUl7DvtJwUoZbTKUQcbG0pVldwGRSzt3Z+Z3Xry8VUXItJSNFnfmNmpZWZK4YnQkuSdeWjU1JGVry9kybOzUFHZt+E8v359ilwDzjHBMgpQ7GytWTx/KGklhRy5EIi1tRUd2zQlJr76Y0qlAOX3rBWFZQMH8NLEcbRp2Iinj+3j+LUIDXZHrRWgSOHJH0nhyR3KC0/KmUsBioO9LdMm92XB/CEM6NmO1/+zFxfvqIobVvqQLig6+rxB2lhZVetJahsrK2b16snxhHAKSkpY3d+ZXs1a8JO/NzGZ6XrsthqkAMVkjF2AIoUnQktVnbB1d+7M4mdmMnXlWOwd7Pi/td/hfiZI832YQxcUQzLaykr3ZF1BE2uefWAiy2YO4dA5f3Yc8+JmUma11rh9pF5BYys6tXJi73uPciMpnQc/2kpO/m+toycO6kZwbCLJGVVfFLSELijGKEAp165dYy6ueZKcokJ+DfZno58XN7L0bwV6V1KAojkpPBFastSLk/1H9yQkXHdj4sFX5mBnb8u+jedIS6re95r3PzudR95cwK9fn2bfhnOMmTmQkdP6M3RiHxwc7Th/zJ//vLObUj3PI0H7m2TOAzqyZsUY+vdtTwMHW6ysrMjIzGXvIW/2HPQiK1t3IbOBgy0/fPMY7Vs7ERadyKb9Vzh7NbzaBZrlzOmGmdZrgfb52qN5c56aMJIPL58jNT+PHk2bk5KXq80InnL1rABFCk+EViw160xt+IxBvPTV47Tp2ByfS2H89K9DhPoYPrLTkgpQ2rV14qGVY8sKUEpZ88bP3LiVXqM1zClPzbEABeDBIYN5ffIEGtrasTcsiP+5X+a6VuelZl6AIoUnQkuSd+aldacWrP37cmasGs+tG6k8PvF9SooN7zxsCQUo5WYvHsIbT8zg2MUgNu5xrfZ1YJAClDs1sLVh9ZDB/BTmRUFJCYNbt+V6ZgaphnbcrIWclMKTP7Kp+kvqN+vbRgwYcgKgzxyx2+UXFHHkhD9HTvjTs3troqKToLEVTywby5jB3dhz0peTrsHk16Adc/mcMdCuCEXr2dXGXhd+m1EG1X+TrG77/uLSUo6E6p6csMGKLg5NeXDAENYOGsbZmCh+9Pfi0g3DT2x/pxZmZ9/J1LPOzGW+dkKO7huj2h7BI4QxRPnF8ulzG9nw1x3MWjMB38u6WYvTl42keesmHNvmSlZ6rsHHMWRGdjlDZmXDbxkNNc/p0rKCEbuMEs6fDKJto4YsmzmE5bOHct49nG1HPAkIr7xbTHnRiZWiYJ9VimJXTGxiGg0d7GjdtBHXEnSV822aNeLT5xYD4Bd1k90X/Dh8Jfie62qdncbIYq1na9/u5s0sFvy0icdGDGfNgCE8PHAoRyLD+ND1HAk52docpBYz1xRZay75KkR9E3Tltyd8WrZryswVo1ny+GRO7brKru/OcDM6udLXT1kynPycQo7+cpmMlGyOb3fjyqlAug/owKhp/SksKEZNzkBp3qTSsQSVMSR773Zu7Bd4g7/8fRctmjeiZ/fWpKZmk51bwM07Zofn5Rfx0JPfM2NKf1avHMMHLy0k9mYqf//yCCHXblV7D4bO3AZtc1HrjNU6XyNTU3njwHHdJ43gP1Pn0Lt5S37y92KDr4c2BSi1kKl52fa1lqPpOQ0kP4UwY56n/Hl80OvMf2Iaq95YzP8OvsqJX6/w6WtbDVrXkPNbrbO1KjcTMvj3Z8fYvMOVqRP7VhSdTB7Rk4CIm9XqgGJOeaplltpkK5pl6C8+vhwJDeXJUSN5eNgQFvXsyyfuLnzjfdXwxY2Ynaa+1iuEMG+J11P4zxPfseeLY3Qd0bui6GTk1P54nAvW+zzT0BytrQwFcDsbyq8tmrJ04VBmje/H/jN+/LjXjdSMqq+XG5qfWmSebbb5ZGZeUTEb3T11+8KKz1bPp7mDI196ufGTnxeFpSX6LVwLOSn+SPuWF3WYdWbe7wpR9GGXUVTxoa+IqERKS1XsMkq4EZGMrbUVbz81iwNfPs0rD0+lS/vmNV7TPqv0d4UohrLN/u1DS8Zat5xNtvK7QhSt/efCJSZ/t4GvXN1wbt2WLQvv599TZhvnYNk2v33Ukrxs+4oPU0jPaVBxk8yUEnIaVxShCGHpslKz2f35MfLjEylNTcN5bE8ee3sRm93f5aV/P0C3fu01OY6SllVxcqEvLXNaHxFRiXz48WFWPvYd23dfZcTALqyYM6zar1fK4qddqyYopZCTU4C97W/v4RMHdQfgSnAMigLvPjKby1/8iQ8fn4tz93b3XFfr3DRGDpfnr9YZHJKUzF+OHmfK+o1scPdkRLsO5Bbp/v82sdMwq2oxb02Rs+aSr0LUR58+8Q1PDPkLJ7dcZMbyUXx/7h0Wrp14z68fN8eZrn3bcSsuFcdGDrTt3AKA9OQsvM6HsPGjA/zySdk86tRMg/PXkOy927lxSmo2VzyiCI9K/EPRSbni4lKOnQrgkac38rcP95GakUtCsu7psg6tnXCwr/77sRbnwVqeo2qdscbIVptsK945cpKzMVE8N2w0F9c8yWujxuNk76DNAYycqbWZo5KfQpi3osJi9n19gkcGvMbP7+0mKjAOAEVRaNe1pUFrG5KvWmRrTdxMyGDrzivYZZTgVGjN356dy+7/Pc5La6bQ3MmxWmtomaeG0modLTM0LS+ff5+/yLT1P7DDz5+odN3DJY42tjRz0CAnjJidprzOK4Qwf5G+MZzeeJLS1DSGTuzDe5ue5stjf2bk1P4GratvjhqaoTWRkZnHtz+cY/UT33P0pD9Lpg/mn68urtEaWmSnoa83t8xUgad37cf95g3eHjuZU6seZV733oYtWsv3Ses7GbVzhztH7VRFi1E8ho7hARjUvwOL5w1l8oTeXPCI5G9fHAKggb0teQX63UDTehRPfRnDU1121tbM69ubG0WZuMVfp03DRjw3dDTbg/0ITkkyzkHr2Rgec3nCTKsOKDJqR2jJkBaVXfp3YPHTM5m+ejwOjvbsXn+WDe/v03aDWP4YngYOtjg62nOzpPrvRa2bN+a9P81nUK/27Dvjy+dbzlNQWIyiwDfvraJ100a8teEI8cmZDOvdgWG9OjBxUHdaOjXk24Ou/HjMvcpjGCM3LWUMj7WiUKKqFDcq5fDyhygsLeEHP0+ORoVXu5tZtcgIHr3JqB2hpbrWjrlZGyeWPDeL88cDiQ65ezetB/40k6ET+9DLuTNqqUqYXyyHN13i0hHfah3D0Ow1Ze4WOlmz/t1VdGrbjH2nfdlz0pektJpdRdPqHNgcxwaA9tnaq0ULnp80mnk9+rDu0hk2BXhrun5dHr8jo3aEVupa1pmDSQ9N5c2v1nJunydb/3ec+GjDr9FZ0gie9m2b8vADY5k5bQDFxSUcOh/Az/uukFyNDijl6uIIHmOcnz43eTRPDRnJ5gBvNvh5kpJneFdZY2dnTXJTRu0ILUnemT9FUZi8fDRr191P+66tCLgayU//OkTg1SiD19Y3R2tz/E6H9s1waNOA4KhbNG5oz/JZQ9l1wpusnOq9b8r4nT8a36Uzb06fTL8WrVhzcKd20yM0zMqYh9+S65h3kMKTO9S08KScFgUoYHgRSlMnRxzsbYgtyKFrh+b89OEazrlHcPh8IJ6BsTWefQ3aF6CA5dz0up0xi1AAZvbqyf8WzMPexgafWzfZHuzHwYgQcor0745zT1KAYhKGFqBI4YnQkhYnbI2aOjJn7WSiolLxcQmjWesmDB7Xi0uHfSgu0rMF3l2YQwEKGJ7RhU7Wv/vcSlEqcrlJIwfmTOjH4mnOdG3fgktekXy25RzxibqnvXt3ac2PH67hpGsIb206VrGGg50NXds0p0vbZkTdTCH8RjKKAtWNe62z05IKUB4Y7MzakUPp3rQ58dmZbPL3YVuwHxlajAsoJwUoNSaFJ0JLdf3ipFXzZn/4uUZNHSktKcXGxpqJC4aw9KmpNG3ZmJ1fn2L7FyervbalFqAM7NeBFStHMXF4T0pLSznlFsovhzyIiK3ZTUMpQKmZXi1bEJOWTm6DIu7vO5Dhbduz3sedqPQ0bQ5QBwtQpPBEaKWuZ50pOLVqzP0vz2fh0zOwtbPhzB53tn12nJsxKQavbUkFKB3aNeXBFWOYMaU/D7+1idibaVhbW1FSUv2CfSlAqVz35s14YdIYFvTsS0FxMb8E+fKdjztJudUv8rknI2ZndTNTCk+EliTvLIe1jTVz1k7iwXeWUlpSymMT3tfsOrElFKAATJ4zgPf+NJ+c3AJ2nvBm+1EvMrKqdw1NClB+z0pRmNmrJ4fjQwFY3KsfMRlp+CQmGLawRjkphSd/JL1lNHJ7+yZDTgRub+ekz5taetn8MDug1KGQIyf8mT6lH7PH9yMxJYujl4LYetiDzOzq31S5vd3T3d707G2t6dmhJS2aNCQzJ5+AawlVPjWs9Qzr29fUet1yWs/JvtPJ8AjGfbOexf37sWLIQP45ZTZvj53M2M3faV98cntbqVq6KWbK2aC3tzc2ZRHK7eN3tOqCIoQpZafnsuuzoxWfT31qKk/+bQmPv7OIQz9f4ugvl8lMM/yCiSHzPcsZMi+7nL4zP397/W8nWYVO1pSqKu1aNWHi8B4smjKIbh1bkldQxJdbz3PsUhBpt31vMX2MrqWgjbUV4zp1ICUjh4jsDPILiwm5nkjI9cSKr61JjanWeWyMfDdG/paoKr/4+LLVx5cp3bvxyOhhvDl2Ein5uewMCdDsOLWZt6bI2fJ8NZcCTyHqm9JU3U392wtQstN/e1L28GYXvC+G8c53j/LQ6/MIuBpFwJXIaq1taPYaen6sb+YGBMcRsG4v7do6sXThcObPGoRfWDwRsUnYWFuhqiolpVXniaEzt8tplYtan+tqna3hySll61rR1roRS3r1Y0XfQRy/Fs633lfxNfSiYC3kaW3mqIzfEcK8ZSRlseGd7ez67AgrXpnPgqem02dIF56e9g+D1zYkXw05p9UnV+NupvPvz47x9cazZGcXgJM1770wHwd7G7YccMc75EaVa2iRp1pnqaHraJmhUalpvLrvKF80c+PZMaN4ZNAwujo144mjew1euyI7jZCbprzGK4QwfyXFJRzeeJZTW13o0LMNxUUl2NpZ8/S6Zez/4TzXI27pvba+Oapvhup7Xnr+WCCPhiSy5qFxPLxoNCvmDGPPSV++3n6hyuu2hmanFnlnTplZqqocDwvHBiusFYVXR46ni1NTjkeF87G7C2GpyfotbMScrO+k8MQItLi5BYbf4Eq4lcH/vjnFV9+fZdzoHsyeM4jlM4ew+cBVALp3bEFSWna1Wz3B3YtQJjn34K9rpmNva0N2XiE21lb4RsWz46wPbkGVd1kxVrGIMYtQbp9VpnURSkZ+Ppu8vNnk5c2Qdu1wbte2oujkvYnTiUxLZW9YEJmFGn5jX8tvsLfPBTVlEYqpb5KVF6FIAYqoS3b/ay/RXhEsfXk+j7yxgFUvzeLUTne+fPtXTda/fbanqW6EgeH5rCgwsFVLZk7pz5RJfWnZrBFxiel8s+Mi+8/4/SGX7Wytsbe3xS8sjlGDujB5RE+Cr91i+xFPzlwJA7S7MQba5KYxctgY+asCZ6OucTbqGn1ateRaaho0gMechzGxY1c2+nlq18axlvJWClCEqH/uVoACYGWlEB+dxJEtLjz3wXJmrxxT7cKTcqYu/tQ3c28mZPDV92f4ccsliktKoQEsnDKQhxaNYudxbw6c9Scnr7DKdap6CKO6tCzM1HItY2TrN1eussPfn7XDhrJm2BDmdu/Nj35evOtyRpP1jZ2ncjNNCFEuPTGT9W9tY9dnR2jVsQUA9g62PPLGAvZuPE/ijVS917akApTs7IKy15YQHnyT++8bydf/txL/8Hi2HHTnomeE0W+iQd0uQLmWlsZfjh7nS1c3bK10nVI7Nm7CM0NH8a33VW5kZeq/uBSgCCFMpCCvkCj/6wB0Hd6dqfcNZ87qsZzZ486WT46ZJEdrswAlKjqJ997fz8+dWrBm5Ri6tG9ekZeODezIreJ8VApQ/qhEVVn042bWDh/GE6NGcKxbT/aHB/Px1Uv6Z6UUoGhOCk/uVFyKkpalSVt/c+mCUlRcwnmXMM67hNGggR1FeYXgZM1fn5lD944tuegZyZGLgVz1i67W01/l7LNKURS4lZbFv7afBaBZY0faNW/CxEHd+M/TC/l6vwu/nK7enGdjF6FYWhcUn5s38bl5ExussLO2xrlVWx4eOJS3xk7iQHgImwJ8CEjWvzr0D+ppFxRT3yQzRQGKUqL+7n1FCK2oqorHCT88TvjRuW97ljw7C4fb8rTv0C6E+sSixZg/U98IA/3yeUC/9iyaM4SJ43rhYG+Lf1AcX60/g4d3NKk2uvfe20fwABQWlfDjXjeysvOxtbFmyqherJ4/gvf/tID+PTz4ZvtFzZ7MhvrbBSU0KblsbSuKckoZ2KoNWxbeT2hqMj/4ebIvLJiCEg3yUQpQao3knagvbO1sKCospjQ1DUVRUJo1BaC07Nwu3P86OZn5tOnUnIZNGpCTWfN/o6bOXX3Picsv5tkVws3IFBKSM3lxzRQeXzaOw+cD2Hncmxu30qu1lpY3zcB8bpyV0zJbU3Pz+PTSZdZfcWfl4EGE5ugytplDAyZ07MyRyDBKDP1+UApQAMk6IWpDakIGqQllI1An92P+QxNYsHYiJ3a4sf2LkyTF6z9WzJIKUAC27rzCrv2ezJs5kBXLR/GvVxfzxS/n2XrYo1qv16Kgsy4XoMSm6/6e2WDFiI4duL/vQFb2HcSesCC+9rpCTGa6/otbcAGKZJ0Qli/UM4pHBrzGilfns+jpmUxZPJyjWy+z8cMDFObr/+/bEgpQYq6n8OF/D2NlpWBXqtKyV3M2//Nhjl4MYsvBqyQkV35fSApQfi+nqIiv3a7wi48vT40awUPDhrLR19OwIk0wiwKUupJ3UnhyD1o8VX07c+mCkld+4S2jhE8+O86c6QOZPrU/M8b2ITktm+93XebAWf9qr6eqEOobRyhxgO7Nz9bGmhMeofztoZk8s2gcSRk5nPAI++33YGONg50Nmbn3/mbU0kbxGLMLCkBhSQnLN22jf+tWPDDYmcUD+rGi3yBeP3OUXaGBmh+vPnVBMbcxPNIBRdQVsSHxfP7STxWfdxrRh08PvMqNqEQO/HCBU7uukleDjlv3ouWNMDBuF5QmjR14+ZmZ9OzemltJmWze7sr2PVcrqt3tyr6u0Mn6D68tnwNaUFTMcZdgwmMS+deri1k0dRDn3SPwD48HtHsyG+pvAQrANl8/dgcEMr9vHx4ZOZR/TZnNtM7defr4fu0OUksFn6bIWHMrQBGirhu/eAQpN9PwvxSKqqqoqWlYWSlYNW9GaalKizZONGziAAp6FZ3cTsvuY7XdeczLNxYv31h69WjD/YuHc9+MwfTr3pan1m2r0TrmOobHHAtQcoqK+MHDS7cuVtzn3I//Gz+NP49OZ72PBztDAgwv6pQCFCFELfI9H8wjA19j5esLmfvIFGauGM3x7W6sf3cPRYUlVS9wD1oUoEDt3EArLCxm32EfDh71ZcrEvrhF6UbujBzYmR6dW3HgjB+51biJaA430rRcR+trw4dCQvG4EccTo0bwwOBBLO8zgF2hgbxx7rhhC1twAYoQwrJlJGfx/dvb2fvlcVa/sZieQ7pVFJ1YWVtRWlJaxQr3ZgkFKOUPhqgphZw6G8SimYNYPHUQRy4GsWn/FeISMyp9vTnkpjkVoGTk5/OfC5f49oo7WQUF0Eg3NSK/uJhvvK+Slq/ntQ8zKECxdIY/FlsPKGlZFR+Gss7Mq/gwhF1GUcWHvsIibvH5d6dZtuYr/vrBXoIiEygs0v1jcmrcgPmTB+BgX/3aJCtFwT6rFKu0IkJ949h+1ocGdrYM69Wh4tcBZo7ozZmPn+XnNx9g7ewRtGra8J5r2mb/9qElY6xZziZbqfjQWlBiEv938jTjv17Pe6fPcjomCoDZ3Xrx5phJdGzspO0Bs21+f2OsFuRl2//uJlltSs9pYPJ52wk5jSuKUISoS+J9Ivjo4a/ISsvhuQ+Ws/nquzzxtyU4Nb93BtSEVlltaEZXls95eUUcO+VPUnIWbVo1Yfb0gbzy3CymTuhDu7ZOlMUkdhklFR93oygQdSOFfWf8adjAntkT+t316+yzSn9XiKIvrbPYGNlujOwtLClhb2AQi3/6hdXbfuVbH92owtaODfl42lwGtmyj2bFqK29rO2PNIVeFqOtsbK1xntiXfx99i4feuY/OfdsDuotaxcmpONmWsuiRSQCc3++l6bENzV1Dz431PR8Oj7zFR58cYcUj3/LJZ7qbN06NG7D+3VXMmzQAO9s/FoDejdY5q9U6WuWrMc5pN1/24Zm9+0nJy+WDSTO4tOZJnh4yUpvFjZylpjxPFUKYl+T4NL56dROPOv+ZE5su0LF764qiE3sH/R4ULKdVttaUPplaUqpy+nwwOdezsMsoYdyQ7ry0Zgp7v3iKp+4fR7Nq3sQzNE+1zlEtaJWhCdnZfHDmHFPWb+RHTy9yi34by9C2oYF3/YyYm5KZQojKJMen8flLP/HatHcpTU2jcVNHfnT5Px54cRYNGhr23qFvjhqanzXJ0JTUbD7+8gSrHl/PgaO+zB7fjy3/Wksjx+r93s0hN7XKTC3yMqugoGwtKxqU2vK483AuPPgEL40YSyNbuypeXQkT3ButK+RPrYa07IRiLqN4iotLuegazkXXcN16wKQpPXn7qVm88vA0TrmGcPBcAIERNytdp/SOVrl5KXkUFZfQ2NGBRg52ZOfrvjke068LAPY2Njw2dxR/WjKB6IRUTnqGccA1iJspd2+JZGldUMB4T2JnFxayyUs3wsgGKwa0bM2Tg0fw1JCRnImJYkugD+djr6HZUWUMT627vfjE3LugKIqyHJgMDAEGA42BX1RVXXOXr+0KXKtkuR2qqj5ghG0KEyspLuH87iuc332FviN7sOT5WcxdNZZfvzwJQKOmjmSn52pyLHPtglJUXMLug17sPuiF84CO3LdgGHNmDGTBbGeCw25y1fMa3n6xhITdpLCoBFsbaygrPilqal3RGaX8v2kZOZSWlmJvZ4O9nQ0FhXd/f5YuKIa7ekP3FJ8NVji3bsvsbr1Y1mcAV+Kv84OfJyejI//wfZBe6ugYHnPIVS1I3glzVFJcyultl2nXrTVL/zSH4TMG4XnKn1vXU1CApX+aQ+e+7YkJjuP4d8fBUfeG26ZTc0ZM7c+Z3e4GdyCz1DE8qWk5pKblYAd0aOGIo70tf3tmDs+vmsieU77sOelLWmbV35tolbNaZqKWa2mZqypwKiKSUxGRjO7UkafHjWRE2w58hzsAjja25BYb2FpYOqAYRLJOiOpLupHC5y/9hJWVousw1rcL351+k9O7Pfj161OkJerf8t3QbDX0CW6o+TXmb746zdmTgTywagxrF49h9fwR/LTvCj/tu1Kt15vDk9xargPaZWhyTi7/PHdBtyZWDOzemt33rWZfeDCfe7ia7QgecyVZJ4R5KO9wYleQR4T/ddb+eT5LHpvM9i9PcnjzJYoK9H9fqu0OKFDzLihJyVl89u0pNu9wxXlAR7LLJkSsXTyKCx6RXItLqfT15pCb5tQBBeCt4yfY6OHBi5PH8crI8awdOJQXTx3m0o0Y/Re14Jw0Vd5J4YkBtLjAVs5cRvGUO77fh/jwJObPcmbWxH4snuZMZGwSj//f1nve2LqdYwM7Rg7sjI2NFWlJ2RQl5WMPOLR1ZMKgbniF3+C/v56nUQM7Bvdoz7BeHXh49gjumzCIv/14DPfQ6/dc21jFIpY8iufzs67s9PBnpbMzK4cMYkbXZZy8FsGTx/Zpfqz6OIbH1DfKLKADyl/RBVc2cAPoW43X+AL77vLzAdptS5irEPdI/vnINzRo5EBedj5WzZvx3k9PYWtnw76N57lw0MugNsXltMppY4wE8Au8gV/gDRQFZk0dwOL5Q3lk9XiGDOrEO+/vpbCohPmzBxESnkBIWAK26bo/j5JmNpSUqtjaWNPMyRErKyuKi0uqlc2g/XgAML8bZOWMlb3noq4x4Zv1rHAeyMPDh/LdnCVEpqUwb+cmCkoM/3sLSAGK+ZK8E2ZHVVUCXcN4a+G/mXL/GJa/PI8H/rwQK+vf3ucv7fPg0IbTujbG+WkAjH9qKk/+bQmPvbmQkzuvcPDnS8RFJRq0F0sewxMRlchjz/7IsMGdWbZ8JE8sG8dDC0ey5E/fk55V/fcsLXLW3AtQQJtcvXL9Bld23MDO2hoaQJcmTTl8/8PsDAnge1934rMNLL6XAhR9SdYJUUPlrfNL09K5tM+dhWsnMvfBsRzZcpmdX58iLUn/9zNTFaCAfpkaEpbAunf30alDcx5YNor0suPb2ljTsU3TKm+igXY30sA8bqaB9g9HxN/M4gcPL9YMHcziXv3YHRrIF55u3MiqfExDpSz4xpoeJOuEMCNJcam8u+y/9BnenUc+WMXTf7+P+56YwnOz/qXZqFh9C1DA+GN4UtNyOHcpFDugZYtGPLRoNM+snMiZK2H8uNeNiNikSl8vBSi/F5GSyot7DrG+TRtemDSGiDTd9x6tHBuSlp9Hcame3WIsMydNkndSeKKButgFBcA/KA7/oDg+/+40Uyf2pWuf1ve8sWWlKBVP+rZt2ZjV80eycMogAiNuctottOLrRnVsT+MG9nhHxBF2Q/eG6RUex8HLQXRp24xmjRoQHFv9C57GLkKxpC4oN7Oy+Z/LZb5ydWNW715kW+sugDW0teWdsVPYGuRHQPIt7Q5Yj7qg1IEbZcb2CrrgikBXQXm2Gq/xUVV1nTE3JcxfXnY+AGpaOsd/OMt9L87l9f+t4bG3F3F4swtHtriQnmx4xx+tctrQjL5bNqsqHD8TyPEzgbRs0YiunVuSm1eIYwM7xo3qyRMPT2LXfk/Ou4QSez0F0oqxBsaM7MKscf0oKCzGK+hGjfeiVQEKGOcGmVbrldM6e7MLC/nBw4ufPb2Z1asnPTu0qCg6Wdq7PxeuR5Ocp0EHHylAMTeSd8KsndvpxrmdbnTu257+o3tRkF9ISnwawVcjKSr4fReJXf/cg/8pHxY9M5N5a8az+LHJXDkZwLuPb0DVoIOTKW+Ugf4FKF6+sXj5xtKxfTOGD+lSUXTy2H1jCItJwsU7kur88WhZgALm9fQ2aJurhSUl2GRbUaqoHA0NY82AwawZMJh94cF8632VyPRUww4gBSg1JVknhJ7SbmXwv+d/YMd/D7H6jUUsemQic1aNZe2YdWQZ2N3T0HPa2i5AuR6Xyn8+P6Z7PTD3vkG8+cQszntEsGn/FYIiE6pcw1yKOc2xACU1L49/nb/ARg8Pnh41itVDnJnetQfjNn1HYamBD0NY5o21mpKsE8IMhXpG8dbcDxk8uR9DZg2tKDoZNKYnQR5RlBTrP17GkPNTQ7uI1SQ/k1OyeeDRb1m+eATLFg9n2ujenHcP5+Ofz5CUWvlsGylA+b2AW7d4Zud+3SeN4NPp8+jYuAmful/mYESI/p2rLSsnTZJ3UniisbpYhJKbV8jhE35wQneyoFvn93OvS1UVRwdbJgzvwUMLR9G9Y0siYpP4bMu5351MTB3VC4CCjEK62Dcir6CIRApIzswhOTOn5r+521jaKB5jPYldVFrK4RBdsY8NVgzu2I4lvfuzesBgvG/FsynAhyORodo9lQ0m64IiBSjmQVXVisBSFG3nwIv6QVVVjv50jqM/nWPYtAHc9/ICHnptLmlJmRz95TI2ttaUFJeaxY2wcsa4IZackk1yii54CgqL2bnfg9W2o1m5dCTjR/ckIPgG6Rl52NvbsGT+UBo42HHwqA9X/KIr1lgyzRmPwFhu3Eqv1j5kDI9hSlSVo2HhEBaODVa0bOPIf6bOobi0lL3hQWzw9ayorDeIFKCYBck7YSliQ+KJDYmv+Pxef19DPaL4zxPf8f3b25i7djIN2zSvyNrpy0Zy9XSgZjfKDC1Agdq7WQZwIz6NG/Fp2AF2djbMmzyAJ1s3JSY+le1HPDl6MYiCoqrfk7XuNFaXC1BuZmXx1rETfO7iyhMjR7DCeSALevRh9KZvySzUIJekAKVaJOuEMNzNa4l8/MwGtv37AEOnDqjI0gVrJ+BzKYwbkdp0GLOEApRyLqdC+LFBQ5YtHsbkEQ/iERjLpv1XcA+IrfK1UoByb8k5uXx49hwb3D3o06olhaUlKMBzw0azKzSQWzmV36SslGXdWKsRyTohzJvv+WB8zwcD0G5wD/6x/XmSb6az85vTnNjhZpIRPFB7BSiZWfn8sOUSv+51Z9mi4cyaOZCcvEIAHOxtyK/i9y8FKHdbx4qfrnjx6uTxfDZjPs8OHcV/r17iVHSk/otmm395hanyzvz/ZCyYMUbxgDZFKIaO4rHLKClbx1pXcDKsB0umOzO4T0fSs/LYdOAKP+xxo6j4t+KGhg3saNOiCUXFJTyzcgJLpg3CK/gGe0/5EnLtliZPXYN0QbnT1Rs3GPf1dywdOIAHhznz6fR5/HXcFGZu/5HUfI1v9NSTMTzlN8rAcm6WmaH2iqI8DbQAUgBXVVX9TLwnYQa8zgTidSaQjr3aknQjFRo0ZO7qcdz35BQOb3bh2DZXg9ssgvkVoMDvs7mkpBR3r2jcvaKZNK43SxcOY+7MQdhYW2NtbUVmVh4Hj7mzadtl8nMLsQOaNXXk1UemYWtjjavPNQ6e8+eSV9Tvsrgy9XEMj9a5m3wrl9k//MSjI4azbGB/HujnzNmYKP5+6TSxmQa0PS5XS93GajtfLa0ApYYk74TJVVW4mZ6Yybb/HKz4vOOw3rz+vzUU5hdxdp8nO78+Rdy1ytv7VsXUY3gMeRijsLCYhx7/nskT+rDi/lG88cRMnloxnnc+O4h3cPW6jkkBSvXdzMri/TNn+crNjWHt21cUnbw5ZhKX42K5cD3asANIAYoxSNYJcRfxUYnEl42xa9KiEY+/vQgHR3vcTvqz44uThHjHGLS+FjfPoHae4M7MyuenrS7s2OvOwjmDWbFsJM8+MBH3v/5S7TWkAOXebmVncys7GxusGNC6NS+PGMeLw8eyNcjP8PF1dbgApYYk64QwgZu+kfz9/k9Y9dZSXvjwfla/OItd353hyGYXCvKLql7gHiyhACU7p4Cft11m8w5XSktVlKbWbHjvQeITM9hy8Cp+YfGVvr6uFaAYmpXnoq5xPuoac/v05uVJ49gw9z7ePHec7cH+hm2u7jE476Tw5A629ja069qSm9HJmq2pZRcUMPwmF2gziqdRI3vGDuvBkvlD6d+nPdm5+Xy/y4XDFwIr2j4pChWtiIuLS9mw+zLXE9Lo2r4Fs8b3Zdro3kwY1p0Nu13Ze8q3Ym2ti1AsrQuK1jfCsgsL2eTlzSYvb8Z27sTonp0qik5eHjGO1Pw89ocHk1GQr9EBZQyPqNLMso8KiqKcA9aqqlr1Iy+izrsRXtYtK6+QuMBokuLTeOKvi3nwlTkc3+7Gvo3nuHXdwNbrmM8YHrj3CciFy2FcuByGU5MG9OvTjqzsfBITM0nLyKX4tjaTaem5rHz0OxbOGcyCuc589PIi0rPyePt/B6p9Ywzq1xgeY3Qfi05L5+8nT/PZpcusHjKYZc4DyCzQZVPf5i1JzsuVMTz3cHthZx0ieScszg2vMJ4a8RaLnp7BzIcmMnPFKC4e8mH9e3tJvZVp8PqWOIanpFTlzIUQzlwIYfDATiy7fySxN9MAcO7TgUYN7HDzja6yXa9Wnca0LkDRYi3Q9nw2NTePUxGR2GBFC0dHlvbuzzNDRxGemsIPfp7sDQ8iv9iADJQCFC1J1glRhcyUbB7u9yoLnpzO4udm8emBV/G6EMpX7+wkPlqb4s7azlV98jQvr5Bf97qz96AXLVrogqdJQwc2vL+aIxcC2Xfar2LM3b1IAUrlAhMTmbnxR54fM6ZifN3+8GDeczlrWAcxKUCRrBPCRNyP++F+3A/niX158K/LefSNhVw46E1BguEPeFlCAUppqS4bHLPh3Llgli8ZwXfrVuEfFs8vh9256BFZ6XloXSlA0SIrVeBIaBjHw8JZMqA/B2+EADCxYxdsrKw5Gxul/wbrDoPzTgpP7tCijRM/XPwbvpfDOb7dDZejvhQaUDl3p7owikdRYPzonixfPILBAzuRnJrN+p/Os++ID3llLZ+UptaoKr+bf11QVIxX0HUAklKzcQ+I4cyVMN5/YT5rF4/GK+g6MfG6m4hatv4Hy+uCYqwxPACusddxjb2ODVYowMROXRjetgPvjJ3M8Wvh7Aj253JcLJodVcbwiN/LBd4H9gHlSe4MrAOmAqcVRRmiqqphs7dEneJxwg+PE370GNyFpS/MZsHDE+g5qCN/Xva5pscpz+j2Q7qy7OEJnD3ii79ndI3XMVYXlIzMPNzcK/8GOCU1m5+2urBp+2VGDO3KnOkDibqhG/UyYVh32rVy4rhLMJnZVRcaGmMMD9SfLiipeXl86erGl65uup9oBB9NnsXAVm04HhXOlkAfrtysfkHQPdXBApQ6QvJOWLSY4Di+ePlnNn+0l6UvzGHKynHk5+j+/bfu0IzkhAxKS/SfsQ2WO4bHN+A6vgG681o7YOWcYUwb3ZubSRnsP+PHgbMBpGVWXWCo5Y0zMK8uKFpnakpuLpO/3cC8vn14ZNRQ/jFlFn8ZM5HHjuzB+9ZNwxavpQIUqJMZKlknRA1kJGfxyz/2sfvzo8x/fCoLnp5JTlleNG7qaDbj7fS9gQbVz9Si4hISbmVgB7RwtCXuVjpPr5jAI0vGcNI1hJ3HvAmLqXwkkRbnq1reTDN0HdAuP29kZPLW8RN8ftmVx0YMZ1z3zuQU6a7ZN3doYFgH7PpXgCJZJ4SZ8LsYgt/sD2jTpSUpWbruzm98+TCh3jEc+eWyQfdxLaEApai4hE3bXfl1rwdzZgzk/mUj+ecri3nr0wOccw+v8vWG5mZdKkApUVV2BwTq1sOKxwePYErnbvglJvC5p6thI3gsl2Z5p1TVdre+aeHQRv3vG18y57GptOvSkuyMXPb/cIEtnxw12jG16IJyO0OKUG53rze8Vi0a8dm/VtGuTVOuxSSx95A3p88HU1hU/Lsnr39bx/qu61gpCqWqyrMrJ7Bm4Si2Hvbgq20Xftcl5U5adUIpZ4yxOcZYs5wxRvEA9GvdiuUDB7J4QF+aOjTgP1cu8pXXFaMcC6j1kxNTXeDTugjFbfa/PFVVHWHoOk6O7dSxPR+v1tce9/8wBri9BdR6VVXX3+vrFUWZApwFflFVdU1196Qoig1wCRgNvKyq6mfVfa3Qj5N1C3WMw3xTb0MvLdo1o0mLRsTEZ+PUohFvf/MIB3++xOVjfgbfBAN44E8zeej1eQDkZOVz8WQgR3e5Ex5UeQvDyhiazYaOyAN45bU5LJnmTGFRMefcIzh41h/PoNh7Zu69aJXFWuelMfJX69zt3rwZqwY7s3TQAJzsHQhPTeF/Hpc5HBmq3UFqKWONla0hS9dpknUgeScsO+vMjZW1FaUlpVg1b8bXJ9/AsZEDR7a4cHybKxmp2l1nN/Tc2BR5a2NjxYQxvVi4cAgjBnahqLiE7Uc8+Xr7xRqvpUXGapWHWueqlpk6vEN7HhjhzDvnT5JbXMS4Drobar6JCdocwMhZ2qBRgWZ5J1knJOssl6IoqKqKVfNmfLr/FRQrhW2fn+DKyQBN1jdlpuqTp507Nue+BcOYM3Mgjg52LH3pe24m1azTmqE5am4ZqlV2ll+Dt3ay4tKaJ4lIS+FrrytcvGHYuCeg0syMefgtyTqhGck7cSeHhva8u/fPDBnfm5RbGfz61SmObXPVpJGAIRmqb37WNDutrBTGj+7JudBoSkpKuW/GYJo7ObL7hE+V3cPKmTo3tchLLbLSxsqKJf378ey40XRxakpg0i0+cj2PS1zVDT5invuzXMe8g3Q8uUNxUTHb/3uQHR8fwnliX2avnVTxa1ZWCjPuH43LUV9yMrW7oWysUTygTSeUO9/w0jPy2LzdlYVzBtOvT3uee3wqc2cOwtMnmqCQm1yLTSYpKZOSshZQdhm66kMrK4X8297Iyts/XU9Ip1RVadqkAQ72NuQX3PsbVkvohFIbo3hA2wt3wYlJvH/mLP86f4EZPXvglal7amxSp648MXgEvwb7czI6goKSEm0OKF1QLEmyVsFZGVVVixVF2YAuwCYBcsIm7inlZhopZe3tW3drRst2TXnn20dJiE1h/w/nObHDjdxs/f+9j5o+gML8IrZ9foJ2XVoycnJf5iwdTkpiFpdPB/HLd2fJrOHTaFo9lQ36F6F8+vExDuzxZP4sZ2ZOH8CscX057xHBm5/sr9E6Wo3iqY9dUKJS0/jw7Hk+vujC/L59WDXMGSd7XUY1srWjq1MzApJvGXaQWhp3Vwc7oEjeCVGJ8sJONS2dTet2sviFuTz65kLWvDKX8we82L3+DNEhBnaewDLH8BQXl3LuUijnLoXSqUNzFs4ZzI2UdADs7WxYOGUQx12CyMqp+v3SXMYH3L6OFmuBtpnqGRePZ5yuINgGK14fNYFhbdvjmRDHeh93TkZX3m66SrXYBaWWSdYJYUbKHwZV09I5tvEMK/+yiHU/PElUUBzbvziByxHfivb6+jD0erMh57D65GnsjVQ++/YUGzdfZMTQrhVFJy8/PJXU9Bz2n/Unw8hjeLTKPq2yWKvsLM9Emxwrvr/izmMjh7N54f34JyXwjddVjl0L1z83za8DimSdEPVEfk4Bb8z6gEET+vDQ31fw7HvLWPH8DNY9up4If8M6DFtCB5TSUpWLruFYA9ZA766tWTLNmYcWjuTwhUC2HfHkRkJ6pWuYegyPlh1QQP+8LC4tZVdAIPuCglnYry/PjhtFK8eGuj1aWVFcWqrdhAjtmG3eSeHJPaiqiu+FYHwvBFf83MC5I3jlv6t47oNlXDrsy/Htbvi7RWh6XGMVoWg5iqeouISjpwI4eioApyYNmDapLzOnDmDNirEUl5QQdS2Ji67hHDzmS0ZmHp07NudmQgZFxSUVRSglzWwoKVWxtlKwtlawUhSsrZRKi07upNWNr3LGuGFVG0UoWhagFJaUcCQ0TLc+VjQpsae7UzO+nLWQtPw89oUF82uIP8Ephs2frVBLN8fKmarNcR0pQKkN5X+xGpp0F8KihHpG8fig1xk9byjLXl3A0+uWsvrlOTw6/j29ikR7DOhA7yFd8Lsczq9fnaKRUwOObr1Mr0GdGDyuF5NnD+TYHo8aF57czlijeKoj8loSn393mm9/PM+ksb3ItNK99zZsYMdfn57D0UtBuHhHUVKNzjHGGMWjZQGKVuuB9oWf+cXF7A4IZHdAIAq6zF0yuj8fTJqBb2ICWwJ8OBARbHjBZy1cBKzjIwSMRfJOWCxVVbl80JPLBz3p3Lc9C5+czow1E/G8EEJ0yE3sG9hRWlpKUQ3O6+7GlDfLQP+svR6Xytcbz+rWAEZN68Frj0zj+VUTOeUayt7TvgRFVt2VQ8bw1Myj23ezdOAAHhk5jO/mLCEyPZV/up7npKEtks3vZpolkawToppUVeXoT+c4vvkCU1eMYdWb9/H2N4/y8au/cGrnVU2OYeoxPDXJ0uycAs5dCsUO3UOMXdo1Y+WcYTy2dCwnXIL59bg3EbGVX5c0tzE8WhWggGH5WVBczA8eXmzx9mVx/348MWYEX89exMp92w0fA1s/M1OyTggz4H8plL/MfB/niX2575UF3IjUjWrr0K0VifFpBp2balGAAjXLT31Hwn768TF277jKyvtGsmD6QJZMG8z6nS78vL/qyQZ1oQAFDD/XLC4tZW9gEAeCgnWFJg3hMefhLO0zgC88XDkSFWbYAw6Wq0Z5J4UnNeB31IPnx/+NOWunMHXlWKYvG0l8dBJvr/6aW9dTNT+eoScFt9O6Cwro3vgyMvPYe8ibvYe8cWxgx7RJfZk1bQDDh3Rh227dydEjq8fTvFlDtu26gm/ADfILirBOK8Ya6Nm9NVNG9gL43QW4qjqf3M4SuqDcvq6ldEEBOBYWzvGwcMZ16cz9gwayeoAzs7r1ZMKW9aiAtaJQotUbbT3ogiIFKFUaU/bfqEq/Sog7lJaquB7ywvWQF32Gd2fg+N4VRSdLnphCkHsUYb5Vt8YDmLBgKIoCnud1hafZGXmE+cQS5hPLhYPetGjblOiQeBQMz2ctslnfk5HCwmJOlf0e7YAebZwY0KsdU0b1IiU9h8MXAjl4zr/Kyvhy0gVFf+WrHPIOwbbQilXDnPnPtDm8OXYS24L8+MTdxfCTmlrK2DrYBcVYJO9EnRAbEs9Xr23mx3U7de2MGzdh4doJLH9mOse2uXJkiwuJcWkGH8cSu6CUu3ImjCeif2LRvCHMmNqfBVMGEnLtFi9+tLNGHVDAvLqgaFmAAoZnak5REZu9ffjFx5fZvXryxNiRONjosq+hrS02VtZkFOTrf4D6eTPNUJJ1QtRQaUkpp7dd5uwOV8YtHI77Vd0YlIkLhtCwcQNO7bpKcZFhhemmLkCBmuVpaanKG2/vpGvnFixdOJzZ0wewcOog/rnhBPvP+FdrDVPfTNNqjXJanI8WlpSw0z+A3QGBjO/SmSvJuqKTp4eMJLOggF2hARSV6jnGuH5lpmSdEGbE72IIfhdDALBp2Zx1Pz6Jg6M9O746yfFtrhQV6p+hpshPfbIz9kYq//niOBu3XOK+BUPxC4sDoHXzxvTr3oaLnpV3hjR1ZppLAUr5PU+bbCtiEtJR+sCXsxYSkZbCF55uHIwIqW8FKDXKOyk8qaEInxi+9PmZ79/exoQlIxi7ZDRJZRfTpt43gtzsfNzPBFW0ItaCuY/iAd0bX25eIYeO+3HouB/WVgolpSp2djYEhsTzwLJRvPrCLLz9YgkIiiM5NRtrayseWjGWPr3a4uoeiecl3dNIndo2Y/M/H8LFO4rjLiG4+lyjqLh6oWAJRSiW1gVFBVxiYnGJicXJwYGuzZqiopt7dnbV43gmxPFrSACucbHatJuqB11QygtQoP4VoSiKMgzwUVW19I6fnw68UvbpllrfmKgzQj2jCPXUfQ/k2KQBq1+aTeOmjvhfiWDv9+e4cjKg0nbFo2cMAKDf8G4sWFvE9fBbhPnGkpdTQFZ6Llm3dToxRoGoKbqgAISEJbDy4W8YNbw7cxcMZvX8ETy8aFSN52rXxy4oWmVudmEhm7192Oztw5jOnXh41BBGtO1QcSLTuYkTsZkZBh5ExvDUFsk7UZ/kZpXd0E9NI+CMH32HdWX5s9NZ/ux0rpwK4OCPF/FxCTP4OFpd7APDuqDUNGfDoxL5+MsTfLPxHDOn9qf/4I4VRSczxvYhPCaJmPiqH2SRMTxVK1VVjoaFczQsHAWgEawdNIznh41me5AfG/08ic/OqmqZe6tfN9OqJFknhHGUlqpc2u9R8fnE+UOYuGAoq1+ezc6vT3N8h5uu4NMApsxUffI0OjaFT746wfc/X2DerEFc9rkGwNB+HenesSWHLwRU+QCjqW+mabVGOS2ys1RVuRgdgw26P5PJnbsxrkNnXhg+hm+8r/BrcACFpXreqM2uG7edJOuEsEzFyal88eKPPPz3FTz/wf2seG4GO746xYntlleAAjXPztS0HDZuvqR7LbBw2UCeWDaO2JupbD3swdGLQRRWUsxq6sw0lwIUgFMRkZyOiGRO7148P2EMn82Yz8SOXXj97DHDNmdmtMw7RbWAqhxFUeagmxtkDWxQVfWfd/z6JOB/gDPwgKqqu8p+fgjwDdAEKAE+VFV1R2XHcrJuoY5xmK/XPr9y+5CegzqReiuDU7vcOb7djfhojUaS3EGLm1x3MqQI5XZ3e/Mb2K8DC+cMZsLYXjg2sKOouARbG2sAPLyj+d83J4m7mQ5Am1ZNWLl0JFMn96O5kyOZOfmcuRLGxl2XSU7P0WtPWo3jAe2LRYy9rtZdUMo1trfntYnjWdS/L03sHbiemcHOkAC2BfuRlKvf/6dK1eLFPVPcKKtOAYrb7H95ajG3zcmxnTq25+PV+trj/h9WeUxFUZYAS8o+bQvMRlf9eLHs55JVVX297GvPAb2Ay0B5H09nYFrZj/+mquoH1dpcHVObWQeG5Z0lcWzswOyHJ3Pfn+bQplML4qOT+OiZH4kMjPvD17Zo68SiRyYBMGrGADr3akNaUhaHfr7I9i9OVut4WuazFrmsTxEKQPNmDRk9vBv73XVdUV5/dDqocOCsP2ExiTVay1wz2Bi5q3Xm2pTNEXVq5cDlh54iJCWZn/y9OBwRqv8FwDvVQr7WJFdDlq7TJOtA8s4cSdbVH606tmDBE1OZ+9g0wvxi+b+HvwPA1t7G4DE8tzM0dw3NWn1zFsDWxpo9vzxHk0YNuOofw64T3rh4RdXoySktMlarPNQyV7XM014tW/DUqJEs6NsHRVE4EB7Meh93QlKTDV/cgAyNefgtOberwyzlOqawHMNnDOLBd5YxYFR3Um9lsP79fZzf76XZ+lqcx+qbqYZk6QsvzuT+2UPJzM5j72k/dh33rva1Y0Mz1NzyU6vsnNi1Cy9MHMPwth24mZ3FX84e4+KNGL3Winnuz5J1dZic2wlLMXTqAB76+/0MGNmdj579kYuHfDRb21TnozXNTmsrhYnjerNyxSj692hHclo2Ww97sO2IZ7XXMCQ3tcg6LdYwNCsVYEavntwsycQnMYE2DRtxde2zch3zzuOae+GJoijWQBgwE91v1h1Ypapq0G1f0xVdUL0OHLgtxHoDqqqq4YqitAc8gX6qqqbf63iGBJi1jTUjZzkz96kZjJzaH2sbazb/9whbPzuu13rVZWlFKCOGdqVv77ZkZuYTFZ3E9bhUMjL/eNPd2kph+NCuzJzSn1Eju3P/KxvJzi1gUK/25OYXEnm95heKtLz5Bca5aWWsAhQwThGKvY0Ns3r1ZPmQAYzv2IVVB3bgGnedJnb25BUX6d+a8V7qcQGKGReerAP+XsmXxKiq2rXsax8H7gMGAi0BW+AW4Ap8qarqxXstUpfVdtZB/Tths7K2YtzC4Sx9aT4fPvMDaUl3f9rVytqqomtZh+6teeythYyZNZBz+7z4+NVfatTRTKt8NmUBSrm/vDSHGVP6Y29nQ8i1Wxw468/JyyFk59bsfVKrHNY6K7VeT+u8dbCxYenA/jw8Yig9m7UgKTeHrUG+/OTvTVq+Rp2zzKQAxcwLT9Yheac3ybr6ydbeFqcWjUjNh1btm/H1ib9wbr8XB3++SGxYQtULVJMpb5iB/jnb1MmRBbOdWbRgKG1aNOZmUgb/3HCSq/41u8EjBShVa9e4MY+OGMbKwYNwvxnHI4d3a7a2PhlqxoUn65CsM4glXccUlmfQhD48+NflHPz5Iq7H/XFq3hCnFo2IDb+l2TFMWdSpT54O7NeB5StGMnlEL0pKS9l22INvdlyq0RrmUIRibgUo47p05vkJo/nrhVOEp6XQsoEjWYWFFJRUP/PMuPBkHZJ1BpFzO2GJBk3oQ3BIEqWlKosenUSzVo05tOkSKQkGdhcuY0h+1mbx5lDnzqxaNYbUjBze+0bXtaORo321r/FKAcpvnpgwgrfGTZbrmHewhJ5no4AIVVWjABRF2Q4sBipCTFXV6LJf+93dIFVVw277cbyiKIlAKyDdGBstKS7B7Yg3bke8ad7WiRkPTiTAV/dEdff+HXjs7YW4ngjgygl/kjV6MwPtR/GA4S3/y92t9b+HdzQe3tFVvrakVOWq5zWuel7D2tqKkpJScLLm+dWTGNynA2HRiRy9FMQJl2BSM3KrXA9kFI8xRvEUFBdzMDiEg8EhdHRqQlxGJjSCl0aMZVGvfuwKCWBrkB/XszT6O1+L7Y1NMSrAEsfwqKq6DlhXza/dCGw05n4slMVknaUqLSnl0j53Lu1zr/g5q+bN7vp15eKiEvlu3R6at2nClCXDOL37Kl4XQqt9TK3yWYsReYaO4vn3Z8f4euNZZk7pz7y5g/nLYzNo26Ix3+y4hKJAdeuYtRgTANpnpZYtj+G3vAVtMje/uJitPn5s9fFjfJfOPDRqKM8PG8Pu0EDS8vNwsLEhv9jAXKyFfLX0ETySdwaTrKuHigqKSI7XjaalkTWXD3oya8UYFjw8AR+XMA79fAm3k/6UFBtWrK7F+DtDzoH1zdn0jFy2/OrGtl1XGDemJ/ctGU5Sqi6UurRvjoO9DaHXqu40Zo5jeMxljEC5m1lZfHT2PF9edsPJwQGAjo2b8OXMhWzw9eBoVFjFLO8aq+VxscYkWacJyTthNP6XQnlzzocVn89/aTYPvT4Pf7cIDm9x4fJRX4NGCIDpxgiAfmN4AoLjCHg3jvZtm7J88XASUnT7t7WxZnj/Tlzxj67yfNXUIwW0WgO0y87LMbFcjonVfdII1k2Yzuj2HfnOx51fAn3JKzZs3JMpSdZpQrJOWBz/S79d0+3cqy1zHxzL8memc/GwD/s3nifUR7/uTuUMyU99r/3qk5vefrF4+8VibW0FjRR6d2nN+ncf4PCFQLYe8iAusfL7eIZkprnkpVZZueGSR9VfZEKmyjtLKDzpAFy/7fMbwOiaLqIoyih046wiNdpXpVITMvj140MVnzex70CbTi144cP7eeHD+wnzi8X1uD+HNl0iO716RRPVoXURihY3u8rpOxMb0BWdAHYZJfxt3R6mTerLrFkDeWnNFF5YPYmthzz4envNCozrcxGK1jfEyt3IyCxb34qzwdfo2NiJp4aM5Nlhozkfe42f/L05GxulzcFq8eJe+Y0yME0RiqUUoAiDWGTWWbrS1LSKH9+tCAUgMS6Ny8f86T24M32Hdq1R4cnttLghBtoWodQ0j7OzC9h7yJu9h7zp3bMNaWm6Fsajnbvy8kNTOXDWn+MuwaRUo7Wxlhms5Q0uY+Su1kWfLjGxuMTE0qphQ5JycqARfDFzAS0bOPKTvzfHosIoKDHgYnMt5KupclWYnGRdPXcrNpmPn/6e79/expy1k1nw9Eze+nota0b+nfTkLKxtrDQrQAHDb5hB7d00KylVuXg5nIuXw3VrAGufHc3cif3xD49n9wkfzl0Np6Co8vdlLfJVqyzUMlO1PH/NLCggs6AAG6xo16Qxje3t+XLWQmIz09no68nu0ECyiwr1P0AtPighzJbknag1B788TEF6JvOfnMGbX64lPTmLY9vd+Plfh6p+cRW0KkCBmuepPlkan5DO59+d1r0emDqvH+uen8e1GynsOObJCZcQ8goqL5aQApR7rWXFlqs+NJ3kwF/HTeGZoaPY4OPBL0E+ZBUakJnCkknWCYv2+TPf8es/97DomZnMWTuZqUuGs/Ob0/zw0QGD1zZFAac+D0KUlJRilwF5N3M4cSaIBdMHsniaM6fdwth62L3KByCkAEXciyUUnhhMUZR2wGZgraqqf7iSpSjKU8BTAA5KQ6Pswet0AI8Pep1OfdozdsEwxi0eyfKnp7F/43kAWndoRnJCRo1a+FfFXItQDH3qOiMzr+LGV+eOzZk1bQCht1IAcGrcgL8+PZvz7hFc9IokI6t6N+yNVYSiZbGIMdYE4xWhuF2/jtv167Rt1Ij7nQeyYvAg5vXoXVF4MqJtB7xuxddohvk91aMuKELcS1VZV/Y1Rs87S+TY2IHcsiIURVFQmjUFwMpKobRUJSUhHYDOfdoafCwts9nQ7mSG5HFYhK6Vsx2gpheRkZXHnx6czPOrJuEVFMuJyyEcuRhUUThamfrWBUWrrE3KySlb14pL4TE8PHIon82YT0bBdPaFBbMtyJeQ1JqPJfwd6YIizIxkXd2QmZLNr58cZtf/jtBraDfSk3XZ+N7Pz1CQX8je78/i72b4tWdL7YJS7ovPTxAeGM+SxcNY9/w8sh8tYOdxL9bvvFyt10sXlKp5x99kzvc/Mb1nD54aO5J3J07n5ZHjGLPpuxqNErgrKUARBjCH65jCMmQkZbHz0yPs+t9Rhk0fyIJnZ9O+S8uKX2/R1sngEQJaFnXWRgFKuYsngng/t5QVy0fy5hOz+NPqyRy/HMznm89Xu5BTClB+4xEXxyPbdjOsQ3temDCaN8dOoqGdLR9fdTFsg6LeknM7YWoJ0Umsf3Mrmz/Yw8w1E4mM1F1Da92hGYPH9+bMHneDHowwVQexmmbnraRM/vvFcX78xYX7Fw9n0bwhjB/SjQXPf0t+QdXnMlKAIu5kCYUncUCn2z7vWPZz1aIoShPgMPCOqqpud/saVVXXA+tBNytO/61W7XpoPNdD4/n140M0aORAXnY+Vs2bse6np2js5MjRra4c3+6q2VyxcpWdJJTfWKsJcylCib2RyoZNuk4ndkCXNo3o0aklE4b1oKS0FO/gG5x3D+e4SzBZOdW7oWGMp7DB/LuggHHeYBOys/nishtfu16hkb09NvlW9Onakl33reJWTjYHI0LYHx6Mf5IG82jrQRcUUWcZPeugdvPOksx7fBpx4Qm4HvZCVVXU8k4oZV1QRk0fAEDQVY06NpWpK6N4vP1i+dOrv9C5Y3OmT+7H9Gn9eWzpWA6dDwCgd5fWxNxMpaDQ+E9plzPnLijGKPj8xceXrT6+jO7cifsHDeSBfoNIys0hJDUZWysrHG3tyCjI1/8AtViAIuo0yTrxO6WlKqGeumxVFIVgl2AWPD2DsbMGEeYXy97vz3HxkHe97YKSk1vI7oNe7DnkxZBBnZkzfSDFZX8WVorCijlDOe0WRlJadqXraNkFpS4WoKjAqYhITkVEMrhdW/q1blVRdPLuhOn4JSVwKCJE/25iUoBSH9Wp65jCMqiqiucpfzxP+aMoCqqq0mlEH747/SZXTgXy61enDB4hAKa/iQbVz9Li4lJOnQvi1LkgBvbrwMI5g+nVpXVF0Ylznw6ERd+q9KaaoRmqxbmkVuejWp2HesXF89iOvQxo05pb2dmgwJTO3Xiw/2B+8PPENf561YuIukDO7USdkZedz4FvT1Z8Pu1PM1n7lwU8+PJsdn5zmhO/XqGoGgUY92Lq7KxubqakZvPtj+fZvMOVXj3aVOTjF28vJzAygb2nfLmVknXP12tRgAKG56UUoJieJRSeuAO9FEXphi68HgBWV+eFiqLYAXuBTaqq7jLeFvWTl627AaCmpfPz339lwbOzeei1uax+aRZXTgWy8+tThHgbflJwpzsvvK3f9yKpSdlcuRDCpZOB3IpPr9F65lKEAhASlsADj3xHrx5tmDSuFxMn9OG1R6ZzySuKrJwCunVoQWFRcZVzysrV1yIUY9wUK1FVMvJ1f+ejb6Tzwv6DLBzUl4cGDuGJwSOISk/l6WP7CU9L0eR4pihCsZgClOLS370PCLNQZ7PO3Nk72tHDuTOPv7+CwxvO4nbEi3CfaDKSsiA9ncXPzmLs7EEUF5Zw8bCP0fZRF0bxxN5I5cdfXPjxFxdaNG+EbXoJJc1s+PTNpdjb2XLBI4KTriFc9Y+pshOKVvlrKV1QQJsbZ26x13GLvc67p86gAtjCzK49+XTGPI5HhbM1yA83Qy4E1mK2akLyztxI1ol7UlWVTR/sYcfHh5i+ajxLX5rHG188TJOmjhz4qWYjVStjiV1QVPW3Odyge+Ci94iOvPTQVF54cDLu/jEcOh/IRc8ICosqL47Q6gluqJtjeHxvJuB7MwEbrGhkZ8eEjp1ZO2gob4+dzNYgX7YE+nIrp/JCn3syVgGKZJ05krwTJqWWdRZOj7zBtn8fYPFzsxg3xxnfy+H8+vUpvM6HGHwMSxvDExAcR0BwHIoCdipYt3bg0zeWopaqHHMJZt9pPyJikypdQ7qg/F7gLd0IBhusaIEjQ9u2Z1u3ngQnJ/KDnxcHIoIN2+TtJOvMkWSdqLO2vvsr4W4hrH57GS98tIJVL83m1y9PGnxeaugDEfpmZ01zMye3EB//69gBjg3syMkvYs3CkaxZOJJLnpHsOuGDR2DsPV+vVdFmvSxAqSN5p6hajLkwMkVR5gH/A6yBH1RV/VBRlPcAD1VVDyiKMhJdWDUD8oEEVVUHKIqyBvgRCLxtuUdUVfW517GcrFuoYxzmG+l3UrW2XVsx77GpzF47mfXv7ePsXg8cGztga2tNRmqO5sfrPbgznx54heKiEgoLinFsZE9KYhZXL4Ry7qgf/p7Req9tSBHK7fQtQinXrq0TNxMyKHSy5h8vL2LKqF6ExyRyzj2C8+7hRF6veRt6LcbxlNO6a4mx1gTjvNE2sbdndu9ezOrXk2dPHCC/uJglvfrRooEjByNCSMzV8O99Ld4kM0YRSsjSdZ6qqo4wdB0nuzbquDYPVOtrj934XJNjiqrVZtaB6fPOnPQa2o1lL85h1JwhFBUUEekXg1qq4tSqCT2cO5ORnMX+b06y7d8HsCrrgjJujjOtOzTjxA43crONU3SmxZg80CaP9c1iRYEhgzozY3I/Jk3sQ5OGDqRn5fG/TWc57lLzC1Fa5a+WOal15mqdtd2aNWPN0CEsGdgPJ3sHItNS2Brkx88B3hSXajDiUeNsjXn4Lc1yR/LO/EjWiepSFIVRswcTGHyL3Kx8Ji8aSr/h3di38TwJsRoVqpfRIm8NyVp9M7ZDu6bMnj6Q2TMH0rZlEzJz8nnq79uIiU+t0TpaZKsWWahlnmqZpeO6dOahkUOY3lXXVfWpY/srxscaIua5P8u5XR1Wn65jCvPn0NCeuY9OYfnL82nczJG1o9dpfo3ZErO0vAvK1El9sLezJTDiJp9tPod/eHy11zAkQ80pO7XITTtraxb168vaUcPo16IVvokJDGnTTrKuDpNzO1EfDJ7Uj9XvLCMxLpVPX98GgL2DLQX5RVW8snoMzU99s7OmudmmVRMWzRvCgrmDadq4AW9+up/z7hHVfr0p81KLrKwsJyP/8ppcx7yDRRSe1CZzCTBbOxtUVaW0cROWPT2NtX+ez6WjvhzZ4kLAFcPnXZdb9dIs1rw6l80fH8X9TBCDx/Wiz6ge9BnUiVZtm+B6NoT3X9lq8HHMpQilbesmTBzXmwkTe+PcuwNWVgqXvCL583/36b2mFKFo79+LZrO0zwBKVRXXuFj2hwdzLCqczEINb+7WUhGKlgUoUngitGQueWdO2ndvzdxHpzBqzhAaOjmSlZpNZmo2+746gcdJP4puGxXzyoZnmbNqLLnZ+Zz89QoHf75EXFSiUfalVQEKmLYIxdbGmpHDuzFjcj92n/fDO+QGPTu3Yvb4fpy4HEx4TOVPmN1JilBqxt7Ghrl9erFqmDNN7R2Yvv1HADo0akJcdqY2B9EgW6XwRGhJsq7uePDNxax6YzHWNlZcPubP3u/PEuRxTdNjmEPBpz4Zqygw1Lkzk8b15r/bzlGqqiydMRgrK6VGI2fNpQBFy3VAuyzt5OTE6iGD+dz/MlmFhUzr0p0mdvYcjgylSI9CTik8EVqRrBPVZWNrTa+h3QiN0BVwvv6/NQRejeTUbneDRgjcyZRFKPrkaONGDsya2p8FC4bwf18cJvJ6Mh3bNAXgxq30aq1haIaaSxGKVpk5pnMnHJvY8v28+yTrhGYk74Qp2dhaU9q4Cb0Hd+aDLc9y8KcL7Nt4nqz0XE3Wt5QCFDtbayZP6MMJvwiKiktYPmsIXTu0YNcJb6Ljqn4AwtR5aYyslMKTP5LCkzuYY4B17NWWBU9NZ+aDE2nk5EhsWAL7f7zAkS0uBq/9711/ou+Qrjw64T1SEnTjZxo1daRNx+Z079+BuKhEAiOTK+aDasFcilCaN2vI+NE9ybEu4dilYOxsrfn+3dW4+UVz2jWUsJia30CUIhTtdGvWjIX9+rJwQF+6NW3G6ehIHj+6F9DNNC/V8r3LQopQpPBEaMkc886ctGzfDMXKiqQb936qutfQbix5fhaTl43G1s6GY9tc+ewv2426r7pShFJuzpIh/PnR6djYWBMdl8KJyyGcvBxS7Qt8IAUo+mhsb09WQQE2Ta248vAzJORkszXQl71hQdoUeRqQq1J4IrQkWVe3tGjXjEVPT2f+k9Np3LQhJ3a4VTx1piVzKEABwzL2Hx8uZ9yQ7hQUFnHmShgHzvrjExJX7debSxGKORaglPt86QLm9ehNYk42vwT5sjXQl6S86l94lsIToRXJOqEPxyYN+OfRt+kzpAuptzLYu+EcR7a4aNrN0xK7oOheZ83/PTuXuRP74+Z7jd0nfbnsHVXt65DSBeU3Wt2Mk6wTIHknzEOn3u145INVTJg/hLycAg5vvsSe9WdJS9JmPIqpslPfzHzi4YmsuG8k9nY2XPGLZscxL9x8r1GdyKwrXVCk8OSPpPDkDuYcYPYN7Ji8fDTzn55JblY+7zz4DQD3PTmF2PAEAtwia9TiqVX7Znx35i0SYlP460PfUFxYQmZa5a0WtbzhBeZThALQqkUjXnt1LiMHdcHG2orYm6mcdgtj/xk/bqXUPDikCEU7A9u0QVHAO/smbRo24uCyNRyMCGFnSAAhqTUflXRPZl6AIoUnQkvmnHeWpnlbJyYtHU1iah5uJwJwat6QL4//BfczQbidCMDHJYxCjVow3q6uFKE4NWnApHG9mT6jP0P7dSInr5C5T39NUXEJ1lYKJaXVzxUpQqkZO2trFvfvxwPDnBncui15RUUcigzla68rXMtI0+YgNcxWKTwRWpKsq5vsHe2Y+eBEUjILuXIyAMfGDsxaOUY3/i4rX9NjmUMRir752qtHGxbOdmbG1P40dLRn2xEPPt9yvsbrmPqpNK3X0SpHFWBC1y48NGoo07p0p7CkhC88XfnC061ar5fCE6EVyTphiMGT+vHAW/cxbFJfsjNy+dvD3xHiFa3pMUydpfrkaIvmjVgw25mF8wbTqnljbiZlsvWwO7tO+FR7DVPnpznkphSeCC1J3glz0qVfB1a+voAp948lIzmLh8eso6RYg3HWZSype5hTkwYsnDOYJQuH0qp5Yw6e9eej709U+/WWXoAS+t6rch3zDjam3oCovoK8Qk5svsiJzRdxbNKA0sw8HDu24eHX5+HgaE9RQTEBVyPxuhDC5WP+xEdX3rJ+wKju2Nnb0KVPW9776WkSrqdw+Zgfl4/5kZ9beNfXKGm/FWBo8eZnnZlX8WNDLsbZZfx2Q0/fC3NJKdm8+c5OnJo0YOLYXkyZ1o+HF4/CxTuKWylZtG/tVFaQUr0bMfZZvwWNoScbttm//VirEwdjrAlgk60A2t4YC7h1S7c2VjSyscMjIZ6HBg7l8cEj8E9KYGdIIHtCA8kuuvvf22rLvu0t0YhFKHnZ9hU/1nIUjxDCNFITMtj39W/fUNs3sSHwUiiTFw5j7upx5OcV4nUhhJ//fZjYsATNjqtlJmuRx+VZXNMczsjM4+AxXw4e86VVy8b06NaKouISADa8/yC3kjM5cjGIy95RFJdUfhKnVfZqmZHla2mVtVrmbGFJCTv9A9jpH8CA1q15YIgzC/v1ZXOADwCtGjiSV1xsWL6WZ2stFXcKIeq+gtxCDn1/uuLz0Utm8PTf7+OhV+dybLsr+zeeJzFOm+K58qzVKmf1yVh9z3XDI2/xydcn+XrjOaZO7ENosq71cbcOLXh82Vj2n/HHIzCmyqfRyrNV31zVKlO1ytPyHAXDslQFLkbHcDE6hq7NmvLQ0KFEpev+3jWxs2dy564cjQqnWI8xPEIIUVt8LwTjeyGYnkO6svil+VwLjgdg8uJhNGvZmHP7vUhPNuwpbq3OW/XNUn1yNCU1m5+3XWbLr26MG92DxYuH0aZFE0A34q5f97YERVZ+bq9Vfuqbe1rlrzGu8wohhKWLCY7j349/x5YP99Klf0dKiktRFIU/f/4QXueDuXjYl4I8/a+laZGdhuZmdTMzIzOPLb+6sX3PVaaM78ONPF1zg9bNG7NyzjB2nvAmIfneI74NyUutstJYD+nXV1J4YqFyy940cm/c4v5OzzFwfB+GTx/I8FlDePydxeTnFRIfnYRT84Y0a92E6JCbf1gj9VYG/35xM6qqMmR8b0ZM7c/oGQMZP28w37+3j4TYe48XAOMWoYDhN75AvyKUjMw8Dh3349BxP5o1dSQtPRecrHlwwQiWzhhCWHQiZ66Ecso1lLjEjGqtWR+LULS6oHenmPR0XtxziGYNHFjYrx/LnPvz9/FTORYVRnZRIa0aOJKSn2f4KJ5aulFWXoQiBShC1B0J0Ul8tPYrbGytcZ7Uj7HzhjJm4YiKE45hk/vSvV97XE8EEBdV87Fud2NORSiG5HBSchZJyVnYAbY21vh4xzBz2gAmj+xFWmYuJy+HsOeULzHxVc8N1boIRasCFC3WAu1zNjAxkb+dOMU/zp4nt6gIGsFroyewsGdf9ocHszXQj4DkW/ofQApQhBBGcvqHU8R6R7L0T7NZ/OgkFj82Gc9zwbz3xAbNnjrT+qYZGJaxNcnX/IIijp4K0L0e6NavCSMGdGb6mD7EJaZz8GwAh88HkJxeefdTLXJVi0zVMk+1upkWnZbO+2fO6tbEikWj+/LBpJkkZGexOdCHbUF+pObnVbGKEEKYToRPNB8/+lXF56OmDWDa0hE88dfFeF0I5fQed9yO+9eo0/bdaHkjDYx/M62kpJSLl8O5eDkcRQE7FYZN7MGnby4jLDqRPad8OOESQl7Bvf9cDM1PLXJPi/yVAhQhhPij+KhE4suu7bZo14xegzoydclwnnl3Gef2e3FihxthvrEGHcPQhyH0zc2aXt8tLi7l1Plg3WuBEUPbs2LuMFbOG8YFjwh2HPXCN/Te418NyUtDs9JYD+nXVzJq5w51oWVX87ZNKcwvJDs9l+mPzSA24haRATeqfJ19Azue+r8lzFk9ltO73fnizR0UFZbU+Phaj+MB044AKNeieSOmjO/N1Gn9GdS7PQCuPtd49d979F7TnMfxWNIonk5OTlzPyKC4USmbFiynd7MW7AkLYmdIgHajAsDko3hk1I7QUl3IO0v11CePsOzpaQDciLyF64kA3E74E+RxTfNjmdM4Hn1z2NpKYcSwbsyZPpDxY3ry0ffHOeESQuOG9tjZ2pBSxY2y29X1UTxaZ+zANm14cMhgFvTrQwNbW3wTE9jo58GB8BBtDnCXXJVRO0JLknX1T6sOzVnw5DTa9OrAv/+0CYDVL83m1o1UXE/4yyieMna21kwc15t58wczYkBnsnMLmP/sNxQW1ez8X4tc1SIHzS1LFWBy9248PHIIkzp3o6C4mP3hwbx1/gQlZdfgZNSO0IpknTCWzn3bM33VeKatmkDrDs1wO+nPu49t0Pw4phwnADXPUQd7W2ZM6cfiRcPo3bU12bkFHLkQyPqdLuRU8+n2+jKGR0btCC1J3glLMXBcb+Y8MoWJS0fh0MCOd9Z8g9d5ja6jlTE0O2trDE+rlo1ZMn8oi+YNpkmjBgSEx/PMu9urPVbdEsbwyKidP5LCkzvUtQBTFIXy/8dWzZvd8+usrBRKS1WatW7C+z8/Tbf+7Xlhzn8qWiwaQutCFC2KUMCwQpTWrRozZUJfVEcrfjnkAcBHLy/ELzSO01fCSErNrmKFP5IiFG3M7NWT5UMGMKVzN6ytrPC4Gcd6H3dOREdoe6BaKEK5swBFCk+Elupa3lmaVh1bMGbeEMbdN5pBY3oSdy2JZ2f8E4DeQzoTE5pgUEvGu6kLRSiNGtlTWFBMtiM8uGAEzz4wkat+MRy5GMhFj0gKiqr/3mxuRShaZ62WGdvY3p4l/fuxcuggXOOu867LGQB6NmtBRFrlHfKq5bZMlcIToSXJOmFtY833Pv+mQ7dWFBUU43E+mIsHvblyKoDcbG07Dpr6xhnol68d2jWlV482nPDXnS/95/UlhMckcuBsQKUtke9k6ptoWq+jVY72bNGch4YOoVXThjx1bD8Ag1q14dD9D8u5ndCEZJ0wNkVRcJ7Yl6KCIkLCU2jWugmf7H2ZCwe9Ob3HXdNxsqbMUn0ydEDf9iyZP5T+/dvzwOs/UlKq0qV9c27cSqekijGx5Ux5U02rNe6VmVJ4IrQkeScsjWOTBky6bxSnjwZQUlzKiudn0K1fe45vd8PXJRwt7s1bSgGKvb0Ns6YOoFXnpny74xIA8yb2x9U3mrTM3Cpfb84FKFJ48kcyaqeOu/3NqzT1950fbi9EKS2rMEtLzMTXNZxu/dszbFJfTQpPjDmSx5ALc4aMAkhMyuLXve66dYCmTo60b+XE1FG9eemhqQRFJuDiFcnRS0HcTKrexTpDZ3/eTuvWUMZqNWWMNo0nwyM4GR5Bq4YNWTKgH8ucB9DFqSkADW1tmd61B6ejI8kpMqw9aG2MDCgfwwMyikeIuibpRgoH15/m4PrTODZpQOtOLShNTcO+bUv+se15AE7tusrRX1yJDjE8i8E443hqeyxedtlNQrsMuHwmFCdrO2ZOH8D7f1pATl4hFzzCef/bY1Tn3K2+jOLRImOzCgrY7O3DZm8f7G1ssCm2Yniv9uxY8gA+t26yKzSAA+EhZBbqmVXZckoihDCOkuISHhv4Gn1H9mDSstFMXj6GsbMGseWTo/zy6TFNj2VoC2QwzSieuJvpxN1Mxw5o4GCLlZXC2sVjePS+sXgHX+eESwhnroSRmVN5txhDz2e1ykGtclmrHI1ISeXvp3QFmzZY0aKNI08MHsEhw7YnhBC1RlVVfC8EV3zu2NKB2PAElj09lRXPz8DPNZxd357B42ywwTfRtBzFo+8YHqh+jgaGxBMYEo+1tRXWJaXQ3JYv37kfRVE47hLMkQuBRF5PrnQNQ/JTxvAIIYT5ys3M49jP5ys+V/LzGT65L1MWDyc2PIFd357h7F4PimvYdfJ2huZmbY3hKSgo5uAxX91rgeY9mvLO03MoKS3lgkcEB8764x4Qc8/ruVqM4TFlTtY3cpW3HmrcvBFZqdm/K0Sxat6sojtKs1ZNAMhMq37r+uqqi0UoAOkZuTz1ws90bN+MKRP6MGZcTx5fNo7w2CRuJmXStmUTunVsgVfg9SqfxtbqRlg5SyhCKT9BAe1OUpJycvj+qgffX/XAxsoKm1IrpgzuzuczFlBQXMyF69EcjgzldEwkWYUGdBW4/WZZLRWhCCHqltzMPKIDdSPxihJTWLfiU2Y+OJG5q8ax6JFJhHhHs+H9/QS6R2l2TK3yWIsc1ucmGehulG3YdJGNmy8y1Lkz0yf1w97JvuIk5bGlY4mJS+Gyz7VKZ26DNsWfWuajlmtpnbEFxbqsi7ieyodnzrF08AA+mDSTv42byolrEaxzOUNKXtVPKwghRG0KcY8kxD2S79/aRr/RPYmLSABsGDt7EHMfHMe+jec1a39sDhmr7/ltXn4Rb72zi9atGjN72kBmTO/PG0/MJL+wiGOXgnF0sKVUVckvuPd5jxbns1pc3NMqS7XO0bTEPN4/dtbgdYQQwlSuh93kb4v+RdPWTZixajxLXpjD3394ksfGv0dinHbjrg0t6NQiR6uboeXdTWzTi/nvZ8eZM8+ZFbOHsnr+CEKv3eKbHRe54hdT6RpaFXBKAYoQQpinbf85yK7PjzFhyQjuf3Uhr368GuexPfn4lV80WV+r3DR2ZgKkRqbz8DMbWTRvCLOmD2D6mD7cTMrknc8OEBx1q9LX6puXhp4fSgFK9UnhST1jY2vNilfmcT08gfO73CjI1d1wLy9CadOlJcMm9QHA1yXMqHupi0UoN+LT2PKrG1t+daNZU0dycgqwKyph9uJ+PLNyAnn5RbgHxHDJK4rLPlGkpFde3HP7RTswvBClvhahFJfq/hxP+IazInk7c3v3YnbfXszs1pOCkmKmbf2BuOzqt5G+p1oqQjFYSfEfOiAJIcxDaamK7/lgfM8H892bW5n+wDjmPjGd/FxdJ4mOPVrTsHEDQn0qv2hVE+Zwgwz0z19VBS/fWLx8Y3XrAHZ2NiyZNohWzRtTUFiEm280Z66GcckritxKRhhp3QUFtCtC0bILChiesRn5+fzo6cWPnl4MaN2apQMHML57ZzIKdE/Ej+/QmZs5WUSlmzBvJO+EEHdQVZUgt/CKz+1Ki+jRvwMfbnmW2PAE9v9wgdO73TUbd6d1xtZWviYmZbF5hyubd7jSq3trbsSngT0smubMU8vHc8EzghMuwVzxj6l0lIChuVoXu6CUqirp+ZV3j6kRyTohhImkJ2ay67Oj7P3qBP1G96woOnnlv6uIu5bEkS0uZGfkVbFK1SypC0ppqYqreySu7pE4NWnAjMn9mDV7EGWXJenQ2okenVtx2TuK4nvkp6mzU4vsvf28UxOSdUKIOqKooIizO1w5u8OV4TMGkVmgO69o06k5M+8fzYEfLxjcEMBSuqBcj0vlq+/PsP7H80wY05OZcwZx41Y6AGOcu9LAwZYLnpH3PN/UomOYqXLynupI3knhST1j72hPu26tWfbSPEbPHozHKX+uBVwnNSGd9j3a8MSHD9C4qSOuh7xI8I+qGMfz5tdrib+WxOndHsRFJWq+r7pYhJKW/tvTvru3XSEy8CZjR/Zg7JieTBrRk/yCImY/9RWFRSU0bdyA9KyqT8a07IZSH4tQVMArLh6vuHg+Onuewe3aMaZzx4qik/cmTqehrR0HwoO5dCOGEkNahNbCKB4hRN2WlZrNvq9PsO/rExU/t+zfDzBn1ViuBcdzbJsrZ/Z6kJ2uXXcJLcYEgOmKUCpeU1jMirXfMrBfB6ZM6MOkCb2ZPLIXn205x/Yjntjb2mBtrZCbf+9OKFoVf2p908zQdcppmbGBiYkEnkmEM2U/0Qg+nDyTrk7N8EyIY1dIIIciQwzrMCaEEEZwdocrF/dcZdKy0dz34jz+9I8VzFo5mpcXfqL5scxpFA9UP1/Dy87/7fIhwD2GE82bMmViH2aP70d6Vh6nXEP45OczVY65M/WNNK3WAOM8LCGEEJaqpLiEAJdQAGztbGjR1olZK8ew8oWZHN/uxr4N5zTrhGIOXVCgehmakZnH7oNe7D7opXs9MP/+ATx631jSMnM54RLMkQtBhMXc+zq7Vtkp4wWEEML8eJ7yr/jxsAfH8eArc1j2zDSObXVlz/qzJMUbnp2W0AWlqLiEs5dCOXtJ972EHXDfjMFMGtGTlPQcDp0PYP8ZP24m3f3BcS3G8IDhRSjiN4qhsxfrGifrFuoYh/mm3oZRdenfgTlrJzPxvlG0aNeU9KRMHBztcWioG+VxfvcVdv3vCBFlT1Pb2tuybs/rDJ3YB2trK0J9Yjiz253zB7zISNV+HM/ttChCuZ0hRSh30udGWLnuXVrStUtLjvnqnrbb8N5qWjdvhIt3FJe8ovAIjKWgsPoFC1qM5Cmn9cmEMU5OjHVx752pk1k6aABO9g4k5+VyOCKUXaEB+CdV3t6r2gwsQol5+C1PVVVHGLqNmrzPncjdrMkxhfmpD3lXFzk2dmDK/WOZ++R0ejt3pjC/iKNbL/Pt3/cY7ZhaZrEWOaxP/ioKDOjbgRvxaSRSwJwJ/XjziVm4+lzj9JVQXLyiqhzHczstclerfNQ6Z7XK2FYNG7Kof1+WOQ+gd/OW5BcX8S+3i/zo71Xp62Ke+7NmuSN5JyTrRE31H9OLRu1b4XEuGDsHW1746H6Ob3PTdNxdOXPK15pmq42NFaOGdWPGlP7YNbLjLx/vA2DhlEEERyUQEZtUrXXMJU+1ytKaZGjkX16TczuhCck6YY66D+rEshfnMuX+sVhZK/zjuZ+5dNjHKMcyNE8NydCa5qe1lcLI4d2YO2Mg40b3xM7WBv+weJ5at63aaxianYZmXk1eH/req5J1QjOSd6Ku69y3Pfe/PI9pD4wH4OSvV/j8zR2aHkOLc1B9c7MmmWllpTBqeDcWLBzKuKHdUFDYftSTL345X+019M3LmuZkwCfaZB3UnbyTjif1UExQHN+9sZXv3tjK0KkDGDnLmWZtnEiMTSbSLxaPU/7k3lYBXlRQxDvz/0Hztk5MXTGW6Wsm8ez7yykqKuHoL5exc7BFVVWKKpnzrC+tnrwup1UnFDDsaeyomGSiYpKxK/t8/34vxo3uycxx/VgyfTAFhUVs2n+VH/a6VWs9LUfy1MdOKOU+PHue/1y4xKRuXVnUry8r+w2koKQY/6RbWCkKPZo2JzwtRf8DWMooHiGE2crNyufID2c58sNZegzuwpy1k8nK0eWRoigseHgCFw/7kJ6cVcVK1adlVzItclif/FVVCAiO070euBaQwMGjPkye1Jcpo3pRUFjEZZ9rvPvVUQqKqn5/1qIDmbl3QjE0X5Nyctjo7slGd08GtmnD8kEDiCjL0M5NnFjRdxC7QgOIzkg3dMtCCKEZ3Rge3cMBnYd1Y8zMQcy8fzRhfrGc2HGFKycDSL6ZrsmxjJGvhmZrdXO1uLiUy1cjuXw1Uvd6wKZtA157ZBr2djZE3UjmhEswJy6H3PPJNNA2T+vKKB4hhKgLovyv858n1/PTul3MXDORwLK8mHH/KGYsH8WFg964HPHR5IFGS+qCUlKq4uYehZt7FI0bOTB9cl8cWzpW/PrLD03BJyQOF+8oiopL7rqGdEERQoi6KTYkno+f2cCmD/ay9E+zsWrwWybd9+QUvC6EEhN606BjmMP4uuqOrivPy1YtGzNv5iAiklMBaNzQnlXzhnPgrD8JlVz/1ncUj+Sc4aTjyR3qc+WklbUVpZXMZ75d1wEdSbyeQr6NA/MfGs8jbyzg4mEfzuzxIPBqFMb8e6V1F5RyWnVDMaQTiq2NNYMHdmTsqB74xiZwyjWUFk0b8t8/34eLVxQu3pGEXLtVZRvj25lrNxRL6ITSyM4OO2trEq1zGNuhE9sWrSQ4OZH94SEcjAipGNFj+IGqV4Ri7h1PFEXpCLwHzAFaADeBfcC7qqpa/nC6OqY+511d1XfaYD47+BrFRSW4nQzg2LbLeF8IpbRU+0w2x45k+nZCGdS/I1Mm9KFj1xa8/M/dANw/eyip6Tm4+ESRX83CWq3y1hw7oWidr4uH9+PfU2ZjbWWF+80b7AoN5HBEKNlFhWbf8USyzrJI1glD2TvaMXP1BBY9N5sufdoB8PjED4iPTsKxsQN52QWan/tqlbG13QUFwKlJA6ZM6MO06f0Z0rcjAO9/c5QjF4NqtI6pn+bWag24d4aac8cTyTrLIlknLMm0B8ax+q376NSzDSXFJfhejuDCQS+Ob6/eg3fVZconukH/DN341SO0at6YjKw8TrqGcORCIMFR1evCbEh2GqsLirl3PJG8syySd6K+auvcgx8u/hVrG2uuhcRzfr8X5/Z7cut6qmbHMCQ3azsvR0/rzT9eWYSCgpvfNfaf8cfFK5KSalz/NkYXFHPveGKKrJPCkztIgNVcn+HdWfj0DCYsGUmDhvYkxKZwZq8HWz4+atQClHLGKEQxl5E8AN27tuLlF2cxsFc7rK2sSErL5oJHBFsOXq20ou9upAhFf80aOLCgb18WDOzD8LYdALh68wZ/OnmIWzkaDnOrpAjFnAtPFEXpAVwGWgP7gRBgFDAVCAXGq6pqQLsYoTXJu7qpU5/2zFk7iZlrJuHUohG3bqTyzuqvibtWvZb3+qgrRSjlFAU2ff8Ends1J7+giMs+UZx2C+OyFKEA2uVr64YNWTygH8ucB9CzWQsyCvIZ9fO3hD39itmesEnWWR7JOqGlTr3bMWRKfw7v0o0Me+2TBxk2qQ9XzwRx5VQA3hfDKMgr1Py4pr55BvrlaptWTZg2uS9HPcNITM1i+ujeLJg6iJMuwZzziCC3Gn9WdXkUj7kWnkjWWR7JOmGJug3sxORlo5m8Yiw5mfm8OP+/AAwZ35uIgOtkZ+RVsUL1WdIoHisrheFDujB3xiAmjO2JvZ0tb//vAGevhld7DXMqQDHnwhPJO8sjeSfqM6dWjZl03yimrp7IgJHdAfi/h7/D/WzNCvyrYimZ2bpVY+bPcmb+HGdaNW9MYkoWD77xM9m5BdV6vZYFKOZceGKqrJNRO8JgoZ5RhD61ni9e/onxi0Yw4+HJDBrTo6LoZNrSEcRHJxPuF0tJcfU6qtTE7e2hQJsLc7e3WQTD3jANGckDEBWdxIuv/oJTkwaMHt6N8WN6MXdifzbtvwqAc58ONGloz1X/GAqL7t6GsZwWrYzLadna3xLG8aTl5bPZ24fN3j50cnJiQd8+jO/RheRcXVvQZX0GUFxayslrEeQWF1WxWiXKx/FY3iier9EF2Iuqqn5R/pOKonwCvAJ8CDxjor0JUW9cD43n+7e389O6XYyZP5SJK8eTcF33/eOUJcMpKijG83ww+bna3RjTclQAmG4cTzlVhbVPbWRQ/45MndiHyRP7MG10HzYduMo32y9ipSjY2lpTUHjv92mt8lar9o5a5qxW+ZqYk8P3Vz34/qoHg9u1pW+rVhSUmH32SdYJUY9dD7vJ9bDfWhu77HbFVlGZOH8Ic1aNpTC/iJM7r/Ll279qelwt2yGDftmqT67eSspk2y7dOasd4FCk0LltM/727Fz+UljEJa8ojlwI5LLPtXuuUVdH8Zg5yTohhNFdC7jOtYDr/PTuLho3b0RpajaOHduw7qcnsbKywutCKBcPeeN6wp/crHyDjmVojtbmKJ7SUhV3r2jcvaJp1NCeqRP74uYbDcDyWUMZO6QrRy4EcdEz4p7XgA3JTkPPG41xfdeIJO+EEBYjIymLg+tPc3D9adp0bsnk5aMJKBtjt+TxyYycNoBz+z25fMyPnEz9izfNITOrk5eJSVn8+IsLm7ZdZvTIHvQb3KGi6OTBBSOJiU/F1Sfqnl1QDB3DAxaRc2CirJOOJ3eQykltWNtYU1Jcgn3bluwK/Bd2Drbk5xYQ5BGN/5UI3E74Ex1i2Dyy6jDnbiiGjuQpn/X5f39bzPQxfcjJK+SyTxTnr4Zz2ecaeQXVL36QTiiG27ZmBSPbdSS3qIiT0RHsCwvmwvVrlGjxHltWhGKuHU/KKicjgGigh6qqpbf9WmN07bsUoLWqqoYP8BWakLyrfz67+B59h3WlqLCYII9reF0I4erpIKJD4o12TC1z2FSdUKysFJwHdCQxKYvovCxGDuzMv15dgot3FKfdQnH1uUZBUe11QqnLXVBAuyfAQdu8k6yzTJJ1ojbY2FozcHwfxswbSlpmITu/PoWVlcKHvzxHoHsUV04FEOF/wyxH8piiE8qAvu2ZMaUfUyf3Iz0zj9V/+QmAZk0cScvMrdYadWEUjzk+BS5ZZ5kk60Rd0ntYNyYtG82k5WNo07E5RQXFfPrnbZzd66HpcUydofrk56K5Q1izaixtWuhG8Ry5GMj+M/7ExFc9asFUXVC0egpcrmMKkLwT4l7mPjqFlX9eRLsuLSkqKMb9bBBn9njgctRXs2NYyigeWxtrNm94gnatnEhMyeLAOX8Ong0gMbXqyRH6ZqX3evPseGLKrJOOJ8IoSsqKIgoSklnT92UGjevDoAl9cZ4ygLV/nk9JUQnRITdp1NSRJY9Nxt8tghCvaAryDegUcRfm3A3FkKexy4tOAD76x0GODfZj0rjejB/bi5lj+xIUeZPH/7YVAHtbmypviEknFMOt3vIrwzt0YGH/vszr25vFvfqxNciXt8+fNHzxbLN/q55a9t8TtwcYgKqqWYqiuACzgDHA6drenBBC55Up63Ce2JfhMwYxfNZgHn1zIZ16tuHjV34BYPLiYQRciSQlIUOzY2rZDcVUnVBKS1V8/K/rXg+kx2Zy4nQAEyf0YcZYXeHnefdwPtt8jsycyp/G0/LJbdDm6W1D1wHjZauZkawTQtxVcVEJPueC8Dn3W5vjpm2dsLWz4YEXZ/HgK3NIuZXB1VOBHPjpomYFn+bUCaUm57OBIfEEhsTz1YaztG7ZBAB7Oxt2fvIYMTfTOHoxkBOXQ8jMvnem6vuEWjlz6oJiZiTrhBAmFeZ1jTCva2x4Zzt9R/Zg8vLRRAbozsWGTerD3AfHc/GQN1dOBRo03s7UGapPfh446sOh474Mc+7M/NmDWT5rKL27tOaFD3fq9mOlVPlkN9SLp7urQ/JOCFFnHP3xHEd/PEefEd2ZumIsk5ePwcHRvqLwpM+QLkQEXDdoGoUhuVmbeVlUXMKDj3/P2JHdWbBwKI/dN5ZH7xvDh98d5+jFyscSGXqOaYZMlnVmfzdTWL6MpCwu7ffg0n5ddXrjZg0BKE3Loce4XhUX44oKi/nhowPs23jeaHsxdiGKFjfCoGYnHsXFpVz1vMZVz2t88tUJBvbrgIOD7vX2tjYc/Ppp/rnhJGeuhFVrPSlC0Y8KeMTF4REXxwenzzK5ezduFGcC0KNpc1b1d+aDy+cMOoYZ61P233v9JQtHF2K9kRM2IUymtKS04sbYxr/uoFkbJ+wcbClNTaPTiD68+eVaAM7s8eA/L23W/PhaZrApx/HEXE/hk69P8tm3pxji3Jnpk/sxYECHas8RLVfXi1DqYAGKZJ0QotpSEzJ4deo6mrRoxKjZgxk9ZwiTFw3j4mEfokO0P5653ECD6mdqcXEp8Qnp2AH29lZs3HyJObMH8doj03lxzRSeeXc7QZEJla5haJZqkX917IacZJ0QwmyEuEcS4h5Z8XkTByv6D+/KhHmDyc8r5JVFn2pSzGnKDNVnFI+HTwwePjE0dXLEqexYzZ0c+fmjh7j/1Y3kF1Tv4UNDclPyTgghzE+oRxShHlGsf3MrTVo2pjQ1kxZ9OvPxvpdJT87i0XHvUlR49zFtNVGem7U5iqcmeVlSUsoltwguuUXQtnUT5s92xjv4BgDtWjXhZlJmpa/X8v6oiZks62TUzh0URckCQk29j2pqCSSbehPVIPvUlqXsEyxnr5ayzz6qqhpcLaUoyjF0v+fqcABuf9xwvaqq6+9Ybz3wJPCkqqob7nK8D4G3gbdVVf2HfrsWWrOgvLOUf5+yT+1Zyl5ln9rSJOtA27yTrLNMknVGYSl7lX1qS/apPbM7t5Oss0wWlHVgOf9GZZ/aspR9guXs1VL2aXZZV7ae5J0FsqC8s5R/n7JP7VnKXmWf2pLrmHeQjid/FKrVPCZjUxTFwxL2KvvUlqXsEyxnr5a0Ty3WUVV1jhbrCItnEXlnSf8+ZZ/aspS9yj61pVXWgeSdACTrNGcpe5V9akv2qT05txMasoisA8v5Nyr71Jal7BMsZ6+WtE8t1pGsE2UsIu8s6d+n7FNblrJX2ae25DrmH1l0nxghhBBmIaPsv073+PXyn083/laEEEIIo5CsE0IIUddJ1gkhhKgPJO+EEELUdSbLOik8EUIIYajyNoe97/Hrvcr+e695ckIIIYS5k6wTQghR10nWCSGEqA8k74QQQtR1Jss6KTz5o/VVf4nZsJS9yj61ZSn7BMvZq+zTMGfL/jtLUZTf5YqiKI2B8UAu4FbbGxOVMte/T3eSfWrLUvYJlrNX2ae2zHWfknWWyVz/Pt3JUvYJlrNX2ae2ZJ/aM8e9StZZJnP8u3QvlrJX2ae2LGWfYDl7lX0aRvLOMpnr36c7yT61ZSn7BMvZq+xTW+a6T5NlnaKqqtZrCiGEqGcURTkOzAJeVFX1i9t+/hPgFeA7VVWfMdX+hBBCCENJ1gkhhKjrJOuEEELUB5J3Qggh6jpTZZ0UngghhDCYoig9gMtAa2A/EAyMBqaia9c1TlXVFNPtUAghhDCMZJ0QQoi6TrJOCCFEfSB5J4QQoq4zVdbJqJ3bKIoyR1GUUEVRIhRFedPU+7kXRVF+UBQlUVGUAFPv5V4URemkKMpZRVGCFEUJVBTlJVPv6V4URXFQFOWqoii+ZXt919R7qoyiKNaKongrinLI1Hu5F0VRohVF8VcUxUdRFA9T7+deFEVpqijKLkVRQhRFCVYUZayp93Q3iqL0KfuzLP/IVBTlZVPv63aqqkYCI4Cf0IXXa0AP4DNgjJysmQ/JOm1ZSt5J1hmH5J12JOuE1iwh7yTrtCVZZxySddqRrBNas4SsA8vIO0vJOpC8MwbJOm1J3gktSdZpy1LyTrJOe5aSdWAZeSdZd2/S8aSMoijW6Cp8ZgI3AHdglaqqQSbd2F0oijIJyAY2qao60NT7uRtFUdoB7VRV9VJ086I8gSVm+uepAA1VVc1WFMUWuAS8pKqqWc5xVBTlVXRvFk1UVV1g6v3cjaIo0cAIVVWTTb2XyiiK8jNwUVXVDYqi2AGOqqqmm3hblSp7r4oDRquqGmPq/QjLIlmnPUvJO8k645C8Mw7JOmEoS8k7yTptSdYZh2SdcUjWCUNZStaBZeSdpWQdSN4Zg2Sd8UjeCUNI1mnPUvJOsk57lpJ1YHl5J1n3e9Lx5DejgAhVVaNUVS0EtgOLTbynu1JV9QKQaup9VEZV1ZuqqnqV/TgLXQufDqbd1d2pOtlln9qWfZhlRZaiKB2B+cAGU+/F0imK4gRMAjYCqKpaaM7hdZvpQKQEmNCTZJ3GLCXvJOvqLwvNO8k6YSiLyDvJOm1J1tVfknWinrKIrAPLyDtLyTqQvKuvLDTrQPJOGEayTmOWkneSdfWXheadZN1tpPDkNx2A67d9fgMzfMO1RIqidAWGAldMvJV7KmuF5QMkAidVVTXXvf4P+AtQauJ9VEUFTiiK4qkoylOm3sw9dAOSgB/L2qBtUBSloak3VQ0PANtMvQlhsSTrjMjc806yzigk74xDsk4YSvLOSCTrNPM/JOu0JFkn6iPJOiMx96wDyTsjkKwzHsk7YQjJOiMy97yTrNOcJWQdWGbeSdbdRgpPhFEpitII2A28rKpqpqn3cy+qqpaoqjoE6AiMUhTF7NqhKYqyAEhUVdXT1Huphgmqqg4D5gLPl7WaMzc2wDDgG1VVhwI5gNnOiQQoayu2CNhp6r0IIX7PEvJOss4oJO80JlknhPmSrNOGZJ1RSNYJITRhCVkHkndGIFlnBJJ3QpgvS8g7yTrNWULWgYXlnWTdH0nhyW/igE63fd6x7OeEnspmr+0GflFVdY+p91MdZS2bzgJzTLyVuxkPLCqbxbYdmKYoyhbTbunuVFWNK/tvIrAXXVs8c3MDuHFbpewudIFmzuYCXqqq3jL1RoTFkqwzAkvLO8k67UjeGYVkndCC5J3GJOs0JVmnPck6UR9J1mnM0rIOJO+0IllnNJJ3wlCSdUZgaXknWacNC8k6sLy8k6y7gxSe/MYd6KUoSreyCqUHgAMm3pPFUhRFQTeDK1hV1U9MvZ/KKIrSSlGUpmU/bgDMBEJMuqm7UFX1LVVVO6qq2hXd388zqqquMfG2/kBRlIaKojQu/zEwCwgw7a7+SFXVBOC6oih9yn5qOhBkwi1VxyqkZZcwjGSdxiwl7yTrtCd5ZzSSdUILkncakqzTlmSd9iTrRD0lWachS8k6kLzTmmSdUUneCUNJ1mnMUvJOsk5blpJ1YJF5J1l3BxtTb8BcqKparCjKC8BxwBr4QVXVQBNv664URdkGTAFaKopyA/i7qqobTburPxgPPAT4l81hA3hbVdUjptvSPbUDflYUxRpdMdavqqoeMvGeLFkbYK/uexhsgK2qqh4z7Zbu6U/AL2XfuEYBj5p4P/dU9g3BTOBpU+9FWC7JOqOwlLyTrNOe5J3GJOuEViwl7yTrNCdZpz3JOo1J1gmtWErWgcXknaVkHUjeaU2yzggk74QWJOuMwlLyTrJOW5aUdWAheSdZd3eKqqqm3oMQQgghhBBCCCGEEEIIIYQQQgghhLBAMmpHCCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4QQQgghhBBCCCGEEEIIIYQQQgghhF6k8EQIIYQQQgghhBBCCCGEEEIIIYQQQuhFCk+EEEIIIYQQQgghhBBCCCGEEEIIIYRepPBECCGEEEIIIYQQQgghhBBCCCGEEELoRQpPhBBCCCGEEEIIIYQQQgghhBBCCCGEXqTwRAghhBBCCCGEEEIIIYQQQgghhBBC6EUKT4SoAUVRohVFURVFecTUexFCCCGMQbJOCCFEfSB5J4QQoq6TrBNCCFHXSdYJYV6k8KSeK3tD1vfjkVraY1NFUdaVfTStjWOagqIojoqizFUU5a+KouxRFCXmtj/rddV4fVNFURYrivKeoiiHFEW5aYz/V4qi9FAU5TtFUa4pipKvKEqSoijHFUVZVsXreimK8quiKCllr/NXFOXZKl7Tp+xrvRVFsdHq9yCEqF8k68yHBlnXQVGU5xRF2akoSoSiKHllH9cURdmmKMo0jfbZRlGUjxVFCS1bP1VRlIuKojyhKIpSyevaKoqyUVGUBEVRChVFCSv7vd4zwxRFaakoSrKiKDcURWmixf6FEPWT5J350CDvJiuK8mHZeVa4oihpiqIUKYqSqCjKWUVRXlQUpYEG+5RzOyGERZGsMx+GZl0l63572zrRGuxTzu2EEBZFss58aHBet66a/996GrjPYYqibCnLnwJFd29wr1LFdVJFUUYoinJEUZQMRVFyFUW5qijK/VW8ZmrZng8asmdRd8nJvrh1j59vBDSs4mvytN/OXTUF/l7245+A9Fo6bm0bBRwx4PVLgB+12crdKYoyD9gJOJb9VCbQHJgFzFIU5UfgcVVV1Tte1w1wK/taFd3fnYHA14qidFdV9c/3OOR3gC3wpKqqxVr/foQQ9YZknfnQO+sURekExAC3XxzMLfu8a9nHA4qi/AA8papqiZ7HGQ4cB1qU/VQ20BiYUPaxXFGURaqqFt7xumaAC9C97KdygF7A+8Bg4F4nbh+XHWupqqqZ+uxZCCHKSN6ZD0PP7f4MzL/t8xygAGgFTCn7eFlRlDmqqobpcwA5txNCWCjJOvNhaNb9gaIoU4GnNFxPzu2EEJZIss58aJV1RUBqJb+u9/mRoihPAN/w2/3+DKANuvuFSxRFeVdV1XV3ed1o4BzgAJSU7XEk8KuiKC+oqvrVXV5jD3yLLk+f03fPom6Tjif1nKqqbe/2Afy3qq9RVXWHCbdeV6UBp4H/AKuAhBq+PgE4CnwILNVyY2UXGH9Fd2HSBeijqqoT4AS8V/Zlj6K7SHqnd9FdmDyF7mJpI2AlUAq8qihK9ztfoCjKY8Bk4AtVVT20/L0IIeoXyTqzo2/WWaMrMjkNrAU6qKraEF2mDAD2l33dY8A6fTb2/+ydZ3hURRuG70mvJKH33lGkiIJYQKT3bhcLKlZsCIoFRf3sDVGxgaj0agFBBAUVlN57TUgIIT3ZtN35fuwuRkiyZ+vZMjfXuZLszsy+Sch5dmaeeV8hRBzwA+bFwv1AJyllLOaJ/UOYJ2G9gffK6P4Y5oXJHUADKWUMcD3mydgIIUS3Ml7veuB2YJmUcokjMSsUCoUVpXdehzNzu1+AR4AOQCUpZYxFV6paHjcAjYAlQgi713XU3E6hUPgqSuu8DmfXMc8jhIgCPsO8+ea0Vqi5nUKh8FWU1nkdrtC6Pyv4ndWUUh53JDAhRBfMRpAQYClQT0oZj3me9qml2QtCiFFldH8Ts+nkG8zzwBjgcctzrwkhYsvo8yzQHHhOSnnKkZgV/o8ynigU3sN6KWVlKeUNUsoJUsq5mE+1aWW2lLKWlLKflHKyGyY5L2GenKUAA6wn66SUuVLKF4AZlnbPWk4GlKan5eN4KeU5aWY+5k3CIOCG0o2FENUwC98pYLKLvw+FQqFQ6IczWpcBdLT0/VpKeRpASmmSUu4FhgIrLW3HCyEiHIjvSaAm5g29ftbNMSllkcXpbz3Nca8QovkFfa1a96yU8qSl31rMi6dgPkF+Hkt8nwA5mBc+FQqFQuE/ODW3k1K+J6X8UEq5TUqZU+rxc1LKD4HxlodaA10ciE/N7RQKhULhLM6uY17IK0AT4A1gjwviU3M7hUKhUDiLq7XO1byB+aDeLmCUlDIRzs8b78ec9QvgdSFEsLWTxezZFXOmkwellHlSSqOU8l1gG+bsYP+ZZwohWgFPA1uAD937bSl8GWU8UTiMEKKaEGKqMNdozhLmes1HLfUv21TQr64Q4l0hxB4hRJ6l5thpIcQWy+OdSrVdBxwr1f3YBbXP1pUxfpgQ4gFhrn2dJsx1OFOEEMuEEH1tfE+Rlnpte4W57meqpcZZD/t/QvYhHSwJ4Kr+FSGEiAasdb4/llJmltHsNcvHSpjTeJWmquXjkQseP2T5WO2Cx9/FfIruISllrr3xKhQKhatQWudanNEqKWWWlHJrBc9L4EvLlzFAKwde5nbLx7lSymNlPP8h5lNuwcAtFzxnr9ZNxpyuebJ1YqhQKBR6ofTOtbhzbmZhY6nP69rTUc3tFApFoKK0zrW4UuuEEJ0xZ/Q6CEx10bBqbqdQKAIOpXWuxQPzOocR5kyTV1u+fEtKWVxGM+u8riFwbanHEzD7A9LkxaXhLtI6IYTAfDghGCfKmysCgxDbTRSKixFC3IC5HnS85aFioAhzut9GwK1CiLFSyq8v6HcZsBbzjQ3MjrpszA70WphTCScAYyzPpwNp/PuGP83Sh1LPlx6/AfAj5pT7YK45nY25ptkgYJAQ4hMp5bgyvidruuD2lodKMNeg7gv0EUI8WMGPBCHEi/zrlm/kaHosL+VqINLy+YqyGkgpjwsh9mHe6OsFfFXq6XOYfwdN+O+phaaWj2etDwghemKe8C2WUi53SfQKhULhAErrLsYHtK6g1OfB5bYqAyFEC6C+5cvytC5XCLEe88+rF//+LMCsdU0xa93+Uo+XpXVtgAnAP8A0e+JUKBQKV6P07mJ8QO+uKfX5hZtitlBzO4VCEXAorbsYb9E6IUQ45gMEAvNmVoF5f8upMdXcTqFQBBxK6y7GW7TOTfQs9fnKctpswJyNKxaz1q21PJ6BuVRqVSFEpQvMJxdpHXAP5nnkOxUdClQoQGU8UTiAEOJSYDlmAfsMc3rfSGmud9kAmA6EAV8IIS6/oPvbmEVqK+ZUTaFSysqYa4k1x5wG8fzilZRyGNCpVP9OF9Q+G1YqrmjMN9g2wDqgmyWueEusj2N2st8vhHi0jG/tc8wCVgjcD8RKKRMwuwGXAu9zsaM9ULik1Oe7K2hnfe5C9+xqy8d3hRCVhZnhwGDMArcGzO5VzKkps4GHnY5aoVAoHERpnc/SzfKxCPNpOXuwV+taX/C4VeumCiHqAQghrgXGln7eckrgU/5dWDXZGadCoVC4DKV3voPlpF8zIcQzmH/2AL9bSwfYgZrbKRSKgEJpndfzPGaj4xdSyt9cNKaa2ykUioBCaZ3X00YIsVsIkS+EyBVCHBBCfCaEaG+7a7lYtS5VSplaVgNLZhKrgbJNqcfzgT8xH9qbJoSIEkIECyEewWw0ysWSZVMIUQN4HTiBWbMVioqRUqpLXRddwIuYnYeyjOfWWJ57tYL+71vaLL3g8XzL413siKWhNRagYQXtnrO0WYdZHMtqM9TS5iwQUurxK0q9xl1l9AsG1pdqM6ain1lFcdr5ezhuGe9FB/uXG6+d47xtGSfdRrt3Le3SLni8CWYXpcS8GJlXKrZ3S7V7zfLYg574f64udakrsC+ldRf180mtKzVOo1L68rUD/R8u9b1VqqDdo6XaxZR6vHKp70VinqRZP19cqt19lsfe1OP/vbrUpa7Au5TeXdTPZ/QO8ylDWc61HKjsQBxqbqcudanL7y6ldRf18wmtw7yZWAykAPGlHp9pGee4g3GouZ261KUuv7uU1l3Uz+u17oLXN2LOqFVc6jETMNXBOBZZxthqo90SS7vNFzzeBXPmaIk5k4yhVFyPlmo3x/JYf73/BtTlG5fKeKKwCyFEQ+B6zDeitypoak3XdYMQonSq+0zLx1ouDw7utnx8R5ZdzwzMLshszGnAOpZ6/EbLx1P8N40wANLsDHy5oheXUr4opRSW67gdcfsCsZaP+TbaWZ+PLf2glPII0BlYjPn/QAhml+wjmF2tVlfuk8Am4GPLYzcJIbYKcy3Cs0KIr4QQNZ3/dhQKhaJ8lNaVj7dqneVU9QIgCnOKz4kODFNauyrSu9LPne8jpUwHrgJmAamYT5IcwTzJHG2JsybwP8yT1Bcsj/UUQmywnHrIFEIsEkI0cyB+hUKhsAuld+XjRXpnBM5YrtLl5BYAEyzaYy9qbqdQKAIGpXXlo7fWCSFCMJfYCQEekVJmunB4NbdTKBQBg9K68tFb64BDmMuxtQAipJRVgGigN7AFc8asZ4UQTzgwtrPzur+Aa4FVpdpsAW6SUr4PIITog/n3sEBK+aMl2+XDQoi9QohCIUSSEOJ9IcR/xlYENiF6B6DwObpaPgYBeyuouWkVrmigCuY36QA/YE5LOEsI0RXzKa1/pDm1k8MIIepgThkG5nRhxgqax1g+NsC8EAZgTS+2Tkopy+n3O2bxVn83DiClPAAML+u5UqkpAcZKKU2W+nzTMNcEnA80xlxH8GohREf537pzCoVC4UqU1vmQ1lkWLL/DPDktBm6RUp7WIxbL646poMl7mFOJ3iylzBdCDMK8cVcILMOc2nQYcK1F6066NWCFQhHoKL3zcr2TUp7FnPXEOmeqgznF9BPAECHEQ1LKGTrEpeZ2CoXCV1Ba571aNxFoB/wgpZyvcywXoeZ2CoXCh1Ba56VaJ6X8tozHioBVQojfMcffCXhRCPG5lDLLw/H9jdkEcxFCiCjMJZqyMGcIA3gD8wGDJMyZUNphPoBwuRDiOillibtjVng/XvnHqPBqals+BgE1NPaJKvX5BKAp0B3zaajHAaMQYjvwIzBDSpnkRFxgdkbaG1d1y8dyX1tKWSCEOIf279ufyLF8jKqw1b/P51TY6mLux5za639Syl1CiHjMIlaIOcXbQQAhxGzgVuBp4Fk7X0OhUCi0orTOR7TOckLjW2AI5onmzVLKVQ4OV1q7ojCftiiL0j9TzXonhOiL+XTcXCnlCkvsH2Ke+PeTlnrmQoipmDXuNeAW7eErFAqF3Si98xG9A0s+bUgEJgshtmJOrfyxEGKTlHKHHUOpuZ1CoQgklNZ5odYJIVpjLr+QCzzghpdQczuFQhFIKK3zQq2zhSX2Z4DVmI03PTAbGLXi7nndC5jLmt8vpUwWQrTAfADiDNBBSplqOQy4GugG3Al8ZudrKPwQVWpHYS9WV+SZUimqbF3HrZ2llJlSyuuBazAvPv2BeaOoI/A8cEgIcZMTcQG00hjXTEd+AAGK9eR4gqWcQXnUuaC9TYQQtTBPwI4CL1ke7oVZEH+wLkxasKaKG6J1fIVCoXAApXU+gGVx7xtgFOZSBLdKKRc6MWRp7apTbqt/n8uWUuZqGbjUKYFMYLzl4Y5AfWCLdWHSwluYa6cOFEKo9+oKhcKdKL3zUaSUi4GTmNd07rbR/ELU3E6hUAQSSuu8k48wl695BcgQQsSUvvj3sKwo9XioHeOruZ1CoQgklNb5Ln+V+ryxnX2tWleRzpV+3p55XVvMBqQ/AGuGzcGYSwN9I6VMBZDmDCfvWZ4fonV8hX+j3vAo7CXF8rGqECLa0UGklBuklE9LKa/GnJZwMLALiAS+FELY61BMKfV5g3JblY81rVi5N2khRDjmFGSByO5Sn19SQTvrc3vsGPsDIA4YJ6U0WB6ziuyRC9oeuuB5hUKhcAdK67ycUplObuRf08k8J4e1V+v22jH2FKAhMEFKecbyWJlaJ821zdMw116tZsdrKBQKhb0ovfNtrCf/mtrZT83tFApFIKG0zjtpZPn4GuYT2Bde1uwg9Us99qAd46u5nUKhCCSU1gUmVq2rLoQoU2Ms66ctLV9qmtdZjJIzMBsn75PyfJkjNa9TaEIZTxT28oflYzDQ1xUDSikLpJTLMde9BIgAri7VxFTq8zIL1FkcmtaFt4EOhLHZ8vE6UX4RvGsJ3PJUGwDrwmGfshoIIRoArSxfaipzIIQYAIwAvpVll0a48AReRSfyFAqFwlUorfNiLJOm7zCnNraaTua6YOiDmE+PQ/laF435BAho17p2mE/CbQA+L6NJWdqm9E6hUHgCpXc+iuX7sm7a2ZsyWc3tFApFIKG0LjBRczuFQhFIKK3zXTqX+vyYnX1Xl/q8TK0DumI2P4JGrcNcAu9K4A0pZVlmFTWvU1SIMp4o7EJKeQhYZ/nyFSFEXEXthRCVS30eYiOtoKHU56WFq3QdzvgK+lvrh90thGivNS4L1lPS9YE7ymgfBEyuaEx/RkqZh7mGOMC4cn7vT1s+5gBLbY1pmeB9BKQDj13wtFVkr7jgcasQH7U1vkKhUDiK0jrvpVSmk1GY037e4iLTCRYH/9eWL28UQjQso9mDmOuuGi1x2IrXekrABNxb6pQA/Kt1HSw1Ua192lheIwc4a+e3oVAoFJpReuedlNaECrgTqGn5fJ0946u5nUKhCCSU1nknUsqGFZVaAGZZmp4o9fh7doyv5nYKhSJgUFrnnVRglrE+H4655BxAHrDGnvGllEcxGyEBniinJN1Ey8cTwO+2xhRC1LHEdBiYesHTal6n0IQynigc4WEgF2gObBRCDBZCRFifFELUEULcJoRYA7xeql9dzPXgJgsh2l/wRrwt8I3lyzzgfD1MS1pCqzPyzgoW4t7GnPorAlgrhHhICHE+zZYQIl4I0VcI8TWwvnRHKeUmYLnly4+FEGMtN36EEPUxi1wXIL+8H4oQ4kUhhLRcDctrVxFCiAQhRFXrxb9/o1GlHxfmeqdl9a96QX8rMRc8F1VG35nW+MsJ73nMv5tawPdCiGaWftFCiOeB+y3tpkopMzR8uy9jftPwlJTywsnXasxvaq4UQjwohAgWQtQD/md5fpmG8RUKhcIZlNaVgZ5aZzGdfIM500kJcLO95XU0xP8W5lSgUcCPQoiOln5hQohxmLULYIaU8qCGl3wI6AS8LqXcd8FzWzD/zusAUy2vURn40PL8D1JKEwqFQuFelN6Vgc5zu6uFEL9bfu51LxizmRDif8CnloeOADPLeG01t1MoFIp/UVpXBjprndOouZ1CoVD8B6V1ZaCz1l0rhPjlwnmdECJUCNHD8v1eaXn4JcvP9MLXXmeJ/Xg54T2N2UB5GTBXmI0jCCEqCyGm828GnAlSSqOGb/dDoBJwv5Sy4ILnrL+LYUKIYcJMG2CS5XE1r1OYkVKqS10XXcCLmGt4yXKe7wokW9tg3gBKw3yTl6Wuz0r1aXjBcyXAOaCw1GOFwIgyXm9yqTYFmNMlHgfmXtCuNvBXqbYmIAPIuuC1D5XxGlWA7aXaFFn6Wsd5wPKaEhhT0c8MaOjgz/34BXGWd80sp7+WvhJ4sYy+Myv6nVva9MP8JsM6Tqbl92j9+ktAaPg+O1j6rSuvPeaTctZxDaU+PwLE6/03oi51qcv3L5TW+ZTWYU6fWTruFBvXaEfiBzpafs/WdtmW17N+/TMQruH7rGvpe6C89sBwy8/d+js3Wj4/5+jPV13qUpe6LrxQeudretftgucNmE9JX/j72F6Bls2s6HduaaPmdupSl7r85kJpnU9pnYZxrTp2XMvvvAI9VHM7dalLXX5zKa3zLa3j4nldPuZ5XWkdMgKvVPDa67Cth/cAxaXGzCilR5Iy9gLLGWewpf2sCtq8f8H3Y/18IxCq99+IurzjUhlPFA4hpfwDs3vyScwpmjIxp9QyAvswOyFvwVz30koSMAh4F/ONKBlzusESYC/m1LyXSCkXlvGSrwKPYq7rVoz5DX8D/k0vbI3rNOZaczdhduAlY3a2h2EWiO8tMV1bxvd0DrgKeAHYj/nmXAKsBHpKKafb/MH4OVLKn4C2mFOkHcfsVM3AfIpthJTyLimlrGgMYT6xPgPzz/a+8tpLKd8Fbgd2YK4TeA5zmsyrZRnuT4VCoXA1Suu8jtLvW0OBGjYuh2qMSim3AG0w/w4PWV4rD3P6yrFAXylloYahpmGuo3p/ee2llIuA/pgn4CbMp1OWAldJcy1chUKhcDtK77yOLcBtwBeY50JZmH8fJsxGjQXAjUBHZ7RCze0UCkUgobQuMFFzO4VCEUgorfM6dmH+XSwCDmI24MdbPu7ArC3tpJTPOvMiUsrPMWdO+Q7z7zMKSMWsQT2klC/aGsOSrWUa5nnaExU0HQ88jtmIGYL54N80oLeUstjR70HhXwgb6wgKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCkWZqIwnCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqHUMYThUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVDKOOJQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhcIhlPFEoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQuEQyniiUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoXCIEL0D8DZCIqJlWGxlZLDjY0hn7TwusANVi42mRnQMp7KzyCwscH7AIOn8GGUNG2xyy7gXEuym+CsiNMjo8dcsCyEEDaNqY8REakEyRukdcV1IeFCJ3iG4lHBR7PIxj+w2pEkpqzk7zg3dI+S5dG1/e9t3Fv8spezj7GsqvI/Q8GgZHlO5wjYmB/TIUf10SjtdoJtVY6Op6Srd9FHNDFStjAmJpkZEFbKLM8ksztA7nDLxF43Uoo2u0jpQeqeAsNBoGREef/5rGSw0961WPZaESlGcOJ1OQZHtv0F7NdMX9TImPIyGCQmczc8jJS/XucHcoDn+pJN662NoUCj1o2qRX5LLuaKzeP4dQsX4gy4m7slWczuFSwgLipSREfGa2sogbUKgVS+rVoulcpw2rfTU3FIvnQwKErSoVo2CkmKOZWY4d990sd74iz7qqY21IqoQGRJFakEyhaZC3eIoC2/WRKV1ClcSJiJk5dgq1G1ei+QTaeRmGWx3CtF+Y3elRgohaNyoGsXFRk4mp9vUBE/MJR3WRyfnkI2rVCY0OIjDGemUmJzQIxdqjTt10d81UQhBg6jamKSRM4XJmKRn9ndtoacWukrrwH/0ThlPLiAstjIthz0OQHGMY2M42q80JTHO3aDCQ0KYNmooHWvWZuzKpaw7ecz5oGLc88cbGeOZN+zx0RrejLiYmtE5Hn/NsmhVqQnPtR5Hbkk2Hx56lYyiNL1DKpMGkd4Zl6M0Dkt16XjDm24/4YpxzqWbWLuihqa2CXUSq7riNRXeR3hMZS7tPd5mu8JY+2cYeuins7oZFhzMh6OHcnnNOq7RTR/VzEDUSoHg3saj6VPrGlanLGf56Xm6xlMe/qKRtrTRVVoHSu8UEBEez5XtHvjPY0VxoZr6xlWKZMaHd2AyScY8O5ucPNv3X3s10xf18pEe3bm9Q3te++s3Pt3+j1NjuUMr/Ukn9dbHoXV6cnvDIaxLXcmixNm6xlIWvq6LT7VepeZ2CpcQGVKJLk3v1tTWWClSUzt7tPKzaWMoLjEy5pnZ5BmKKmzvqbmlozrprEZe37oVb/fvy3d7dvDM76udGsvVGulOfQwEbYwPrcQbbR8lKiSG9w++THJBoi5xlIU366HSOoUriQyKpr28gU+/ex0pJQ/0eh2TyfZ9WybEan4NV+pk92ta8uLEQcxbuZX3vl5rs70n5pJ66GOT6tWYe/NojmSkM3rZPAwlThzWdaE2+oMu6qWJl8W1ZHLrcRzPO8z0w/+jWLr+ALYj6KWHrtI68B+9U6V2KiDUwUNcjvYrTUiu9tN4ZVFYUsL9C5exP/0sn/QeRKdadZwPKtc9PiVDbrhbxr2QzDxtbxxcSUqe9jc27mRf9hFe2D2N6OAYxjd7jqrh2m5enuaEwWvvlQ5xtKi63iEoFOUSJLTpTHiO55zLzuins7pZZDQybuFyDqSn8XGvQXSoUcup8XxVMwNRKyWSGUfnsSL5d3rWHMSg2jfqGk95+ItGKm1U6E1YlrZFkaxsAy++uoxqlWN4flxftMimvZqpx3zTWb18ec1alh/ax6Qu1zGy5SVOjeUOrfQnndRbH5ckrWZZ0hq6Ve9Dr5qDdY2lLPxFFxUKVyAytG08BGdr2wixRyunvLaMmlViefa+3jbbOzK3dETzHNVJZzVy2d59fLLxb25ucxljLm3v1Fiu1kh36mMgaGNmcTaTd0+n2FTEg00nUiXMJQebXYLSQ0UgYTJJvpm6iAYtanHNgHaa+mjVSFezdv1+5i/dzOg+HejZpaXN9p6YS+qhj/tSz/LY9z9xSbUavNujL84prevwB13USxN3ZO3nvUOzaBzTnDGNHiLIS2wGSg+9B+/4H+HF6Gk+cZbcoiLunreEpJxs3r2+H6Ea04VVPKhvbqRZCcQNNSuHco8zefcHhAWHc1WV7nqHUy7+JhBqg03hrTSqW4XKcVFuGdsZDdRzMy23qIi75y8hJS+X928Y4Lxu+qhmBqJW/ms++Y1u1XtTPdxJ45Gb8BeNVNqo0ButG2r7DiTz0We/cnWHJtwyoJOmPv5uPpHA08t/5veTx3jl2p7UjnHy/q3MJxWitz7OOr6EtambGFh7FPWjGusaS1n4iy4qFK5AL/PJnn2n+XTm73S/ojkje9s2W3jqYINe5pO3129g1bFDPHdVd9pVr+nUWMp84l2kFp7j+d3TCQkK4cb62rIMeQqlh4pA4vfFf3P8QDI3j+9DUJC2e7ZeGvnJl+vYvj+RSWN70aB2xeXOwXNzSUdwRh9/PXKUV9euo0/j5tx92eWOB6F08SL0mi/+kbaVz48upG385XSt2kOXGMpC6aF3oErtuJHQXOfSIIfkCqfTPKYbDIyZt4iYyuEUO1NDrTS5IW5Ji2zIDfdY2R1Pk5IXq3uqZIBjeYk8vv0NIoJcUHrJjZwwVPXqVJH2crSousvL7igUzhIcHMSbTw7lgZfnUWijHnd4jsnulI/OaqCjOKud5/LzuXP+IqITwlyjmz6qmZl5kR4vu+MNWjnj6HxWJK9HkqxrHBXhLxqptFGhN2FZxZpSJC/5YRuV68Tx57ajbovFUc10Rmud0ctik4mHFv9Au6a1OJ3rgvu2G7TSn3RST32USKYd+oaDORs4me++vwFn8BddVCg8SXC2QVM5Aa1aOX/JP1SpXYmte09pen1755eenls6o5ESeGrZSm68si27zp5xbWAuwJ366Clt1FMXTxmSeX73R0QEafu/7kmUHioCBSkls6cs4MnP7qNBi1oc23fapeO7UiONRhNTpi5jxI1XkHw2y1UhOoVec8iZW7ZRbDSx+OQex17ciovnjkoXHWdFym+kF2WSbNjg8ddWeDcq44kG9Dq5Dc477QGSc3I5dOIcALe1aUetaBe44Hz0FDfo58LX+7SalbOF6ZwyxJEQWoWnWk6lXlQjvUMqE39zJ6rT3QpvIzkli5aNavDiA/00ld3xVFpkZ/pZcVY7E7OyOXDcvGAzquUlVIl0MjOMj2pmIGY+AfNiYqIhnisrX8vQOrfoHU6Z+ItGKm1U+ApffLqOo4nm+VR4mO17uic10xmc0cv84mL+3HcSgOvqNaRNVSf/nlXmkwrRUx9NmFiZYjadNIhqzOUJV+kWS3n4iy4qFM6iVzkBgI8/WsORU+Y5VEiw7eVmby4pAM5pZF5xMV9s2IJRSqpFRRMXHuF4ID6mj/5+whvgaN4p9uZAEEH0qjGIsCDPZPHWgtJDRaDwx/ItjOkyxS7TiT0aqTXziRbSM/KY8fFaioqNREXYNnP6sz5+u30HOUVFhAeH0LqKEyXLVOaTi9BLFzel7+CkIZbYkDi6Vr1elxguRGmh/ijjiUZ83XwCUFvG8tSV1zB74AgqR7jghuejG2mgzCcAZwrjiQ6O5uFmz9Aoupne4ZSJv4mE2mBTeBO5uQVM++xXul3RjAdvulZTn0AynwDUE3FMuboHM/sPIyY0zLnBfFQzA9V8AlAptCnX1+jHsLq36h1KmfiLRiptVOiJ1hTJ5rZGHr/jet6fNMItG2qOordeRuaHMOWaHszsP5yGcfHOBeNjm2sQOOYTgERDPDfUGMhtDcfRIaGzrrGUhb/ookLhLHqVEzC3NfLknT14dfwgzX3swZc218CskQuG3MhHvQYS4kwZVx/TR3/fZLMSGXIZ/WqPYGzjxwgR3pNUXumhIhCQUpJ5JBGAyjUqae7naoOmPRpZnXBmvXobtwywXWrG280nzhCSG8Qr197AnMGjaRJvu/xQufiQ+cRT6KmLHRIGc2P9u7m+ej/dYiiN0kJ98QnjiRCijxDigBDisBBiYhnPPy6E2CuE2CmEWCOEaFDquTuEEIcs1x3OxOHr5pPUvDzuXbSUurGVmNl/uPObaOCzG2mgzCephed4euf75BRn8WDTibSIbaN3SGXibyKhNtgU5aGH1i1avoX5S/7h8KmzmuMMJPNJck4ODy//gVZVqjOj7xDCg4OdC8hHNTNQzSdfHV/M8tO/0r16X2U+cTNKGwMLb5nbWbFnsXDv9lNc1qIOE+66QVN7T9Xo1mvREMxld+5duJRgIZg9YCTVo6KdG9DHNtcgsMwn/9s/h6O5B7m94QO0jeuoayxl4S+6qPB9vE3rysMd5pPEI2lc07EJN/d3/caao+hlPik2mZj2x0aurtuAKVc7eQrYTXNJdxEI5pMDOcf46NC3tKx0KXc0fJAgL9pmUXqo8ATeoHXj37yJNxc+QnCI9r8/vQyamVn5HDyRyrgbr6FD63qa+tiDJ80nzq63fvjbRoqMRr7sN4wEZw7I+4j5RK99SE8y5+QPbM3YyNC6t3BN1Z56hwMoLdQT73lHVA5CiGDgI6Av0Bq4SQjR+oJm24DLpZRtgYXAG5a+lYEXgCuBK4AXhBAJzsTj6+aTzUlJPLTsB1pVqcbnfYcSHuyCm7OPbqSBMp+cK8pk4s4PSCtK5b4mTynziYdQG2yKC9FT6z76fC0r1u8FICJc2/08kMwn644e4+kVP3NVnfq8d0N/TWWJKsRHNTNgzSfHFrE8aQ3dq/dleN3b9Q6nTPxFI5U2BgbeNrezonWx8Nff9zNrzp8M7H4pN/XTtunuC+YTZ7XyaHoGdy9cQuXISGYNGEGlMCc1SZlPKkRPfSw0FfHcnhmczD/GnY0eoXWly3SLpTz8RRcVvos3aJ07Su5o1crF32/l100HGHfjNbRtXttme3/XyaV79vHxxk3c0qYdj17exfEgwGc22DyNnrq49uwmvji6gHYJV3Bj/bt1i6MslB4q3Ik3aB3AHwv+pHbDavQY3sm++HUyn7zx5gpOJWfw8sP9qZYQU2FbT6296mE+ScrOZtzi5dSMjmFGn8GEBTlx0M9HtNHfDZkmTLy671t2Zm5hVP0xdK5ynS5xXIjSQn3weuMJZvE5LKU8KqUsAuYCg0s3kFKulVLmW77cCNS1fN4bWC2lTJdSZgCrgT7OBuTr5pN1R48xYcXPXF6rDp1r17XdQQs+upEGynySUZzN0zs+4O/09ZzMP6Z3OOXibyKhNtgUF6Cr1oVlGel8WUMWvTeWJvXc97fmiye5AZbt3cfUX9fSu1EzrnSFbvqoZgas+eT4YpYlrSG3JFvvUMrFXzRSaWNA4FG9Eybti3VaFwu/+nYDv246yEM3X0fX9o01j28PvrapBrAr5QzjliynSXxlRrW61KmxAGU+sYGe+lhgLGTyrk9ILjjFFZWv1i2OivAXXVT4LF6xjunqTTXQrpVvvbWClLNZvPzwAOJjbd8bfcF84gxvr/+DBbt281inrgxq2tK5wXxkgw08r4t6aeMPyeuYe/JHOiR0pnp4LV1iKA+lhwo34hVat2nldg5sP8FNj/QmJNQ+84I7TJq2MBiKeP7lJYSHhfLKowNtlnD1VGYwR3FmDrk9OZmnflpJp1p1ea5rd+cC8ZGsYP5uPjFKEy/v/Zq9WTvoXr0vwcLJzOEuQmmh5/EF40kd4FSprxMtj5XH3cAKB/tqxtfNJ9/v20/Pz7/it1PHnR7rPD66kQb6mk+8YVMtz2jgrQOLMBjzCRWhXFH5Gr1DKhN/Ewm1waYohe5ad3LPGUpKjLw9YRhV422nyXd08uOLm2kAM7dsY+Cs2fyVdMp2Yy34qGYGqvlk5vHFfHFsHQCVw7xTi/xNIxV+i8f1zp4NNS1ICa+//iPb9p1CSqmpj69kCnNWL/88cZKhs7/l8x2bnRrnPMp8UiF66mO+0cCknR/x9fFPdItBofBidJ/b2YurtTIvv4gXX1lGSEgQjepU0dTHU+YTR3FWIyf/vJr3N//FupMuOPClzCflopc2zjv1E49se5XUwmRdXr8i1DxR4Sa8Ruu+nrKAmvWr0Gt0Z0eHqBBXZz05cSqdN95bgdFkIioyzGZ7T+ijXnPInw4cZPKq1Xyx00XzRxfhD7qolx6WyBKm7P2KDw5OxSiNusRQFkoLPYsvGE80I4S4FbgceNPOfvcKITYLITaXFORp7ufr5pNTWVmE5AZxXb2GTOriotRHPrqRBvrWWvOGTTWAREM8reL6cVvD+xlYe7Te4ZSJv4mEHuaTQglHS4SmS+F9OKp1lr7n9a646L96l3Yul0kvLiI2Opw3nxqqqeyOp533eptPDpxNIyQ3iKvrNuC2Nu2cHs9XNTNQzScABmNDJrR8hWF1b9U7lDLxN410FqV3vo0r5nZFJeYDdq5eLCwoLObxCXP5c7t580hLGTZPmk+cwVm9tGplo7gEnriiq4uici3+UloA9NXHPKOBk4ZYYkPieLTZc9SNbKhbLGURKJqotM63cYnWmcrXOHtOc7taKw8dTeXGOz9l2/5EzTHYiy9trhml5MO1f5FdVEh4cAhtqnrXQSR/2GSzopc2phaeI9EQzzVVe3rdgT5f10Sldb6Nq9Yxi2RhmW02r9rJrk2HuWGEfeV2QL+SO+s2HGD8k9+RnVugqb23m0+cYe6OXSQmmTMMN03QZlYtE2XKvAi99LDIVMyB3BBCRCi3NLiXBlFNdInjQnxBC/1F73zBeJIE1Cv1dV3LY/9BCHED8CwwSMrzKqSpr5RyhpTycinl5SERtk95l8bXzScAV1Wrz33tOvF4JxctDProRhqYb/qBXnpnRfLv/Jyynl41BzGsjtpY8wQq84kCD2gd/FfvQsMu1rvDR1OZ8r/lNGtQjRcf7O+VG2l6m08ARra8hJevvYEhzVo5P5iPpIO8kEA1n5wrymRN6t90r96XQbVv1DucMvE3jVT4HR6d24WFRNkdoNbFQnNbI0N7tGX686MJD3WPYVOPRUNX6GW/Os15uGMXxl9+lXMD+aBOBsoGm5XUwgQqh1XlgaYTVIkBhcKMZ7UuqOJ7jjtKCWjVysLCEsKyjIzs3Z7OlzW02d5TBxv0nFOG5AYx5ZrrmTNoNK2rVHN8IB/LCqbnYT9PEoSgbXxHbm5wDy1i2+gdzn9QmqhwMR5fxwwT5d+jpt74Hs/d/ql934E1Rp3MJ1JCXGwkr44fSIPalTX1sQdPziNdMX98oP0V/DDiVi6tVsPxQZT55CL0nCueK6pO05hW3NvkcRJCnTAVuRClhZ7BF4wn/wDNhBCNhBBhwI3A8tINhBDtgU8xC1hqqad+BnoJIRKEEAlAL8tjLkUvNyC45qb+5u8bmLdjF49c3oX7213hgqjwafMJ6Ft6R28kkk+OzOX707/SvUZfRtUbg8D7HHT+JhLKfBLweI3WbfznKB/N+JXTqZma+/jKKW5wjW5O+n4Vfyad5M3ufehWv5HzQfnYoqGVQDWffHVsESuSf6dnzYH0rTVM73DKxN80UuFX6KZ39pQRsMd8kp2Sy2Ut6vDMvb00tQ8U88mnf//Dgl27Gd/pKka3utS5YHxQJwPJfHKuKJPndn+EBB5sNtFrFhWtKE1U6IDXzO3Ov56LN9VAu1aGhgQzsNslTHmwH7Wrx9ls76mSO3qaTz76bSO5xYXM7D+cerG2fybl4mP6GAjl6ExIXt47k5SC09zd+DFqR9az3cmDKE1UuBCv0rrM1Gxyjp8mNDyE+6cMI76q/mtHWog2wGUt6vDa+EFEhodW2Nbb55HOauPCLXtIy8/nsz5DqBEd4/hAynxyEXppYk5JHlP2fEpoUBj3NnmCsCDvyDyqtND9eL3xREpZAjyEWXz2AfOllHuEEC8JIQZZmr0JxAALhBDbhRDLLX3TgZcxC+E/wEuWx8olyEFzvR4THSuu2ER7bvUvLDu0j4ldrlXmEwuBbD4B+PLYIhYnrqJ9QmfiQ13vunUFJwxV/UoolPkkcPG01tli0fdb+eSjXzFJSWhIsKY+njSf6L2ZVmQ08sDC5exPP8vHvQbRoUZt5wICn1s0tBKo5pPPjs5nzZm/6FdrOO3jr9Q7nDLxJ31U+A966507NtTW/3mIT2f+Rq+urbhrqHvqijuKngckAJ5b9QvrTh7jlWt7Om/U9EGdDCTzyemCVF7Y/RERQZE81GwSsSGVdIulLJQmKjyJ3lrnLK7WyuISI8+/tBSAN58cQnRkmM0+/m4+Sc7J5a75iwkLDubrASOoEml/hrbz+Jg+BoL5JN9YwAu7P6XAaOD+JhO8bk1VaaLCFXir1tWuEkHvGzvz0qz7iIzWfi/TK+tJ2rlcXn79e+rXTmDiPT1ttvf2zGDOaGN6voH7Fi8jJiycz/oMISLECX1T5pOL0EsTEw0pvLn/K2pH1uOOhg96zQF3pYXuxeuNJwBSyp+klM2llE2klK9YHnteSmkVqxuklDWklO0s16BSfb+UUja1XF9peT1P3cCteIP5xCQlE5atZNmhfdSr5F2LRGWhzCeeYfaJZTyy9VUyis8BeI0wXIg/CYUvmk+EmbFCiE1CiFwhRJ6l/ub9QogydUYIMUAIsU4IkWXps0kIcYenY/cmPK11WmhQuzLz3r6LK9s20NQ+kMwnuUVF3D1/Ccl5OQxo2sK5YM4P6luLhlYC0XwikUw//C1fHF3IrqwtusZSEf6kj96A0jvXoLfe2bOhppXvFmxixS+7GDuyKzd0sa0JvqKXzmplicnEI4t/YN+5VO5q29GpsQCf1MlAKS0AcDw/iZf3foLBmE+Q0GZc9iRKE7WhtM41eFLrwiNCqVzD9jqePSV3XK2Vp1Myef6VZdSvmcDURwcSHOT6kq6+ppOHz6UzdtFSakbH8M71fR0PAnxOHwPBfHKuKJOX9nxCeFA4zb2s5I5CaZ2r0HteVxbHdp9i6q0f0rh1bSbPuIuQUO3vSfUyn2zdcZIvv9lAr66tGN6znc32ntJHR3FGGw+cTePxH37ikmo1eL1bb+cCUeYTr2Fb5l6+OLaQpjEtqRZeU+9wzhMI80O99M4njCd64OmyAd5gPjFKyVPLVjL5918AiA2zfQrBJrkhKvOJg+i9qWYlozibREM8/WuN4M5GDxPshYuI4F9C4YPmk2+AGUBDYA7wORAFfAzMvLCxEOIh4HvgEkvfz4DawEwhxFseiVihiczjWeTkFfDKIwNpUs99f2O+aj5Jzzcw+pu5vPTHWucCKY2PLRpaCUTziQnJD8lrOZ4fQ1RwDNdVc3JS7Cb8SR+9AKV3foKrFwsB3v5wFVv3nqJagra0wIFiPskvLubueUsYu2KpU+Ocxwd1MhA22KzszznK+O3vkFWcgUAQKipOGe5plCZqQmmdj1GvaQ3ufHqgprbuMJ9o1cptO0/y9keruOKSBnRora38iLdvrjnLttPJPLB0uWvmk25ae3UXgaCNx/OTGLflJf5OX6/L61eE0kOldf7MPz/v5L2HvqTDtS157K2bEcL1B2ldrZHfzt/IH1uPcGPfDpoyT3tCH/WaQ/565Cgvr1nLgv27HQ/Aig+ZTzyBnnPFn5J/44GtL5NamKxbDAGKLnqnjCcuxtfNJyYpCc4NolpUNCtGjeGB9qrsDijzCcDpAiPtE67k7kaPEiK8c0LrTxMnXzGfCCGGAjcDx4A2UsqxUspHgXbAD8BtQohhpdo3BN4C0oHLpZQPSikfA9oCR4AnhBBdPPtdKMrDYCjimecXkl9QxJtPDqVynO0UwI5mDfNV80mGoYCQ3CDqxsYxf8iNztXodiPKfOJeLonry4h6tzOkzs16h1Im/qSPeqH0LnDRulhYXGLkyafnMucn7VmQAsV8km4wYMwyERsWxuvdelM5wkm9UOaTCvEGbUw0xHN7wwcY2+Rxr5s7Kk0sH6V1vknm2WxuGHkFzdpqM3PYYz7Rilat/GnVLu588Ev+2X3S5TFY8aXNNYD1x09wIjETgH6NmzuXZ9jHNtgCQRuzS3JJNMTTNKYVjzR7lshgJ8oquZhA1UOldYHBqtnrmfniAi7v3orqdRM099NLI6WE117/kbEvzKG4xKhpXG83nzjD7G3b2bj/FABVnSlHBz6jjf5ecgcgq9j893VDjQE0iWmpWxyl8Wct1FPvlPGkAjy9eeZsXyvOTnoAss4WsDk5kQmdr+WBDlc6HxQo84mDeMPCIcD3p9fy6ZG5XBrfkbGNH/e602tW/EksfMR8MtTy8W0pZZr1QSllEfCc5cuHSrW/CwgHpkkpj5dqnwG8avnyfrdFq7Cbs+dymfTiYuJjI3nzySGEh9m+lwea+QQgriScZglVmDN4NHVjnSxZ58N6Gajmk8VJq/nx9Dp61OjP8Lq36x1OmfiTPuqE0js/w54yAlo31IwmSViWkY6t6zH9udFERdrOIBko5hOApmFVGNysJV/0G+pczW5Q5hMbeIM2bjx3lFaV2nJ7wwe8rmSr0sRyUVrng6SnZJJxNof7Xhhmu7GduEMrj580l3LudEl9urRrZLO9p3RSb428vmpjpvcexPNduzs3kI9ssFkJFG3MLk6gUXRzxjWZQHhQhG5xXEiA6qHSugBhzpvfc9/lEzlzKt2ufq4uuaOVnNwC8hNzCQ4OYvD1l+KGRC0O4ag+ukIbBzZtwW+33EPHmrWdHssXCATzSVphDTpXuY57Go+naph37H35sRbqpnfKeGKDQDWfmKRkwvKfWbZ3HxOuvIYHlfkEUOaTlSnrmXboG1pWupSxTZ7QO5xy8Sex8AHzibUw39EynrM+do0Qwrrzcr3l48oy2q+4oI3CjQij1Nz20JEzvPTG92TmGAgO1vbWwVH9dBS9zScHzqYxZt4iYsPC+G7QKGpFO3nf9mG9DFTzyefHFrAsaQ3dqvdmVL07vW6TDfxLH3VA6Z0f4o4NNYBwg+TS5rV56aH+BGlYMQwU88mulDM89sMKLqteiw9vGECws6upynxSISl5sbrq45rUv/jy2ELaJ1zJTfXv0S2O8lCaWCZK63wQk8nEzBfm0+aKxlwzoJ2mPu4ouWMPYVlGxo7syssPD9BU0jUQzCe/Hz/OV5u3cGfbjoxzNvu0Mp+Ui166uDVzL28d+JL60Y25r8kThAoXlLd3EQGoh0rrAoj0lCwAbn60N9cObK+5n6vNJ/bMJbu1bMjEe3oxZkhnm209VZJOL/PJpgOJnMnL5ct+w2he2Yl7lQ/por+bTwpMhby4ZwYCuLfJk3ZlAgsWwVwS18EtcfmpFuqmd8p4ooFANp889dNKlu7Zy1NXXsPIFpc4HxT49GYaKPPJmtS/+ODQ1/x97ne9Q6kQfxILLzefWN2SZR1Vamz5GFLq8xaWjwcvbCylTAbygLpCCO/JP+rH2DPx+XPTYSY9u5B8Q5FbzSd6aqezurknNZUx8xcRHx7JnMGjqBEd41xAPqyXgWo+mXl8MYsSf6Zl7CVEBUfrHU6Z+JM+ehild36KOzbUNm8/wfsf/0LX9o15+JbrNPUJFPPJ6kOHeWnNr/Rs1JQp1/RwaixAmU80oKc+fn96LfNP/USXqt0YWHu0bnGUh9LEi1Ba56Osmv07+7YcI6Ga9syL7jCf2DO/fOGlpeTlF/LWU0OpEm/7fbOnDjboqZGvrv2NZYf28XTna51fg/WhTTYIDPPJpvQdvHdwFk1iWjK2yWNeVYouwPRQaV2AEZSTTbtrmvPku7fStktTl4/vao1c/+chfl6zm3uGX0WnSxrYbO/P5pN0g4E75y2moKSEr/sPp06MExmmfUgX/d18klJwltf2fUH1iBrc2ehhgjTaFKqF12RInZuY1Op/btFQP9RC3fROGU804qvmE2cxScmEFT/zzvo/WHnsov9vjuPDm2mgzCe/nf2Hpaf3AtAsprVX1SgtjT+JhY7mk6pCiM2lrnsveP5Hy8fHhRCVrQ8KIUKBKaXaWQtqxlk+ZpXzelkXtFO4GXsWBwEqG0P45PnRDL7+Uk3tA818sivlDHctWMyp7CwMxfb9bMvEh/UyUM0n35xYzvjtb5JnzEVY/nkb/qSPLkTpXQDjjg215Su2s2DpZm7s15GcrrxtAAEAAElEQVShPdo6GppX4qxWfrNtB59u+pseDZpQxdma3eCT5hNPo6c+zjn5IwtP/czurG26xVARAaaJSuv8FJNJ8th1L7L8K/sOCOlpPjmXnsszLy4iLiaSN54Y7JaSrr5m0JTAxOU/8/up47x8bQ+qRTlpZPehTTYIDPPJhrQtTDv8LTnFWZikZ7PE2sKP9FBpneI/lBQbeXHYWyQdP8vzn99Do1bayrbYo5Fa0aqR73y0mmNJ55jyUD+qVbZ9qM1T+ugozmhjUnY2d85fTGRoKF8PHEFkSKjjgfiQLvq7+WRP9iE+PjyX5rFtaBzTXFOfc4Wp/H52FTEhlZjQ8hWigp088FkGPqaFXqt33mOt9QHCc0wUxtrv1QnNhWIH/wac6Qvmm3pJjPZyCmVhkpLpGzcBEBEfQt/GzVlycK9TYwLmG31MifPjXIAhN5zImEKXj3shmXmRxEe7/oSkLVLyYqkZ7fo3Po6QWVybsU0eJ63wDNMP/4/cEu+IqzQnDFVpEJlmu2EAUSRDOF5cRWPrU2lSyssraDAXuA3oDewVQiwDCoAbgFrASaA+4F0zasV/CMsqpihO2xt3Q34R2XkFPHnnDZxJy2HjzuM2+ziin76snduTk7lz7mJKYkyEB4cQExbGOUO+4wH5sF7qoZXeoJP5xgLyDfFMankjwSKYr49/jMnLboOBoI9K7xSUuP7XYY9mTv9iLbUbVOaSZrVZsmanzfae1Eu9tfKt3zfw6d5/SC9wkUa4QSvdqZOBpo/fnlwOQN1IaBjdlON5h3WJozx8WROV1imsSCkRQOdel3B072lSE9N1i0WrVh46mspLb3zPK5OHMuC6S1i0ervLY9FDJ53RyGKTiYcXfU+rRtU5m5/nWAClcbE+unsO6Ul91EsX16ZuZG3qRupGmogNicNgzKNE2v4dRQRF0i7hCjae+81tsXmrHiqtUzhLbmY+kwe+zrvrXuDV7x7g+Ts+5dDOUzb7iYwcZILtjfngbAPGStqMAlo0sqCwmBdeXsqn793G8+P68vArC2yOa+9c0hGd00sbD6alMXbRUtrUr46hxMlDfj6ki57SRL30cE3qX+zLOULQxQk2yqRYFvNn2lryjfkMqj2ap1u9wvsHXya9yLW6pacW+oveqYwnduKLmU+cPZFWmtubtuPdHv14rNNVrhnQh09yg8p8kluSz5v7v6RGRG0ebf4ccaEJtjvpgI85FX0KKaURGAhMBM4Cd1iuQ8BVgPVdS6rloy1npC1npcJNaHXdG02Sl6Yu5+ipNKY+OpBmDapp6hdomU/MYwTx/g39mDNolPMnun1YLwM18wnAvpw0Ola+ijGNHiaIYL3DuQilj9pReue7uLo+N2jXTJNJ8uJLS3n5k7JK5JaNr5TcAedPdWenFSKAF6++nlEtXVTW1cX4wym20uitj1XCOvNEiymq7I6XorTO94kRxTz1/m08+rr2vzF3ZD0B7Vr556bDPPjkN5pNJ76ik85oZF5xMZsPJgHQq2FTmiVo3YAoBx864Q2BkfkE4ExBVR5tPpmxjR8nVNg2alWLqMmA2iN5tvUbhAW573fg73qotC5wOZuUztN9X8OQV0iNek7eV8vA1SVcTyWl89Ib3/PBN+s09/FE5hO9tHFr0mlm/7UdgCbxlQkLcmKNzYd00d8zn5w2pJJoiOeSuA7UjWxos32JLGFL+p8sTvyGYlMRE1q+Qu3Iei6Pyx+0UE+9U8YTBwhk88nMLdtYsGs3j15+FU9c0dUlY/ryZhoo88m2zL1M2fMRcaGVGd/8eaqEaduE9jT+IBbeipSyWEr5upTyUillhJQyXko5BDgONAPSpJTHLM0PWD5elENNCFELiAYSpZROpIdQOIrWxUGDoYhJzy8kN7+Qt58aSvXK2u5HvmY+cQVfb9pOvdg4vhs0isoRTuqFD+tlIG6uASxN+oUvji6kfcIV3N34Ea+q421F6aN2lN75P+7YUCsqKiEsy0id6nG89dRQ4mNt3w99ZVMNnJ9nRuSH0DAugf91682IFm2cC8YHdTLQ9HFn1gFWJq+nV81BjKw3xuvK0SlNVFrn62Sfy+XzZ+bQ4dqW9BrdWXM/d5lPtLL3QDJhWUbq1oznuk5Nbbb39LxSL2IKwphyTQ9mDRhOrWj95zal8aeSdHrpYrEsYcGpX2lZ6VLub/qUTTNJiiGRFcmLCQ+KZFKr/xEfWrnC9s7g73qotC5wSTqcwtj2T7Phx+0ABAXb3h7Vsyzdxn+OcmJnCgDNG1TXHIe70ct8AlDdFM2SYbfwTo++BAknxlLmk4vQc554Kj+IRMNxm+0EAolke+bfzD35JdnFmTzW/AUaRtl+/2gv/qCFeumdMp44SKCaT0xS8szKVczbsYuHO3ZhwpXXOD0m4JOLhKUJdPPJ3uzDPL/7AyKDo7iiiov+T7gBfxALH+NGIAyYU+qxXy0f+5TRvu8FbRReTNq5XCY+t4CzGXmEhmh/O+FLi4Su0M2/ExMZu3gpDSrF8e2gUSQo84lH8Qad/CF5LTOOzKNt/OXc1nCc3uGUidJHp1F65+XofZoboAphdGxdj9cfH0xYqO3TWYFiPik2mXhg4XI2JJ7gje59GNa8tXPB+KBOZuZFelwj9dJHieTTo3NZnLiKa6v1ZEyjh7zOlKk0sVyU1vkIP325jh1/HuLe54dQtabNEuzn0XNjzcq40Vfz8sMDuKxFHZttPaWTem6uFZSUMHbhEmJCw/l6wAjiwyMcH8wN+ugPm2x6syb1L947OIsmMS25v8lTFRoyi2Uxf6X9xpLEbzBJE0+3epU6kfXdFluA6qHSugCguLAYU3oG1w+7nBlrnyEiKsxmH701slfXlsx67Tauv/Ki/eCL8ETWE2f6gXP6mJ5v4KO/NjKgaUtevPp6x4MAZT4pA73miXuyD5NoiK+wjdV0YsUojWQVZxAWFM5jLV6gQVQTl8flx1roVr1TxpMLEAJCNDgdIXDNJxKYvGo1323fwehWl1ItKtrpMQGfXCQsTaCbTw7nnmT8tv+xMnkJgNedXrPix2KhG0KISmU81g54E8gA/lfqqa+AQuAhIUTDUu0TgGcsX37irlgVtrFn4nP0RBrjHvmapFRzhrXgIG1/94GymWZl48lT3Lt4GY3i4vnghv5Oj+fLehmo5pMVKb/z0eFv2XB2jd6hlIvSR9sovfNt9D7Nvf9QCq++/SNtW9ThmXt7a+rjS3rpDEVGI+MWLufPpJO8dX1fBjdr5dyAPqqTgWI+AZh9Yhkzjy2mXfwVtIj1vjJLgayJSut8Hykl7977KcEhQTz8P/vKWum9sfb2Oys5nZrF/x4fTJ3qtk0zgWA+2X82jfsXL6N+pTi+7DeMyBDbJVnKRZlPykVPTVyftpm39n/BhrRf/rOhVhYmjGzL3MSSpG/IKErjiRZTaB7jpGm3AvxVD5XWKQASdxylTqNqjBzXQ1N7ezRSK5rL0q3ez44DSTw/ri+XNqtts70vmE+c4Yt/tjDj73+4/ZL2PHp5F+cGU+aTi9BTE8syn1j3Ga0a2TauI8Pr3sYjzZ6leWwbkgwn2ZaxkZP5R90Sky9roV56p4wnF1C3RgIvPzyAYGU+qRAJvLB6DUNmfcPZ/DynxzuPjy4SWgl080laUQanDHFUCavGpFb/o1F0M71DKpMThqo+LRheyGohxDohxDQhxGtCiKXAP0A4MFRKedra0JK66ymgMrBZCPGREOJdYCfQBHhbSvmX578FRWnsPZkWlmXkufv78Ox9vXEmy6EtfNl88ueJk9y3eBlT/1zn9FiAT+tloJpPfjnzJ2vPmm+HbeM6urUut6MobbSJ0jsfR+8Ntd/+OMinM3+jd9dW3DVM2wKZr5hPnNXKwpIS7l+wjN9OHiO7sNCpsQCf1clAMp8sO72G8dteZU/2dsD7Di4EsCYqrfMDko+l8vGT3/Db8q1299VzYy03t5BJLyxCAG8+OZSYKNv3XEfXZe1FT438OzGRx374icuq12RQs5ZOjaXMJ+WjpyZuTN/B8tP7bbazauXurG3MO/kVyYZEHmg2kbZxHd0Wm5/qodI6Bfv/OcK6ZVsYdt/1dmUI04Krs2gWFRt57sXFpKbn8PoT7jFnenoO6aw2vvHbehbu2s1jnbpyQ0MnM10o88lFeJP5RCIRCC6Lv5yb64/l7sbjubZaL3JLsvk5ZSlfHvuAr49/fN6YEhOi/xqwF6GL3injyQXk5RbQ7YpmvPBAX7ee3Ab/MJ8k5+QSkhvEU1dezXNXdXN6TMBnFwmtBLr5BOB0QSzBIpiHmk2iTaV2eodTLn46edKDhUAscCvwONAWmAG0llL+dmFjKeWHwCBgD3A7cC+QAoyRUj7pqaAVFWOv+eT08XT6XtOGx27XlubQ09rpDbr5x4mTHDmZDsAtrS+jUpiTuuTDehmo5hOAYlMT7mr8KOOaTCA8yIl02W5CaWOFKL1TlIk9mvndgk38tGonXds3JjTEdskdCBzzSUFJCWPnL2XtSfNJpZrRMU6N56s6GUjmk1OGZBIN8TSObs7EVq9RLbyGbrGURYBqotI6P2HlrN/4dfFmh/pqNZ+4ozxd0ukMnpu6lLo147lrWGdNfbx9cw2c18hVhw4zdPa3zNu3y6lxAGU+qQC954wVlRmwlhiwmk8qh1U9//jdjcfTKNp2CQ5H8UM9VFqnAODLid8SJAS3T9CWnVjPLJpZ2Qaefm4hQgjenjBMU9UGT+mjozirjc/+vJoXN6xh3cljLorIdfiDLnqD+SQ2pBItYttwf5OnuKvRo1xR5RqyijOZf+orph9+nZ+SF5FWeAYT5v/rtSLq8WzrN+lfa6RL4/FhHdRF75Tx5AIyMvOZ/sVaenZpybP39SZI49HtQDWfWIkyhXH3ZZfzQtfurhnQRxcJrQS6+eRsYToTdr5HiiGJsU0e54rKV+sdUrn4sGh4DVLKN6WUHaWU8VLKcCllYynlg1LKxAr6fC+lvE5KGSuljJZSdpJSzvJk3Arb2LORNnveX8xd/Dcje7fnvlFdNfUJRPMJQPOwKrxw9fV8PWAEsWG2a8lWiA/rZaCaT84UnuPdgzNpFNOMB5o+TUSQ99UvV9pYNkrv/AN3LRbaVUrgo1U8OHUexSVGzX0CxXxiHiOIbvUb8dst99CvsZObKD6qk4FkPgE4XRBBbEgcjzV/gXpRjXSN5UICTROV1vkXpvQM+t5yFU++d6vdffU0n+zYfYoJzy/g0/l/aB7bXnxRI/emniUkN4gWlasyrv0VTo2lzCflo7cmXmg+ubDEQJcq3bir0SOMafQQdaMasj97FyuSF3Ms76Bb4/InPVRap7By5mQaS6f/zPXDOlG9ToKmPnpm0Uw6ncGzUxbz2YI/KDG6J+OXL5WkM0rJN3/toMRkokpkFB1r2i5DVC5KF8tEL02sEhZP1fAu3N90AmMbP07LSpdypiCZOSc+56PDr/FH2q8kF5hv2VadrBJWjdsbjiMqOJpeNQcxsu4dLo3JF3VQL71TxpMymLf4Hz77+nf6XtOGh265VnO/QDafvLJ2HV/8s5k723ZkytXa6uLZxEcXCa0EuvkkqziXp3dN43DOPm5rOI528U5Oit2IL4qGQuEp7NlI+/iLdXy/cgdjhnTmlgGXa+oTiOaTo+kZPLzse1pXrc6s/iOICVXmE0/iDTr5R9pW3tr/BQ2iG/Ngs4lEBkfpHdJFKG1U+DN6m09KSkyYUguJigjl+XF9qK0hVTJ4rpwA6G8+2XroNDtTU/ig5wB6N3KyfKeP6mQgmU8O555k4s53KTIV8mizybSMvVS3WMpCaaLCl4kMlvQY3olrB7Z322u4+lQ3wNYdJ5FnC4mODOPay5vabB9IBs0RjS7h6c7XcndbJ8urqE22ctF7zljafCKRhAdF0LnKddzX5ElG17+LS+I6kGQ4wXcnPmPm8WmsTFlyvn2dyPpui0vpocIfmfPm9zw26B1SkzI099HTfLJ7XxLrV+0DoEk923+TntJHPbUxJDeIqdfewKz+I7i0mhMZFJUuloknNTFEBDOo9vU82eJuJra8l7qRDTied5i5J7/gvYMvsSn9d84UnP5PH4kkNiSOMY0eomZEHdalrmRt6gqurnYDtzd8wKXxKR3UhjKelMM38zbyyZfr+PG3PXb1C2TzyWvrfmfG3/9wx6XtefFqbaUWbOKji4RW9DSf6D1JAigwFjJ592esTF7CvuydeodTIUo0FIGGMGnXK3vMJ+98tIplv+5k/9EzmvsEovlkzZGjPLL8By6tVoOZ/YcTHRrq3IA+rJeBaj7ZmL6D1/d/Tp3IBlwS10HvcMpEaaPCn9HbfAJQIyiCq9o15q0nh1Ip2j2lt/SaZzqrlfnFxdwzbwk7UpOZ1nMAvRra3nSsEB/VyUAyn5wuSOWpHe+SVniG+5s+SYOoxpr6NYhqzMi6dxAstJWuchSliQpfZfEHK9i/7TgPvDyCuMrRdvXVc2PNyl3DuvDq+IFc1c52NqRAMZ+8+ft6fjxygOe6duemVm2dGkttspWP3nPGREM8CWFVuSz+ch5qNomb6t9Ds5hWJBlO8sXR95lx5B02pf+OwZh/vk+rSm2Z0PIV7mk83m1xKT1U+Bv52QYOrDPvW4RFaF+b01sjW8Yn8NUrt3LvyKtstg0E88lLK9eSUWBgZv/hNI7Xlr2mTJQulonnNFHQq+bVNI9tyJaM3Xx6ZB4Td33MX+fWkW/MPZ/dpDSRwVHc0fAB6kc1Zt3ZlSxJ+paVyUtYmbyEDglXcl8T11ZEUzpoG2U8qYA5i/7m1G7zxlnntg019wtk88kbv61n+l+b2JKS5JLxALcuEnpqQy2Qs5+UyBI+PfoLR/IiCAsKp1eNQQR56a1HiYZCUT5aJz8mk+Sdt1eyZe8pAGpWraSpXyCaT345fITxP/zEpdVqcHnNOk6P56ubahC45pN/0nfx8NaX+Sd9g96hlIvSRoXCjDvMJ0nJmUx+eQm1q8fx1oShRITbvo97clPN2b7OamVecTF3z1vC7rQzTOs1kCbxlZ0az1d1MpDMJxnF2UzY+QE/nV7EyXzbtdoFgpiQSnSu2o1nWr1BfKiT/0dsoDRR4YuYTJJ3xn5KVGwE414eYXd/ezbWtGLPxtqsL9Zz+MRZXnp4gFed7HamHzinkSYpeXLpCtYcP8Ir1/VkWPPWjgcCapOtAvTSxOrhVehcpR33NXmGMQ0fpn5UY47nHWbm8WnMOPI2e7K3kVmcDvxbYqBhVFNubXA/RlnCpXEdeaTZs7rErlD4KjeP78OHPz1JcIi++xZaNfLoiTR+/mU3dw7tQv/r2ths7+/mk7N5eYyZtwiTlHw9YCQ1omMcD8THdNFTeEITS2QJj26byit7P+bNA1+w6ox5vTTJYDYTWcvOWQkiiM5VrqVZbGtyS3I4krsfgAKTgd/OrmJZ0hxaxF7CI82eLdO04ihqXlgx3rn762Vc27wB704czoM3qbI7Wnhnwx+s2H4IgEur1SBIuGBcNy0SQmBkP/EW6kRew8A6o7mn8XhChXYHcUyIto1rVxAIolEoQzlaVF3TpfBv7E2JbM8CYViWkes6NWX+O3dxTccmmvoEovnk54OHuP6zL/nt1HGnxwLMeummCZK/bayBd2jkmcJzJBriaRDVhMeav0BsiLaSG57EV7VR6Z3CFvZuprnDfLJzTyIvvb6c1k1q8tpjgwkNsZ21wdPmE2dwVitzi4q4c+5inl//C0cy050PyI066U4CyXySbyzgy+O/I5HEh1amR40B5baVSA7k7GHOic+QmJjU6n80jHIyO44NvE0TldYptHBiXxLfvraUq/u3o3pd+w1aWvXSHTpZUFjMMy8sIt9QyJtPDqVynO0SlYFgPik2mXh48Q/8kXiCQc1aOR6EFR/TRn81n4SIEIbX7c2ElvfwWPMx1IqoxuaM3Xx17EOmHXqN3Vn/Gk6sSCQ1I+owptGDBIkgvj89j4WnvqZ2ZD0mtnrNLWVdPa2FSusUnuDQn/uo36wmQ+6+TnMfd2XR1Mo701ezcedxJt7dk06X2C6z5anSrXpp44nMTO5ZsIS48HCmOFuRwY37ka7G3zTRKE1szdz7n8ck8j9l6KyYMLEnawefHnmLYlMRQ+rczBWVryZEhJJvzOXPtHUsPDWLvJJcakbUdWmc7tBCf9E7ZTzRwMZ/jrLkh63cOrAT947s6vbX8wfzCUCTkMosGnoz7/XoR2iQC/6rKfOJw3jDxhrA+rTNfHJkLm3i2jOu6dNEBNn+eYSKUHrVGMTEVq95zIDibYuJCoU7ccfEx8r2P45x4NgZpj4yQNMECDw3CbLiDbp5JjeXkNwgejRozOwBI4gKcbLsDqhT3XbgLRqZVVKZOpH1ebT5ZOJCnUgL6iaUNir8FXec5LaidVNtw8bDvPXBzzSpV5XqlbWdzPKVTTVwjflk4T/mErgtK1flqjra3lNUPKhvbbBBYJlPwFxioHVcb4bUuYmWsZeW265EFrM5408WJ37DmcJkHmvxAu3jr3RrbEoTFb7I/Hd+ZMpdn5Ga6JiJT0/zSdq5XJ6Zspj42Eievrunpj6enFfqtcFWZDTywKLl3LdyKYDz53h9TBv9baMNQGKia9UONIquy59p2/jg0Gxe3/8ZPyQfpFgWlXlaOz60Mnc2epjY0Hh+PfMja1NXsiHtF5YmzSEqOIaJLV+jSlg1l8eqtFDhb2xauZ2/Vu3ijgkDaNa2nuZ+epbcMRpNTHlpKSdOp/Pq+EHUrGr7XmWvPvqaMXNPaip3LVjMM7+tdjwAKy7WRaWJzlOW+SS1MJm92Tt45+CLmKSJvrWGUyvCnFm80FTApvTfmX/qK5ILTrk8HqWFZaOMJxp5/5Nf+H7lDu4c2pm7hnXR1MeZSY4/mE9OZGby7oY/GNSsFV/0HerVG2mgzCee4ueU9bx7cCaNY5rxSPPJxNowk5gwkWQ4SWxIHM+0ep0m0S08EqcSDUUg4Y7FQQCDoYiJkxdy4nQ6rz8+hLbNa2vq50tlBMB1uhldEsZVderzVf9hXq2Z/raxBt6hkbuzDjFlz3QqhSYwvvnzblkcdBaljQp/Re+TagA/rd7F7WM/Jyk1S3OfQDKfmMcI4vmrr+eLvkO5spZrTyu5Cn/TSL31ccGplby271P25+yy2XZv9g6WJX7HnqxtjGn0EN2r93VrbEoTFb6GscTIpoV/YErPoNP1rRn38nCCglx3+Kw07phfHjx8hmdeWsRbM9doHttTm2vO4oxGGopLMGZJKoWFs2DITfRu1My5YJT5pFw8dcr78e2v8cKeD3j/0Cx+O/s3YC6nk2iIL7PEwGXxnagaXh2TNJJRnE6wCEYi+fvcBhYnzsaIkUeaTSYhzPW6pbRQ4W+8fdd00lOzmTR9DNGVtN9f9DSf5BuKmPj8Qr5e9jcpae45VKHXHNJRtp1OJjutkNCgIO5u25EQZw7FK/NJmeg5T8wqNptKSpsxBYLs4kxmHZ9OXGgCPWsOOv+cURrJKcn2eJyBjDKeaERKeHvaz6xYvYu7hnWmcd0qmvoFuvnks783M3HFz3St24DvBo0iIcIFNz9lPnEYvRcOrWxI28Irez8lIjiS8KCICtsapZG/09fz7YlPOVuYwiPNJ3NN1Rs8EqeaQCkCCXeZT3JyC3jqmfmczcjhrae0pUaGwDSf/HTgII/98BMda9ZR5pMANZ/syznCC7s/JDI4ivHNn3fL4qCzKG1U+BL1m9Wgaq14TW3dZT6xRzPzDUWEZRl56OZrefTWbpr6BJr5ZPzSH0nMyebL/sPoUEObobVclEZqQk99lEj+Tt9Z5sm20lgXHY/kHeCn5MUczT3IkDo3M6reGLfGpzRR4as0bVaNQWOu5dkZdxEeoX3OobdWbt1xkqxjWQQJ4bZyrr6qkSIHhIBpPQfQq6GTJceU+aRcPKWJu7MO/edrq+HkQj00YWJLxl+8sX8yp/KPMarenVxfvT9RwdGYMLI982/mnfySfzI2uC1WpYUKfyInI4/XbptGbHwUTdrUsauvnuaT1LM5zPvmLwBqVq1EeFjF93FfmEO6Yu7YrWojnuvanTe69XYuK5gyn5SJHvPEIXVuYEidGy4yY0okAkFKQSIpBYk0j23tlWuqgYIyntiBlPDGBysZN2UeRxPPae4X6OaThbv38OCy72lVpRojWrRxyZjKfOI43rCxBrAtcy/jtkwlrSjVZluJZG/2DhYmfs3WjI2MqHcHN9a/2wNRqgmUIrBw10ZaRmY+T0yax7Tvfic9K19zv0A1nzxeynwSHuwCvVMba5pJyYvVXScP5Z7gmV3vsSd7O1lFjqVCdzdKGxW+Qlh4KA++MkJze7031KyEGwU39uvIXUM7a2vvAwuHVpzVyvR8A7fPXUhqXh4z+w/n0mo1nAtIaaQm9NZGKDutMphNJxJJRFAkVcNr0K16b+pEmcsxda3aw+0LjkoTFb7It68t5aPHv6Zzz0t4dc6DxMZrOxwA3qGVgzq14o0nhjCiVztN7QPBfJJXXMzd85awO+0M03oNpEeDxs4Fo8wn5aK3Jl6oh7kl2ZwpOM0Hh17hYM5uetYcRMPof81HB3J283PyMjKK0twWk9JChT+x7+/D3NbyMXb+ddjuvu4o42qPPsYXBfP5Szfz/Li+CBuS4gtzSGfnjmuPHuOd9X8wrEUbnr2qm1NjKfNJ2XhSEyOCw6kalkD/Wt3oWrXDRXookZikCQlEBkcTE6ytjLHC9SjjiZ2YTJIDm821oLq2b8yIXu019fNl84krNtJ+OXyEQbO+4bMdmwEX1B0FZT5xAr0nSVaM0mTz9FppTuUfY1nSHFalLKdLlW480HQiEUHu/xmqCZQikHDX4mDq2RxWLt0OQMtGNahVreIyW1YC2XyyJy2VQmOJ0+MBPr2xFojZT07mJ/PWgUWYMBEbEvefhUNvQWmjwhdIS86gc89LubrfZZr7eMOG2rTPfmXFL7sYO7Iro/q4b77pixtrAGfz8rh97gKyCgu497JOzgUDPq2RnkRvbayIymFVGVlvDA81nUSnyleTV5LLsqQ5vLZvols32qwoTVT4Iss//YVXbp1G00vq8vaS8cTE6VtSwB5WrtnN+i1HGH97d67uoDKfWMktKuLOuYvZm5bKx70Hc229hs4Fo8wn5aKnJlYJiyfREH9RiQGAhae+xihLGFB7FGFB//6si2WR2+NSWqjwJ/It2tVrdGeata1nV1+tGumOuWS+oYi5CzZx/ZXNGTf6GpvtPb3m6gjOzh2nb9zErC3buOeyy7m/3RXOBaPMJ7pSYCzk97P/kFGcTf9a3bg0rvl/nhcImsa0Ij40gTMFpzlTmKxTpAplPHGQsCwjvbq24okx1zPsBm0Lmb5qPgHXbKQdSU8nJDeIBpXi+Xn0GNpVr+l8YMp84jDetHCoNXUyQGZxOulFZzlTcJoWsW2Ycsn7HjOfqEmUIlBw5ynuqDzJq+MH8uGzI6mWoM15HKjmk1d//g2ARnEJVIuKdnpMX91Yg8AtvZNoiGdkvTt4uNkztIy9VO9wLkLposLbyUzN5tDOU4x7abjuNbpBu2ZKCW++v5J1fx/isduvp9+12rJGBpL5JDknl5u+nceTa1c4F4gVH9XIQDOfXDhvrBVRj6F1b+HR5s9xeeWrKDIVsi51Je8enMKvqT+RUpAEQHhQBKPqjaFmhH0p0+1BaaLCF9mwbDOTBr7OxtW7yc1yvUHEXrTqpMkkmfrKcg4cS2XqI/1p37KuW+LRSyOdIbeoiDvnLWbNiSMczcxwwYDKfFIeemji0Do9ubXBYABOGeLOr5dayw1kFqdzKv8YVcNrEBea4PH4lBYq/ImwwnxuHt+bSdPH2DWXBPeYT7Qyf+lmlv64jdsGXcGg7rbXkTw1h3RGG52dO079dS3LDu3jgQ5XUjVSe5a3MlHmk4vwpB4ezD3OjCPzaBRdl1sbDKZJTE8AQkUYzWPbcEONAcSEVOJw7n6PxaS4GGU8cYLXX/+B9VuO8NRdN2i6iYP5Ru6oAcUfzCcAQXmCsOBgvhs02nn3PZhv9j66UGhFmU8qNp9YJ1BNY1oypM7N3Fj/bmpE1OZQzl5+OL2AApPnFkjUJEoRKLjLfFJSYuLFV5cRHxvF+5NGEBer7f4XiOYTgLC8YGb0GcKCITdSN1ZblpgK8WG9DFTzyfuHFnOmIJn7mjzJZfH2nexXWcEUCsl742YQVzWW/rd1taun3uYTo2VT7Y+tRzAUaD+lGkjmk5TcXIxZkrjwCD7rO4RGcU5usPioRgaC+aT0QYREQzz1oxrTo8YAHm0+meuq9abYVMSfaWv54NBUliXNJbs48z99BtYeRdeqPXiyxUu0iL3EbXEqTVT4Irv/PMjnT84CoF7TGrS7urmNHmb01smCwmImTl7A6dRsXn5kAOFhtu/hvqKRzupjTmEhDy/6gcScLATQvLKT9yZlPikXT2piWFAoYUGhdK3ankG1ewD/NZ8AhIpQIoKjCA8KJyG0ssdiK43SQoW/UJBXyGu3TaNa7QTGv3mT215Hq0bas/b6wSe/8Nf2Yzx11w1c1sK28drfzScSmLj8Z4Yu/pY0g/YS8OWizCcX4Uk93Ja5l5f3TqdqeALjmtzMUy3e5LEWL3B34/G0rHQpR3L38/vZVRSZCj0Wk+K/KOPJBQij1Ny2pMTElJeW8uf2ozx9d0/NJ9HA8ewn/mA+OZWVxY3fzuNYZjqf9x3KoKYtnQ8M3LpQ6K8bauAdm2pWyjOfVAmrRq+ag7m/yVN0r96XfGMeC07NZPaJT1ifthowL0a2rqQ9jbozqEmUIlBwl/lk/8EUJr24iNrVK/HexOFER4Zp6heI5hOTlEz6aRUJEZEsGHITTeJdsHjkoxtrEJjmk6ziXCbunMbJ/KPc1egRrqx8raZ+QQTTvXpfnmgxhahgF2TMqQCliwpv5vD2EzzZ6xUWfrzG7r56b6oVlxh55rlFrP37EAAxUe67z/qq+QSguima9jVq892gUdSLjXNuMB/VSH83n1gPItSPqk3/Wt14qNlkBtUeDcAfaWv45PCbLEycRW5JDhLTf/r0rjmEa6r15EjuAU4bTvFA06e5qkp3t8WqNFHhq5jSM7jrmYG8/PX9dB96uaY+eutkVraBJ5+Zx6R3l1NYpK1EaaCYT8xjBPFAhytZPvwWutap79xgynxSLp7SxCJTMWtTN3E8L4neNbtyTdXLCUJwymB+7yMQNIpuRkJoZVIKkjhdcMojcZWF0kKFv7Dv78N8+fw8ru53GYPG2C5dUxp3aKQ9hxhemrqM+Su3cuD4GU19nKnWYA96aWOxycSJxEwA7m7bkS617Suh5G78QRM9OUfcm32YZ3e9y9rUTRgxUT28NnklOfxy5nsWJX5zPvOlQh+U8aQM7Nk8Ky4x8sKLS/hn9wlaNKxu1+sEsvkkLS+fW75bwNaU07x3Q396NtRWk9UmPl56JzMvMiA31UqTZPj3pGIQQXSr1oexTR6nf60RhAaF8cPp+Uw/9D82pK0hq/jftKFXVb2e+5o8yQNNJxIqQt0ep5pEKQIFd5lPduw+xXNTl9KkXlVuGaBtYRMC03yyPTmZm+fMJyQoiHlDbqRNVfveb5SJj26sQWCaT/KNBp7Z9TEHc/bQo0Z/gkWwpn7nilKpFl6DZ1u/QYOoxm6NUemiwpvZu/EQJpMkrnI0oWHa/n6s6L2pZm5rpGv7xix+/x4uaVbLZntPzzP11spjGRncMW8hESEhfDdoFLVjnLxn+6hG+rP5JCwolNsbDOHZVuO4s9EwQkUIy0//yvTDrzP/1EzSilIxSiPwr+EEoHOV6+hXazhHcg+wKPFrvj05g20Zmxhd/y4G1B7ptniVJip8lddvn8buv48w4YPbuKpPW0199NbJtHO5HNhs3mDvc3Urala1fW8KJPPJ/H92cSwrk8/7DqVLHSc32ZT5pFw8pYmphed45+BMgkUwtzccwoh6fagcFkdOST3aJ1xJv9rDqRQaz5Hc/RSZtGfMcwdKCxX+wqIPVrJp9W7umTyEqjXtM7nraT7JNxTx6fRfKSgsISoilAQN5YLs1Udf1MaYgjBGtbqUL/oN44paTpTqU5pYJil5sR7TxJSCs3x+bD6Tdr7NEzv+x+Pb3+L70/NJMpzwyOsrysd9u/Q+TlhWMUVx2javi4qNPPvcIvIsh0lDQ4IpLjFq6hueY6Iw1n7/T2guFMfY3c3pvlZCcgUlMdqzw5RFblERd81bzLjrrmBD4knnAvrPwCEQo+2kg70YcsOJjHF/iqbMvEjioz1bXzclL5aa0drfDLkagUBa/iUa4hlepy2Xxnfg0riOAPyR9is7MzezP2fXRX1axLZhWN1byC3JpmF0U55r8zafHHmT0wb3uvtPGKrSIDLNra/hDgpNIWoCqHAb9ujnpi3HePTpOexMPWvXaziinXrqpis088DZNG76bh6zRo9gfKerGLtiqVPjAW7TS09oZSDqZKGpiOd3f07TmCKM0nheA8vDhJHN6X+Sb8yjd80hPN5iCt+d+IxN6b+7LUZv00Wld4rSVAox8unaZ/h+5nq+fXel214nONuA0c4a4Fo4vD2JzBwDb08YxoMvz+fwyYq109PzTL218sDZNMbMW8TsG0fw3aBRjFo6l9T8PMcD8lGN9LQ+ekobi0zFNIquS9XweL4/vZZtmXvZkWmu2103kjI1sUl0C26qfw8GYz4b0n4hyWBec1iRspickix61hhE5bBqfH18ulti9pQmKq1TuJL8bAOTB/yPd357kUffuJH9246TfibbZj+RkYNM0LbJYI9O2jO3rGoK5bE7riczO5/7p8wjI7viVPqenFM6o5HO6mOGoYA75ixk9s0j+bLvMMb8uIhNyYkOj+cOfXSnNnpSFz2liSkFZ3l655tManU/I+v2ZXDtHpRIIzEhUZikib3ZO1hz5ke/KjGgtE6hN2/eNZ3W119GWkqW3X3t0Uit2KOPYVlG3n3hRoKDg3hw6nybGcLs1Udf08aCkhJun7OQb28eyVf9h3Hb9wvZeua0Y4EoTSwXT2miUZowyiKSDObMPjkl8dSNzHT767oLf9E7lfGkAuzNfBKWZaR29TjmvnUn13VqqrlvIGc+KTIaef/XvyjONBIdGsp97ToRJJwf19czn4B+J7r1OtVtXShsG9eC+5vcxM0NxnJpXEcO5uzh86PvMf/UV+dNJ9b6pRJJ1bDq3FR/LAZjPsuS5jL7+MfkleQyoeVUOiZ0cXvc/iAEigClRLv22HMyDezTzz37TxOcXkxCpSgevOlagoO1vTUJxMwnxzMyufHbuTy+5ienxzqPj57qhsDMfFIsS9iXE8RpQwJjGj3E4DoV1xk2YWJ31jYWnZrNzszN3NxgLEPr3OLWGJUuKryVzNRstv62n9EP9aRBC9tZQ0pjz0k1e7BHLzMy83li4jwMBcW8P2k49Wom2OwTaJlP9qSmcueCxRiKiwkJcsFSh49qpL9mPpmydxpP7HidmccXnzedCASJhvgyjZiVQuPZlbWFiOBI2lRqR8Mo8xrNmYLT/HLmB1YmL6FWRF1axF7itpiVJip8keKiEt644yPCIkIZ9cANbnkNd2Q+yc4pYOILC6leOZb3Jg7XVJ4uUDKfpBsM3D5nIYk52XzcexDRoU5mCVanvMvFU5qYVZzL5F3v8s2JZWzL3Ed2cS4Hco7x5bHFLEqczbki+w73KBSKislJz2XTwj8AqN+8JkEa1y6taJ1PuiuD5tz5m2jVuCYvPNAXLVtv/p755Fx+PrfNWciZvFxmDRhOu+o1HQ9EaWK56LWGmmiIp3lsG8Y3f47I4ChdYgh0lPHEBvbcwAHyEnNJy8hl6sMDlPnETgbWacmkLtcxredAwoPtS4FdJsp84jB6iEJYUCiPNLuNya0f4PrqnUkuOMu7B2cyde8sdmVt+U9b68JiRFAktzS4j/iwyvyWuoq/09ezK2sLixJnsydrO7c3fMDmppwrUAuKCl/FXWmRwX79vLp5fW4d2Innx/XVbEAMRPNJck4uhvQSIkJCmD1ghGtK1fnoxhoEpvkEQAIpBYVUDat+3oxZEcfzD7Ps9Fx+Tf2JbtX7cHejRzX1c5QThqpKGxVeycePfUVuloEpM+/1ijTJYJ9enjmbzROT5gGCD54ZQVys69MlW/FV88mO5BQGffUNp3NzCBKCOjGVnAvIRzXSX80nx/P+e0rfOi9MNMRf1HZb5iY+P/oe35z4lA4JXRhS92bqRNYHIKs4g7WpK5h94mMO5Ox2a8xKDxW+yKmDyUzq/z++eGWZ5j72mjTdoZV79p1m8tQlNKpbhbefGkpEuO17eKCYT87l53Pb3AU8uOp78ortm6uXidpoKxdPaWKJNLLs9BreOvAFz+x6hxd2f8CKlN/YnvnvSXmtJVoVCoU2GtaN5aOVE3jhi3uIjHbPPcsd+vjHxsN89PmvdL+iOY/e1l1TH383n5zNy+P2OQtJy8+nWWUn368rTSwXvdZQzxXFUj+qCQ83e4aoYCfLfyjsRhlPNGDPYqDBUMTTzy5g75EUpj4ykG6dmmnuG+jmk4W79/DKr+vo16Q5M/sPJzYszPlBlfnEYTwtCkWmYgzGQoJFELOOL+F/+2awIW0LeUZDmQuJAG3i2lMnqj4CgUkaiQ0xLywfzt3H8tPz2HD2FzomdOGy+MvdHr9aUFT4Kt5iPvl5zR4++XIdva5qyTP39tLkwAffNJ+4QjejDKHEhIXxce/BDG7WyunxfHVjDQLTfCKRfHZ0PlP3za6w3E5p0grPcCLvCEmGE7SNv5ypl04jPCjCrXEqbVR4G1lnc5g85A1i46J45dsHiIm37/SLN5hPTiWl89Tk+Xy/dhdZOdpeI9DMJxIIyQ1iwpXX8P2IW7m0Wg3nAvJRjfRX88mFWI2U5c0ZN6f/wadH3qJ+VCOurdbr/OMFJoPbS7NaUXqo8EX2bjpMcZGRqNgIEqprM/G503yilX+2HuflN76nTbNadG2vzaQfKOaTtLx8/jmYBMDQ5q3pUKO2U+Opjbby8bQm5pTkUSz/LfeQaIinY8JVPN3yNeJDK3s0FoXCnzm68yTTHptFx+ta8uaiR+w6zKD3XHLhsi3MX7qZ0X06MLxnO019/N18kpKby4CvZrNgv9mIHhniREYwpYnlosc8cUfmfv63bwY1I+rwSPNniQlx8kCKwi6U8UQj9iwG5huKmPDsAvYeTublRwbQ6ZIGmvsGuvnkqy1befyHn7i8Zh3mDhpNtUgXpEJS5hOH8bQofHZ0PvdsnsyPyetINKT857myFhIP5uzm3QNT+Dt9PYPq3MjQurdQI8I8cT5TcJoVKYv5+vjH7Mna7oHoA3dBUQgxRgghbVzGUu0b2mg7V8/vJxDxFvPJnEV/8+U3G+h/3SU8daf2tM6+Zj4B53Uzu7CQMXMW8XdyIu/26MctrS9zPig3bqx5YnMtUDbYSmOUpnI32kpTN7IhPWsM5JYG91InsgGJhhP8nb6BQlOB22P0F21UWuc/HN5+gudHvM2hXacozLe/drLeC4YAh4+m8s1Mc6rnujXjidVw2i7QzCcACzbvJq+4mDmDRtOldj3nBlPmE03ooY0SSRCCVrFNSDTEk2T4bxkqgeBw7n6O5R2ic5XraBLT0uMxgm/oodI6xUVkZvL2kvFMeP82hMaTAe4yn9ijk7/9cZA77v+SNRsPaO4TKOYTMB9ieKjDlcwaMJz2NewrP3gRPrjR5ilt1Hu+eDCnkPiwyjza/Dkqh3m/BnkSpXcKZ1jx1TqeG/4OtepX5d3vH6dJmzqa++o9l5z++a/MXv43G7Ye0Ty2vfiaNhaWlBCSG0SnWnXYcOtYrqxV1/FAfFATPYUemrg1cy8v7/2EauE1eLT55IDLfKKn1rlvR94PCcsqpihOm+vNYDGf3HXPtew5kmzX64TnmCiMtd8TFJoLxQ7+7TjT10pIrqAkRtup24pYvm8/GQYDz/XsTniIi/6L5oZATIntdg5gvflHxti/cG0PmXmRxEe7/jSILVLyYqkZ7Z669mWRUZRV7nOJhnjqRmae/zqnJJuckmy+PTGDZEMig+vcRE5xNkuSvgUgtySHw7n73B3yfzhhqEqDyDSPvqYXsB2YUs5z1wDXAyvKeG4HsLSMx92b71pRJiIjB5mg7U1gcLYBYyXtb07t0c9Zc/4kLCyEq7s2IzY6nJw8bfdWR7TT13Uzr7iYe+Yt4YNhA3jlup4IAd/s2eFcUG7WS3/TSk9rZHlcqI9WKoXEc2l8R/rWGkpsSBwmaeLnlKVsSf+TM4X/vj+tE9mAJMMJt8XnJ9q4HaV1fsOuDQfYteEAQZUTiImPojC/kOIio+2OFtylmfboJUBMPnz4zEjSMnJ59LWF5BdUvODo6Xmms1rprE6eyMxk9Ddz+erG4cwcMJyHV/3AquOHHQ/IupDoYp10tz4Ggjb2qXUt/Wt145W9H3O6IPU/uiiRlMhiTuUfp2lMKyqF2Fdmy5X4gB5uR2mdohQmk2TphysYP+0u+t16FT/O/kNTP3t00h7s0clTSemEAS0ur8cNXVrw3uy1SBuS4sk5pTMa6aw+FhmN3D53Ed/ePJJZ/Udw2w8L2JGaYrtjebhhDukv2mjdaNNjzrgv5wjP7/6AF9s8yPjmz/PhoVc5W+jE79m/2I7SO4UTbPllF4/1eImXlzzFlT0v5cieJM199ZxLSgmff7qOorhggoSgSf2qHDpxtsI+gaKNp05ncc6Qz8z+w7l7xRL+TDrp2EA+pomenCvqMU/clXWAKXum07dmWwzGPI++thewHZ20ThlP7MRe88lHH/5CUVwwkeGhdL6sIWv/PqSpb6CbT9YfP0Hfz2dRGG1EAPUqxXEyu3xDgibctFBoxR831KzovbEmEEgkoSKEREM89SKzLiot8GvqTzSMbka36n04knuAnVmbdYrWJxYUXYqUcjtmIbsIIcRflk9nlPH0dinli+6JSuEI3mI++WzW73w7fyM5YUaCgwRGkzZdCUTzSZHRyEOLvueZ3tfxV5KL0sUr84ld6K2RVpIMCdSJzDj/decq13FVle40iDanGF+buoK9Wds5mLv3P/0ui7+cuxo9yj/pG/jmxKdui8/XtVFpnX8SnJvDm79MJPHwGV57YCYmjXoD3mE+KSo28uHHvzDlmSG8/vhgnnhzCUXFFRtoAs18kpqXx83fzuezkUN4u0dfrvnmMzILncz25GOLifDvaTZ/XVRML8oiOiSS2xoOZtbxJaQUpP1n3hgTEkvNCPNpVKPUbjJzB96sh0rrFGWx4qt1XDvqKu5+djCb1+3jzKl0Tf28QScBOjWuw6g+HQB49+u1NtsHygbbmdxcbpuzgG9uGsXsASO45fsF7Dp7xuHxfFUb/VUXrRzOPcnkXR/w0iUPMr75c7y2byK5JfrPXfVG6Z3CFRzfk8gDnZ8lD7MmxVeNJTNN29+XO9dftRCWZeTWMV25ffCVPPnmEjbvqdhkEQjamJaXz61zFvD1jSP4st9Qxq5YyvpEBw9o+Zgm+rse7s0+zN7sw9SNlCSEVSVEhASEEVNPrVOldhzAnvSO5vZGbup/Oa+OH8RtA6/Q3E+vsjuuSIvsitSPRikJyQ3iwQ6d+WnkHVxVp77TYwI+X3pHj3ICYBYFvdJESiRRwZGMrNeHUBHCKUPc+Vre8G9d7z1Z2wCoFelESjQXccJQ1SfSKbsTIcSlQGcgCfhR53AUbsCdZXfyDUVE5JiY8lB/xt/eXXO/QCy7U2Iy8dKKtZxIzASgX+PmOK3CfqCVnkTPNMpWDZRIEg3xdEjozO0Nx3FT/XtoEN2ELRl/8eWxD1ia9N1500mQZQpQN7IBo+rdSYksoVPlq3myxUtEBUe7LVZ/1EWldb5NcVEJK7/8lav7t+Oh10bZ3V/vVMkAGzYe5n/v/sTllzRg6iMDCQ62PcUPtLI7WQUF3DF3IXf8sMh504kVH0ujbMXT6ZQ9pY8bz21nVcoG2se3ZlidXtSPMpdfPWWII1SE0SSmJfWjGnG2MIVzRRWf6vQEvqaHSusU74z9FGmSjH/zJs0ld8A7dPKb+RuZv+QfRvXpwNgRV2nqEyhld5Jzcrl1zgKyCgvp4oo1Vx/URn8vMwBwPD+JZ3a9z69nflKmExsovVPYS056Lqb0DKrUjOPj1U9z3wtDCQpyfWk6d5SlW75oC4kpGbzxxBDaNq9ts30gaGN6voHb5izgaGYGn/cdSusq1RwPxMc0MRD0MNEQzx0Nx/FY8xeoH9VYlxi8AU9onTKeOIi95pM5X//Jqj/28cBN1zDuxms099PDfOKK/uCauqMAS7buJTEni6/6D6N/kxYuGdPXN9TA85tqVvQShpaVGtO31nXc3XgkIRbzCfybDQUgJsQcW7XwGrrEWBa+tqjoYu61fPxCyjKPFtYWQtwnhHjG8rGtJ4NTlI27anJbsUc/TSbJuZQcRvfpwNN334DWNc5ANJ9Y6VGtCdN7D+KN7n0ItmNRuExyQ9yml/62uQb6GTStGtimUjPubjSCOxo+SMeEqzied5jZxz/m6+PT2ZH5D/CvScWEidiQStza4D5Cg8L48fQC5pz8nNjQOCa3fosGbpyA+aEuKq3zcZZM+5nvXl9G35uv4s6JA+3u7w2baqvX7uXd6au4pmMTbh3QSVOfQDOfGIpL2HnEfKrpxlaX8tSVVzsXEPjcYqIVfzVnfnfyB5Ym/UKPGl14qOktDK/bmysqt6VnzTEMqXMz0SGx7Mzc7NbScn6M0roA52ziOT59+ltMRklkjH33KW/QyY8+X8sPP+/krmFduKlfR019AmGDDSA5J4eBX81mxnbzfMElc0gXo8wnzpNoOMOsE+ZSWXUjG9AwqqkucfgASu8UDnFu/wnWzPmDIfd0Y/xbN+kai1Z9zM4p4IlJ80hNz+HtCcNo2cj2fkogaGOGoYDb5izko62b2J/uZJZCH9PEQNDDdw7Mo8hUwCPNnqVF7CW6xOAFuF3rlPHECeyZ5BiNJl773w8s+3Ebtw+6gqfu6uHWDTTwH/PJmdxcbv52PjtSU/iw5wBua9PO+cBAmU+cQA9hOJxzgm0Ze+lc5TIG1e5ObEg0iYb48waUuNAE6kQ1wCiNHM09CPCfrCh64oebbDYRQkQCtwJG4PNymvUEPgFesXzcIYRYK4RwUXojhaN4k/nko89+5Zt5fzGkx2VMvq8PwRpPDgSq+eS3Y8d4b8OfjGx5CR/0HEBokAve6inziV14WiPDgkJ5sOktTG49jj41ryWtMJOPDn/LZ0ffZXPGn/9pazWphIhQbqx/N7Ui67H+7Gp+Tf2Jjed+Y9Gp2ZwrSuXxFlPoXOU6t8XsL7qotM5/mPXSIn74bA2jHryBfrd2detruWtTbemP23nxtWXMXbFFc59AM5+YxwjikrgaPNihM69e15OgANxgA/81n8w99SMfHJpNTEg0N9cfwNMtx9K75jUIwvj97CpWpSz3SBz+hNI6hZWVs37j2Vumk59jf+YobzCfvD3tZ9ZsPECbprU09wmEDTaA3KIiQnKDaFO1Or/ceBeXVnPyMJcPamMgbLaB+bT3yHpjeLDZRJrGtNQtDm9E6Z3CGUwmyYyJ3/Ht/5bRc+SVXDuwvaZ+eutjRmY+T0ycS3ZuAa8/Ppiw0GCbfTy93uoozmhjVkEB03/bhElKasfE0r2+E4ezfEwT/V0PTxek8tTO90grPMP9TZ6iY0IXj8egJ57SOp8wnggh+gghDgghDgshJpbx/LVCiK1CiBIhxIgLnntDCLFHCLFPCPGBsJETUpjsu3Hae3L7nemr+Wb+Rq67vBlV47UXKwt080l2YSF3zl3MmuNHmNy1G3VjKzkfGCjziRN4WhiyS3L55MgcjuaeYlS9foyu14/mMQ0BiAxuR7fqfeiY0IW0wjMkWk6xWTfYvAF/2WSzg1FAPLBSSnnqgufygZeBjkCC5boOWAt0A9YIIdxX68FL8aTWaYrHi8wnn329ni9mr6fftW146q4bNPdzdDKk14YauEYzp/21kVd+XUf/Ji34rO9QIkJcoHXKfGIXntTIIlMxUkrCgkKZfWIZr+77mF9TN7I/p/y3+ZfFX06buPYYpZFzhWcJFWEA7MzazKLE2ezO2srQOrfQutJlbovbT3RRaZ0DeJveWfno8a+Z++Eq/lq1y+6+7tRMe/Ry7YYDyLOFREWGMbSHtkMpgWg+efGXX5n+1yZubn0ZH9wwgLAg2wusFeJji4lW/NV88tvZv3l570e8vv8z5pz8gdknljF173Q+PbKKApPBaw4n+BBK6xzAW7XOWUzpGVStGceYpwfYVXIH9N9cM5kkr772Pc9/aM4ortV4GCjmE4C8jCJCgoL4duAoOta0XXahQnxQG/19s83K1H0zyShKZ1zTp2kZe6lucXghSu/sxF+1zhm+eXUJ+7ce56FXR1K5hrb9K7318ey5XB6fNJcXPvqRouKykh9cjKe0Ue+5Y0huEBOuvIYZfQbTt3FzxwdyQ0ZpZT5xnIyiLCbs/JBjeYfoXr3v+XLkAYJHtM59u+4uQggRDHyE2WWTCPwjhFgupdxbqtlJYAzw5AV9rwK6AtZVtw2Yf1DrKnrN4GwDxkra/7jCsoopigvV3P6zWb8zf8k/nBVFAISGBFNcYvumHp5jojDW/j+C0Fwo1u5xcXl/MN/kS2KcMwEUlpTw0KLvaV2jOom52QAIcN5akBsCMSXOjlImhtxwImMK3TJ2aTLzIomPtm/T1xWk5MVSM9pz9UHzjQW8tPcjHmhyM71qXs31NTqTXpRF9fAqBIsgMorSWHPmR07mH/VYTPZwwlCVBpFOpmhzAcWmYBIN8VqbVxVCbC719Qwp5QwN/awpuz698AkpZSrw/AUP/y6E6IX5Pn0lcA/wvtYgfR09tE5TXBk5yATtbwDdqZ9fz/0LQ0ExOxPPaB4fPK+d1kmRM7rpCs38astWcouKePGG62lbrSZ/Jyc6NR7gNr30hFbqoZOe1MjpR75j/qkVpBVl/OfxREM8dSMzL2p/PO8w0w69Rq+agxlR73ZqRNRm3dmfyShK43jeYZYkfkv96MacyDvi1rjdrYse0DuldXbirXoH5g2pryZ+Q1DlBIKCg2h6SV0O7jipub87NdPe+ebwqy7hgbu7U7NqJT6et8Fmez20Um+dfGfDH2QWGHimezcqhYVx50+LMUonxnSDRloXE92pkZ7WR09pY3LBWZILzvJ3+s7/PG7VxVaV2lItvAa/n13t9ljcjdI678Obtc4VXNahLqMf6kl6ajbLv/rdrr72aKU7dLKkxERIponYBpV4Z8IwPpm/gb+2H7PZzxGd1EMjndXHxKxsbvp2HrNuGsHsASO5Z8US/kzS/l7oItykjf6ii55eU7WSUZTFxJ0fMPXScdzb5Am+OvYhu7K0Z8zTA7WO6X34u9Y5islo4o07pzP8ycEU5Gm/V+mtj8kpWSSnZEFcMH2ubsX2/UmkpGVX2MdT2qj33PHFFb9SZ1QlPuw5gMfW/Mj3hw84HoyLddGdmujvephvNPDsrk9oHF2ACRMhIoQS6Z49Ynvwl7mdL1h5rgAOSymPSimLgLnA4NINpJTHpZQ7gQutdhKIAMKAcCAU0LRj5c6T2wBZ2QbCsow8cOM1vD9pOFGRYZr6BXrmE6OU7Eo5Q0huEEObt2bOoNHEhUc4H5yfZD7x91PdVqYf+Y53Dn7F3+d2UmQs5mxhOr+c+ZMPD81jU7p9Cx+exgdPeKdJKS8vddmcrAkh2gBXYZ54/KT1haSUJfyb4utah6L1XXTROi14U+aTBUs3c2Cz2Yzb/YpmhIdqu3froZ16u/IBFuzazfWffXnedOJ0vW5QmU/sxJMaeaHpxEqiIf6iScu5orMczt3H9MP/44+0tZasYZ1LjZXKtoxN5Bndnw/Vi3TRLr1TWucwXqt3VkzpGdzyWB/eWvQo7brad6rJWzKfzF/yD8t+2s7tg6/kwZu0/TcLxMwnX27eylM/reTPpFPOmU6s+KhG+mvmk/JINMTTqXJXRtYbw8Dao3SNRQeU1nkGr9c6Z1j9zQb+XrOHOycNpFZD+9/H6X2yG8B0tgCTlLw2fiDtW9bV1CdQMp+kWMqdn8zO5Kt+w2hXvaZT46nMJxWjlyZml+Qyaec0kgwn3FpiVSfUOqZn8Gutc4akwyl8cP+n5OfaZwjwBn2sYgzl0du6M23ySKol2HZ7BELmk9yiIu6et4TNyUm816M/Q5q1ci4YH8t84ilN1EMPi2UJB3JDOG2ozD2NH+Pm+mN9LfuJ187tfOGnWAconfIl0fKYTaSUf2FOA5NsuX6WUu7T+sLuNp8AHNt/hkub1ebDZ0YSF6vtj9iZRUE9N9HANQuEVkz5kvY1a7F46E3Ui41zfkA3m0/8ufROSl6sx8Vh47ntfHT4WybsfJNndr3Dx0fmsCNr//nNtf61RtIx4SqPxqSVE4aq3rTR5g6szskvpJTacvT9y1nLx0BLUelxratWO15zcN5kPgFoVimeqY8M5K2nhhARrswnFXEmN5eQ3CD6NG7GjyNvp2a0kynMwGc31kAfk6aeG2zWcgLBIugi84n1ucWJszmUs5f+tUbSJLrF+ec9Wa7ORzVRaZ1j6Da3s4fFby4l8VgqL84cS/chHe3q6w3mEynh3emrWPrjNm4d2IlHb+2mqV8gmk+W7NnL5xvMh4TaVqtJtSgn/yx9VCMDSRsB/rdvLj+nbKBXzcGMrneXKr9TPkrrHMMntM4Z3rtvBsWFJbzyzTjqNqlud3+9N9fy8ouY8Mx8Tqdm8+ZTQ2ndRJu5wlGddAQ9zSfn8vO59bsFzNq9jb1pZ213sIUyn1SIXpqYZzQwadd0Zh77EIBg4WTpQd9G6Z39+L3WOUu9pjV4d/ljNGhRS3MfvfUxJ7eAp59bQHxMJB8+O5IEDZlVAsF8kl9czNgFS9h4+hR3te3o/ME+HzKfgOc0US89NGFid9ZpulTtxh2NHvQ184k9eEzr/PYnCCCEaAq0AupiFr7rhRDXlNHuXiHEZiHE5qKS/P885+7NszW/7WPy1KU0qVeF6c+N0uQkBOcmO/5iPvlx/wHumLeIKpFRLBl+i/MufHBLvbXS+LP5BDwvDsWyhBJZQlbxf9+UpRRUpUlMC8Y0epAbagzwaEz24KMbbRUihIgAbgOMwBcODGE9cu+d9ZK8EK1aZ2l7Xu/iq8bSa/SV2l/Hi8wnJ06d49V3fqR963q8N3GEV2cN84ZNNYDs9ELqxFZi4ZCbaBJf2fkB3bix5o/ZT/SaPEkkkcERDKx9PWA+5W3dXJNIBAKJZE/2doJEEHWiGugSJ/iWJiqt0weH5nbSsbSzORl5PN37FQ5sO8GED2/n7mcHERSk/X7sPeaT1SxYupnuVzbXtGgIgWk+AYgyhPJJn0EsGHIjdWK01WMvF2U+0YSe5hMTkk+OzGHhqZ+5uloPbmt4vz8vMDqE0jp98KTWOcO55AwmD3mTiKhw7pk8xKExHnqyD698MkZTW3foZFa2gScmzSMjO593Jw6nST1t70Xt1Um95pLO6mNmQQFvrF5PkclIfHgEfRo3c2o8ZT6pGL00scBYyLH8aCKCInm8+Yt0q9ZHlzj0ROmd53F0HVMPvXOGnJPJVK9TmWc/uZPIaPfcr9yhj/sPpfD0CwupUSWW9yaOIFZD7IFgPjEUl3DfgmXc9sNCr8yW6S+aqJcezj31I18dW0yHhM4W84l/mTE9rXW+MLNOAuqV+rqu5TEtDAU2SilzpZS5wAqgy4WNpJQzrOlowkKiLhrE3eaTv/45woTnF1KjSiU+fHYkwcHafi16mk9ccZN3xSLh5qQkRn4zl7yiIuYOHu38IqEVZT5xGL1PrwGUyBKe2fUJW9L/YnCdmxhVb4zXnmLzpY02jYwEEoAVUspTZTUQQnQQQlx0oxNC9AAes3z5jftC9ErcrnXwX73Lzy1g3EvD7Tqh5k3mk9Vr9/LS69/TpklNPnxmBJWitZVdC1Tzyd+Jidw6dwHhISEsHnYznWvXs93JFj6ulYGywdY2rgUj6vbhlvoDAThl+DdLnDWrifV0W53I+p4PsBQ+pIlK6xzHs3M74fi9JCsth4l9XmX5J6vpe/NVVKuTYFd/bzCfAEz77FfufXgWGdkGhIAgDaezAtF8UmQ08sjSH0mIiGTB0BtpHG/f7/silPlEE3rPHb89uZzZx5dRaCzEdFEW+IBHaZ3j+IzWOcP+f47wyDXP8+4T3wIQGqZ9gf7Gh3vS79araN+5MR98dz9hGrJYukMnz6Xn8sTEuew+dJqM7HzbHRzEV80n5jGCeKhjZz7pPZjbL2nv3GDKfFIhemri8fxo0ovSGF7vNvrVGq5bHDqh9M4xPL6OqZfeOUp6Shav3T6N2o2q8cjrozX3Exk5umc+2bU3iWdfXkLDOpW5ukMTTX0CwXxSUFJC3rkiwoKC+azvEIY2b+1cMMp8UiZ66eHy02v48thCOiR0ZlT9MbrE4EY8qnW+YDz5B2gmhGgkhAgDbgSWa+x7ErhOCBEihAgFrgMcStvlbvPJ9l2neHzSXD6etx6jUftNWi/ziSv6g2smQccyMhg5ew5TNqwlKTfb+aCs+PiGGijzySv7vmVJ4mquqdaTMY0e0jukcvGhjTYtWFN2VVRT7h3glBBigRDiXcu1BvgFc23P56SUf7o7UC/D41qXcuwshYZiJn50B6Eay9WAd5lP1m04wHOvLKVJvWpc0VZ7poZANZ/sOZPKiNlzSM3L4+sBI7w68wn4r/nE0xp5NO8UB3KO0q36lfSqcTVhQaH/KbtTKSSe2pH1MEojR3MPejQ2H0ZpneN4xdxOK8YSIx89MZv7Lp/EmVPpAFStqb3Ep7eYTzKz8gnLMvLEmB5Mvr8PwRqytwSi+WR7cjK3zJlPaFAw84fcRKsq1Zwb0IfNJ4Gy0QawOGkVbx9cDEBCWFVChbZMegGA0jrH8Smtc4bUU+fIOJxIcEgQL319P/e+MNRmhrAh93Tjtif7kZacxbxpq0moFsu0eQ8QoyEzlzt0MiU1m0nPLiQ9K5/gIEHVeNsZxB3dXNNDI12hj++s+YOfjx7ipWt68ED7K5wbTJlPKkQvTSyRJby8dzZrzvxF31rDGFH3dq89wOcGlN45RsBonTPsXL+fr19aRLfBHel/W1e7+rrLfKKVzduOc9t9X7Bi/V7NfTxpPtFTG8Pyg4kKCeXt6/sq84mb0EsPvz+9lk+PzGX92V90eX034lGt83rjiZSyBHgI+BmzAM2XUu4RQrwkhBgEIIToJIRIxOza+VQIscfSfSFwBNgF7AB2SCm/dzQWd5tP9h9K4a9fDgDQ/YpmtGxUQ1M/ZT6BdIOBBf/sBuCy6jV5vmt352utgc9vqIHnFw6t6L2ACOaT3F+fWMqMI/PZkfmP3uFUiD+YT4QQrYCrMdf1/KmCprOBbUAnYCzwANAMmA9cK6Wc6uZQvQ49tM5YUsJbYz+lSZu69BypveQOeJf55M+/j3DL2M/45S+zfmq99Qeq+SQpO5vR38zlpT/WciQz3enxAJ/XSn/XyLOF6cw4Mo+c4jxubjCAIXVuoEZ4FRIN8dSMqEOXqt3okNCZ9KKznCk47bG4fBWldc7hTXM7eziblI4pPYN+t3Zl+uqnqV5Xu3HPXs20B3vnm+nJ2fS9pjVTHuqvKctmIJpP9p9N4+bv5lNkLGHMpR2cHs9XzScQGBttpTlTUJWHmz3DuKYTiAjS5wCHt6C0zjl8VeucwZiWztFtR6laM46Kss8PuONq7po4kIyzObxy35d8/eZPzHrteyKjwpk27wGqVLd9L3CXSTMsy8jTd/dkxpSbqF7Zdhy+pJHO6mOR0cijS35kycG9TOh8LU9debVT4ynzScXopYkmTEw7/A3LktZwXfXeDKg9Spc4PInSO8cJRK1zlHlv/8Dfa/bQbUhHhJ37Ve4wn9ijjSlnsgjLMtKmaS1efLAfIW6aQ/qaNhaUmMvu/JV0UplP3IheergyZT2b0rMA6JDQ+XyWaF9FD60T0hX1qPyIuKhaskvTuytsY9RYH9tKUVyoXe1DQoL48uO7qBofzVNvLWXb/kTNfQtjHfMSFcc41M1l/QFKYlzzf3Fc5yt44pqr+fXEUR5a9T35JfYtyJZJTInzY1RAZIxnahTGR7ve/WqLmtHuW2y3l7qRmbSNu5zkglOcLTyjdzhl0iAyrdznnmq9aouU8nJnXyOhZXXZ7YuRmtouvXq6S15T4X3EBVeRnSP60/aaluze49jfg0yw7w2gu/Wz1RX1efyO65nw9lLOnNN279FDN71JM5vWr8zNbS7j5T/WUmxyQYp5N+mlJ3TS3zUyLCiUSS3v45K45mSX5JBsOEvdyJrEhkaTW5LNiuTFbEhb47F4ymNax+9cpjtK7xRWrXMVtRpV56M/X+bU4TM8Ofx9jCXa75v2aKY79XLU0E48eE93ftt8mOc++IHiEqPNPr6mla7QyBoxMaQG5VJsMiEAp0dU+qgZPeePXat24LHmd5CYf4Lph18n35jnttdyld4prVO4WuucISg4COLKzgzWskNDHn/7Zuo0rsbcD1Yx++0VAISEBtOl96XcMXEgkdHhPHXn5ySdOFfh67hLJ5s3rcG7r91IZo6Bh1+ZT0qa7fuRpzVST30UwJSePbiqUX0GLfyG7CInNcgN2uhuXfSkJuqph/1rdeNk3noyiiv+W7SF0jqFK/EmvbOXqEqRGKOiKS6yPfcqC3fMJe2ZQ/bv1ZYJj/ZhzcYDvDDtR4wm23riiD76mjZGhITw6cjBdKlTn/FrfmT5of2OBwIu10V/0US99LBZTAPeuGwCOzL/4atjH2KUZf/9qnXMi/H6jCfeiLszn5SUmHh84lxS03N5d+IwrmrXSHNfPU5wu6I/uOaEGsDHG//muVW/cF29hiwYciM1ol2ww+fG09zg36V39CgrUB6phdUYWe8OHmv+Ig2jmuodTpn4Q+YThW+xc735TXH1OglUrlHJrr7elPkEQGQVU6taJT5+fjS1q2srheCLmU/AdZrZtWoDbr+kPV/1H06lMBdokTrZbRee1MciUzFT9k5j1vElnMxPpk5kDYKDgtl0bgcLTs3yCtOJQuHtJB9L5d0HPqdlh4aMeXqAXX3dmSrZHr2cv+Qf3p2+musub8qUh/pp6uNrWhmSK5zWyTO5uchsSIiIZPmIW+nRoLFT45Eb4pMnvEGfsnR68UfaVv637zNqR9bjkeaTiQ2x772xQhHomIwmTOkZZT6XdDSVDyfNZ+vvBxh057Xc/GhvqtSMo6TYyPoftvPZlCUY8grpMaCdzddxl04ePHyGJ56dR1xMBNOfG02tarbvAYGU+UQCz69ew4iv55BdVEiwEM5lm3aTLrpTGwMh8wnAj8nr2JVtRCDoWWNQwGcCUyicJT/bQGFKGjFxkYx84Aa7++ud+eTHVTv56PO19Ojcgolje2nKNh1ImU9WHD3IkQwXZJVWmU/KRC89PJR7gs+PLuCy+E7c1egRn8984kmU8cRBHJnk2HMzTzuXy6NPfcfRxHO8/vhgenRuobmvMp/AnB07Gbt4KQ3iElgy7GZqx7jg5uSmxUIr/mw+Ae9In1xkKubZXR9SYDLwcPNnaRvXUe+QykSZTxSeJjg3h7cWP8qED263WZP7QrzJfLL3QDKPTZpHZEQYHz83mvq1EjT187UNNSuu0MxZW7fx1E8rubJWXRYMvYk6MS7YYPFx84m/b7D9kLyWN/d/zqPbXmHCjjd448Dn/JB80KMxKBS+zPol//D9jDWMuL8Hna63L6Wut5hPlv64jTfeX8kP63Zr7uOLWukKnQzKFRhNkk96D2ZQs5ZOj6fMJ9rQc+64OWM3L+/9hKph1RlS5xbd4lAofJmyzCc5mfns2niY5277hI2rd3PTo7258oZLzpcd2LR6Ny/dOYOvP9JmhnaXTu4/mMJjk+YRFRHGm08McdvmGvjeBpuVzIICQnKDeKN7H96/YQChQU5sL/jg3NGTc0a911LDgy+lf+3hPNz8WWJC9F/XVSh8na7XNeeuSQMZft/1dvd1RwlXew8wfPXtHwy47hIeu11b/IFiPnl08Y/sSUsFoF31mo4HAsp8Ug566eGPyeuYcWQ+beMvV+YTO1DGEycIzja4dUEwK9vAE0/PZdehZJo1qGbX6yjzCfx+7Dg3zZnHupPHSMlzQWBWlPnEYfSeMAGcLkjlqR3vcNpwkrsbj+faaj31DqlMlPlE4UmKi0qYNWUBl13VjFEPutd5D+7dTDt05Azjn55DSEgQHz8/mppVtRkpfHFDDVyjmUv27OXOhYupFR3DkmE306BSvPOBuXEBUW2wOU++sYDsklySC86efyzREE+iIZ5mMa0ZXe8uglCTKYWiPD6d+B17/jlKVIz99yNvMZ/8uGonm387DECXdo2IirCdatkXtdJZncwqKOCOOQvZnJLEBzcM4K62HZwLCJT5RCN6zh13ZR1g8u73WZg4S7cYFApfJ9JoTu8uynBuvPP4dxzYdoJRD95AXJV/sxSfPHTGK3Ty0JEzPDZpLv/7YjVaK9T7mvnEFfPIA6fPMqBpCz7pPZjwYCe0zUezgvn7ZhvA4dyTvLpvBjUj6jC++fPEh1bWLRaFwh9YMXMdG37czp0TB9Cmk5MZFSvAHn20Zw4587s/mLf4H6rERxMcrG1r2VF9dAQ9jZkhuUH0bdycpcNv5d52nZwaS5lPykYvPVyR8hszjszjkrgONI1xwWGUAEAZTy7EjjrdVty5IJiXX8RTE+fxybwNAMTHav8jdmbS4w0baa6YBO1LPcvzP67BJCXVo6IZ2ty+U4nloswnDuMN5pOs4lwm7vyIPVnbvHrS5C7zSbEp+HwJJFuXInBY/c0G1i7dwq2P96VVx4Z29/cm88mxE2k88tQcVv91gNRz2uPyxQ01cI35ZOPJU4z6di6/nzrO6dxs54MCn9fKQNpgK018WBuurtaDe5s8RliQZ96TuAuldwp3UVxYzOPdXuS35dsc6u8Nm2pW6oRE8r/HBvHexBFER4bZbO+LWumsTuYVF3P3vCX8dOQgz3e9ntsvae9cQOCTG2wQWNp4OPckh3LDCBGh3NnoYepENtAtlopQWqfwRqrXq8Jtzw4l1JCH8Vz6f8wnQUECKSVbf99PtdrxdOl96UX9vUEnjxw7y8HNiQAM79mORnWq2OzjS+YTcF4fv9y8lWd/Xk33Bo35qv8wokNtm1grxAe10d832wC2ZuzhxT3TqBQaz2MtXqBauJOn+R1AaZ3Cn3j7no9JOZXOxOl3/Md8qQV36aM9c8jpX6xl6tRlGI0mwkK1HViyVx/1LNnqDGt3H+X7Q/t5pst1PNbpKqfGUuaTstHPfPI7D299mQM5e9z6Ov6id8p4UgaOpK1y54JgcYmRsCwj1SvH8u0bd3DvSO03LWcchXpvpIHrsp+E5AZxb7tOvNujHxOuvAaXjOrjG2qgT0kB8I7NtUJTES/u+YqPj6wCoEZEbUKFk5NkN6Aynyg8yQf3f0pqUgZPT7uDmDj77w3eZD45lZTO9A9/wSQl1SrH0LqJtsURvTbU9F40BDh8Lp2J36+i2GSickQkI1q0cXpMX9fKQNpgs7IkaTUfH55Dq0qX8XCzZ4gOtm8hRKEIJEzpGfQafSU3PtLL7r7esKkGcPZcLi+98T0tG9fgg2dGUik6wmafQDSfFBmNjF/yIx/88RcrjhxwLhgrPrjBBoGnjfnG+jSKbsajzSfTRJ1wUyg00bB1Xfrf3Z37Xr8ZIQTGc+nnn7NmEMnPLQAgoVrZGSrdqZP2ULkkhDsGX8FHz42iaX3b2agDzXwyb+cunvhxBVfUqssnvQc7Fwz4pDb6+2YbwL7sIzy7632klFQKjdctDoXCH8jPNjD1pveIjY/ivheG2t3fG8wnRpMkLjaSL6feyohe2kz5gWA+KTGZeGLZChbs2s2jl/+fvbOMjurqwvBzJ+4J7u7u7u7uBVooxd21OFTQYv3a4u7u7u5uAUIM4m4zc78fk6CBjEtyn7WyApm7zz0J5L5zzn7P3tWYWLW29hMByXzyDUylh76x7/GOcaeYa2l65x2KtWC4PW9LRzKefAMhJMKsEmgA4W/CuHT5Bb3aVmX0T/WRqdNoFNOaT8zlJDfAvBMX2Hz3HgPLVWZJw5bYW+vhwWDghFpqrn5iDs48JSIiIgFxmRhScCJDCk3G1drdpHNKDsl8ImEsoiNimdtjKXcvPUehRQUwMC/ziep6BeN6N+CviR0pXyynWjGW2q5On2bNXqXKMa9eUyZXq6P2+41vYqDSySAl2AzFsXcX+P3Jv2RzyMWIwlOlft4SEt+haMns9BjVlJotymgcay7mk/OXnjNl1h4K5MrA0skd8XBN+blnF6E0as9uXWNBd51UiiJ/XbpMSEAsVoLAkPJVzPZ0d2rZVEzClNr4Pi6IsXcXEp4QysAC4yjlVsFkc5GQsBQeXH7GzRMPqNSkDM1/rouVtRXK4BAEQVXtBCBv0WwA+L4O+N5QamOo5FpkVBzDxm0hPkHB0kkdKZw3U4oxac18sv/xEwbv3c/y21d1m0gSkvnkm5hSD19FedP/5kxeRj4BwM3Gw2RzkZCwdDzvv2VWtyX8O2uvVvHmYD6J943irV8Io36qR8s6X1cvS460YD5RiiITjxxj3a3b9CldgVIZdawSJZlPksWUeiiSg7IelemTb4RZHmQ3ByTjSQqYUwJNqRT5c8lRNu+8RvtGZZgzohX2duo9eExlPtFHPOgnmSZXKply7AS/nTlHs/yF2Nq6CxkdHHWfnAETaiC13jEGccp4VrzYTjb7HIwpMpPcjvlNPaWvkMwnEsbi6U1P5vdaRkxUHJlzpiNPkawaj2FO2gkwb8ER/APDmT+2HbUqFFArRpeEmiUn1ZJYcvoKa2+qFknLG5m3WTO1mk9MrY/Xgu8x9cESnoY/IFKueTVACYm0wvJR63l04xXjlv5Iw06VNY43F/PJ5esvmTB9FzmzeFC3UiG149Ka+SSJSm45GFahGttadyGTo5Nug1moPhq7eqYpdTEwPoSxdxfjG+PFz/mGUSW9jqcXJSRSOdHhMSwctJKwwAi6TWhDq34NcHC2RxRF7BxsaftLHRp0rERsdPx3jSeGXFdqopE+viEMHbOJqJh4lkzsSImCKa+R05r55MSLl1x/6gNAx8IlyO3qrtuEJPPJNzGlHspFBd4x7pRxr8SvxRdQ2r2iyeYiIWHpXDt6l6Anb5BZyfhxXAtcPTRbU5jafKJQisyavY/Ld14xvk9DmtdWr2pyWjCfiMCMk6fpuHEz9wL8tZ9EEpL5JFlMpYfH311k2YuNFHUtxYACY00yB3NHMp6ogbkl0P5edYbFf5+gRrl89G5bVe04bZNokHrMJwD/Xb/BwD37EEWRWIVcL2MCkvlEB0ydXAO4GnyXsffmIxflDCs0hUrpapp6ShISJkUZHMKgWR1ZuHeEwU9xg2G1MzgkimGjN/HszXvmDG9Js1rqt5Cx1HYC+jjVPfPUaWaePE2jvAXZ0qozGfRl1jQAxjKfpKUT3gBPIjxZ+Fx1Aie9bUbKuFcy6XwkJMyRuJh4Jjafy50LTxk5vxutetXSeAxzMZ/cuP2anwauYteJu6p5qSkladF8ct3bh74795DbzYPd7X6gUDodTeIWro/GwpS6GC6PZNy9pVwLPs+rqOcmm4eEhKUQFhjBmCZzCPQJ5udZnVl+aSYzV/3MHzuG0GeyqiXL8sk7eHbH67vjmIv5xP9dGEPHbCIkPJq82dV75qc18wmAR4ID46rWYkfbrhRLn3Jrou8imU++iakPKpwJ8MY7+jW98w6lRob6JpuHhERqIF9OV9r1qcNfB0eRr1h2jWJNbT5JkCv4ddourj14w8RfGlO3UkG14rQxn5hCG3XVxbt+/lhHyqidMw+rmrXD0VqH6hgGMJ8YUhNTux6eeHeJhc/WkM9Z/UM7aQnJeKIm5pRAA9i1/xajJm1j1e7LGsWB5bYQAP2ZT068eEnHdZuJiI/HzsqaGjly62VcyXyiPaZOrgF4Rfsy8s48PCOfSq59CQlgwS8rePnQh4krevHzpFbIrDR722BO2hkRGcuY8Vu58cCLzk3KYaXB92KJ5hPQj2auvXWbgXv2YWdtjaj7lFQYMLmW2hJsYB766B3jTuMsbfg53zAaZW5l6ulISJgdcdHxTG3zJxcP38XOwVarMTTdNDRUYs3PPwzbMAX5c2Zg7Zwe5MqqXhn1tGg+Off6NV03b8VaJmNHm65UyaZeS79vIplP1MKUybZ4ZQK/P9nGzZBoAKqlr4uNoN3vvIREWiA6PIYR9Waye+kRIsOiKVe/BFlzpufFfW/mj9jIyZ3X1RpH03bohtLIgMAIfh64hv1n7gPgqIbmpzXzSURcHN02bSVeoWBL6y5UzppDtwlJ5pPvYio9jJRHM+H+ch6G36Fzrt40z9rBJPOQkEgNPLv1ilENZ2FlbcX83cOo0byMRvGm1sf4BAVTpu5iz6m73Hvmq/b4xlw/mtJ8ApBR5kTtnHnY1KoT7nb22g9kYZqY2vXwQuBNpj5YavT7WgKS8UQDzCmBBnD7nhfK93E42tvwv6ldqFFO/fYguix8zCGRpo8HvghYR8roV6YiG1p2ZHC5KrpPDgxuPkmtp7rBPJJrEfIoJt7/H3MebwBUPUsdrZxNPCsJCdMQ7B/G2IYz2f/PSTr0r8+s9f1xdtes6oU5aWdMbAITp+xg9IStKBRK7GyssZKppydp2Xxy4sVLWq/eQFBMNNYyGSUzZtZ9YhZu1EyL5pMFz3ZzPfgCLbN3pluuX5BhZeopSUiYFQnxcmZ2XMDWWdsByJQjncZjmMupbgCbSCXp3Z1Y8Wtn8uc03MluU5tPdNXJx+8D6LB+My9Dg4hKiNdtQmCwNq6SNuofe6tSdM7VmyGFJuFs7WrSuUhImDMKuYKVU7Yxud08hteZwYj6M5neYT6ndt3QeCxTJ9cA4uPl2IYpKF4gK7sX96F62XwpxujSxlUbTG0+8QwOofOGLbyLimRdiw40zqteu9tvYoG6mNqTbaAyY057sJoT7y7RJGtbCjgXNck8JCRSA89uvWJw9Sm8fOjDpL970a5vXY3iTa2P8fFyFi88RlBoFFZWMmqWVy9PaSnaqKsu7nn4mEF791M0fUa2telCZicdck0WpompXQ8fhkuVMJNDMp5oiDESaJpuCrrEybC2tuL3ka3p0rSc2nHaJtFA9w1Cc0mmAfx34QZ7Hj5idOUarGjcCicbHUpeJWHAhBqk7uonpi4XCaBESZwyHu8Yd3rnHcqYIjPJaq/jCUYJCQtFnqBg6Yi1zO//Lw7OdiTEad6izJzMJ3K5krDwGGzDFMwc2oI/RrfBwU69535aNp8oRRHrSBlDyldhZ9tutC1UTPeJSeYTjTC1NspFOXMeb2ar1yGqZqjDwAJjsZeZpkqahIS5Ioqq2lDpHWUsOzKGgbM6IKjbryYRczGfeL4OYNjYzcjlSpZP6UTRfOqZDrXdPDRVcg1010m/iAg6rtvC/YB3AFTLnkv3SVnYhmISaUkbX0R68ceT/8jukIuRhaeR0S6LyeYiIWEJhAVE8PSmJ2+f+hLoG6L1OKZOriXh/zQQ73eh/DailcFaC4Dlmk/8IyPpunErDwPfk9VZD89qC9TF1J5sA9X+6bIXG/n1wWJeRD42yRwkJFILIe/CGNdoFvv+d4L7V15oHG8O+mgbpqBt/VL8MaoNP7WprFaMpWijrrp44sVLeu/YRVZnF3a26Up6XdqZW5gmpgU9lPgcyXiiBYZOoIFmD/SQ0GiGj9nE2RvPGdajLmN611e7hYCpzCf6iAf9JNPiFQpGHzrC7FNnaJinAHvadSePm7vukzPQabUkUrP5BMxHJP73ci82gg2jCk+jtHsFU09HQsJkHFt/nuE1pxIXE4+9oy3Vm5bWKN6czCdJXLn4nEol87D8186kc1PvDb+pzCem3jhMYs2l21z382Zh/WYMq1BV9wENqJWpNcFman3c8vYgi5+txUZmixLt30dKSKRmAryDOLTqNC1/rMnI+d3MqlWdJnrp5R3MkDGbiIiOY8nEjgatfAK6bSCag05aR8qomS43m1p14vc6jbGW6bjdYqANxdSUaAPTrhuvBt9lyv3F2MscGFV4GvmcpB7fEhLqogxWmU/K1iyMW3rNTv6aQ3ItIjKW0eO38vClPzOHtqBxdfWqPRg7wWbKJFtobCw/bNzGmvu3Acjvng6dRrRAXTRmRWlT6uH9sGd4x7iT2zE/AwuMw9HKyWRzkZCwZBLi5SwbuY6nZ+4B0L5fXbLnzah2vDno48Gdtzhy4gH9OtVgYJeaasWkFfPJ1bfedN+8nQMvnxIUE63TWJZoPkkLeiihQjKeaIm5mU/i4uRMn7GHjduv0K5BGYZ1r6N2rGQ+UbH65i1+2r4Ta5mMxMOK+iGVmE/SauudZ5GvGXH3T3xjvemTbwTNsrZH0G2pLCFhsYiiiDI4hDZ96jD5n94MmNEeK2v130poo52aLoQ00c4DR+8xccYucmX14L8Z3cidTb22CKYwn+gjXh96GREXR5+tu9n54CEjKlZnfr2m2OiaWAOLN5+kpSQbwJmAa4y++xfxyjjsZQ7kcdKxfLaERCpk5eStrJm+gwYdKzFh+U/Y2GrWnspczCf+78IYNnoTRy4+xstP/RPqllglTB86ednrLUsuXaZz0ZKsatYOZxtb3Qa0UH00tjaaUhefRb5m7L35hCeE4yAl2iQkNMJFJufX/35m7F89kKnZAjUJQybX1NXJqOh4xk3cxu3H3vw6oClli+RQK86SWtPpqo0JSiXWkTJy4Mre9t1Z1qgVdlY6aJuFtqQzZrLNlJqoEHNQwLkowwv9iruN5m0nJSQkPuJiJafjgAYs2j+ScrWLqB1navOJQiny26JD7Dl4mx6tKjG6V33UKQKaVswnD9+/Z96JCwDkcXOnUlb13jskiwE0MTXpoYTpkIwnOmDoBBpotikoivDPmnPMmX+QTQeva3QfyXyi4orXW5r8t4Y34aEANM1XSGOLQdtCxbCz+mJj2cDmk9Rc/cQcRCIkPoxxd5dw8t1lyrhXwkam4wayhISFs2Xmdnb+dZhWvWrx29bBeGRU//dUU+0Ew1Y/uXrDk2HjNmNrY83vI1sjU7Mlgi4JNUtPqiUolYw7fJSFFy7SKG8Bcrm66zwmYLHJtSTSmvlERMQ7xp0W2ToyrOAUKqerZdL5SEiYI5v/2MffYzdSo1lp2vxcR+N4czGfBARF8teiYyTIFbg62xu0pQBYvk4uvniZ8YePUjVbTra36UIWXXp4g0XrY1oxn/jHBjLk9m88DFed6s/uoId2SxISaYCwwAj+HrORcrWK0HlIQ43jDZVcA/V1MiY2gQmTd7B001nuPvVRe3xLM5/oo/XO4guXaJKvIJtadSSdvY76IJlPvoupNPFK8F1mPFqGu216RhSeapI5SEikFsICIhhcfQrvvIP59b+fyV88u9qxpjafiCIsXH6czTuu0qRGMbJndlcrLq2YT1RjyPi1ej3Wt+hAgzz5dRtMMp8ki6n3TdMykvFER8wtgQZw9NRDgl+GIggwtncDShTMqlacXYTSYk9xg34WQgAKUcQ6Ukb9jPlZ0bgV/zRtg4utekaDhnnyM79eU85260OtnHk+f9GA5hNIvYk1ML1jHyBBlLP0xQbG3F1EvDIOG8GW9Lbql7ozJxRK4cMpxJQ+JCSSQ6lQ8s+EzfzWawUFSubkr0OjKVBSfYe2uWnnsxfvGDhiPVOXHUSpQcmrJM1U16zyKaZOqulDL5ddvkrD/1bzMjQYgAxa9Cetki3n560IDJhcS20JNjCPRdS/nsd5EfmY7nn60Tp7V7OqCibpnYQ5sHvZUSa1ncfu/85oFW8u5hPV9Qp+bFWZOcNb0b1lRbVi0qr5ZMeDh/TZuZvsLm6UzZxN4/isTl883y30hDcYP9FmKm1UiEq8Y9zJ5ZiPsUVm0zHHj0bRREnrJCydw2vOcHLndX4Y0ZTS1dQzNn6KOZhP4uPl7Nx8DaUokjGdM63rlVQrzpLMJ6C7Pq6+eYsh+w5QPEMmdrXrpnubcwvUxbRgPnkQ9pyJ9xcREOuvtzElrZNIq7x/G8SE5nMJC4piyr8/4+qhfnU9U5tPAP5efZZeA1bh7R+qmpNU+eQzxu47wuOgAP5u3Jr2hYvrNphkPkkWc9g31YTUoneS8eQL3NI5UadNeY1izC2BlkRmwZ6KJXKxdFInGlQtrHacKU9xm1P1k7OvXjH9xCnq5srHnvbdye/+/RKBGR0cGV+lFglKBVEJ8axp3p6p1et9fpGBNgyTSM3mEzAPoYhSxOAd407bHD8wtshsiriot6EgIZEaOb3tMiPqTifQL5Tw4CiNYs1NO/3fh/Pqjh8Av3Soxg8tKqgV16x4QcZ1rUv7WqXI6K5ZeXVTbhqCfvQyMCoa60gZXYuW4mTX3lTPrv4J38LpMrCpVSfOdOtDhSyfnNywcJ00hfnElPoYpYhh8oN/OOx3lgaZW9An3whsZcZ5PyIhYSncOHaP+HeBuHo4MWZxD1zcNTPqmZP5ZOW/Zzh28TGDutZieI86am0elkiXnh8bVaBe2QK4OKr/fLDk5BrAxTde1PtnJYc9nwGofbrbRiZjQ8sOnOn2M0XTf2F0t8AkG6QtY+bloBD2+pykVqZG/CJpooSEWvw14B98PN8zdklPnDXUSDAP84nqWgWdGpdjfJ9G9GpbRa2YHDaOtKxajEpFcuFgZ6NWjCXr49Fnz+mxdQcutnb0LFFWo9g5tRtyuGNPcrq4ffyiBepiWjCfvI7yZvz9FSa5t4REaiMsIIIZnRfh7OZIkXJ5DHYfQ60h3wWEYxumoHOTcvw5ui12Nik/t11jZVQumouiuTNjbaVeCtsSzSehsbH03LyDSz5ezK/XlJ9LqZ+X7lumIrvbdfu8uqYBzCeG1ETJfJJ6kYwnX+Ds7sjohT9QvWlpjeLMLYEGEBwSxcARG3j00p+ZQ1qovegBqfVOEutv36Hn1h242tqxt3136ufO981rJ1WrQ173dKy5f5sfD+5k1b2bdCteimOdf6KgR/rPL7bwpBoYv293EuYiFBvfnCMkIYgBBcZSN1MTU09HQsJkeN5/y7Aav/LeJwSAPEXUP9VrjtppF64gZ1YPBnerzeif6n+3mom7iwOZ0jnTtGIRhreryd/D29Ossvp9VyF1mE8ALj31wi8ygrUtOqi1gSgAv1avC4CdlRXb2nRhRMXqHy+wcJ1Ma/qoEJX847mNf15uJadjXhytNDNhSUikFXJnd6FGs9L8sWMombJ7aBRraPOJunoplyuZM3c/2/bcoHPT8swY3AIba6tvXm9na03pwtnp37IKM3s14a/BbahaLLfac7Pk5BqoNhOtI2WUy5yN891/oWOREinGTKham7zu6cju7MrBjj35qeQXumqBSTZIO+YTEZF1b/bwv5dbKO5WlqEFJ+Fs7WqSuUhIWAqxUXHM6rqYLX8dIzI0WqsxzMV8svKfMxw58YC+HaszoHON78/DSkaVUnkY16Uu8we0ZF6/FpTKp17VakvWx9u+frRdt5E5l88CYGeVsq61KlCE9oWKUzh9Rk506UWrAp+suy1QF9OC+URCQkJ/PL/9ip5FR3Dt5EON4szpAEN8SBxVS+dl/ti2ONp/32hZtUxeZvzYmKVD2zKzVxPyZv3+gfAkLNF8Ep2QQP/teznw4ilN8xX6vCr0N6iYNTv9ylSkdKasnOjS+/NWPRamiZL5JHUiGU++wPflO57cfsO4pT2pWLeYRrHmmEALC49hzPitHD2pWvSM6FlX7VjJfKLimrc3bddu5GlwAPEKRbLX9CxRllYFi3L7nS+/XT6Ld0Q4f9++xqSzx1GIIkc7/0Sf0l+cnjdwUi01Vz8xB6F4FxfE6DuLuBd6g3Y5etAp50/IpEcqgiDUFwRhtyAI/oIgxAmC4CsIwlFBEJolc201QRAOCYIQLAhCjCAI9wRBGC4IwrezFxJmizI4hA7967F4/0iKV/y2Se9LzE07RRFmz97H5p3XaN+oDHNGtMLONvnndWhEDNuP3qZJv+Us2XgGX/8wZvzUhFEda2s0P1NXC9OHXvqEh9N5/RZOe3kyo2Z95tZuhK3s27/KoyvXoGr2XBx79ZyeB3aw4eEdhpSvwo42XcnomGhaMGCVMGMl2NJKki2Jw/7nGHBzJqEJwQgIeNhmMOl8DIWkdRLacvfcYya3m0embB4sPTKGivUMu940lF6KIiz79xQrVp6hSN5MODt+uy1pXLycQ+ce0n74StbtvQpykSVD2vJz00pqz8vUyTV96KTn22Bu+vvyZ90mjKhY7ZvXtSlYlJ9KluNlSDD9j+5l59OH/Fq9Hisat8LqUzOsgTYUU1OiDUyri0f8zzP38f/I6pCDsu6VTTYPbZG0TsLYvHnsw96F+wAoWaUA2fJo3trYHMwnSqXIb4sOsffQHXq2rszon+p/szqYQqHkzPXn9J68kT0n7uLu4sC/ozrSuW4Zte5lan3UBd/wCAiH9A6OHOn0I71KlvvmtUXSZWBytTooRZHfr5zj+OuXLGrQ/PMq0xaWaAPJfGIuSHonYSlEhkajDA6hZosyNO9RPeWARMzFfHLw2D1mzTtA6SI5WDShA87fqYR596k3U5cc5PItTwrnzMiacV1oVKGQWvcxlflEF12MVygYuecQfbbtRq5U4mBt882DkBkdHJlVqyEe9g6svneTy75e/NOkjcEP80nmE8vG2FonZUm/QFQqmdzqd14/8WPSP70oVbWARvHGSqBp8lBPkCuYs+AQ/649x6mrzzS6V2own+hjo9A/MpIu67dy+cnbr17L4uRMm4JFAQiOiaFgugw42dgQGBPNjqcPmXT2OAdfPCV9cqWVDWg+gdTdesfUrQUAYpVxzHi0jt3exynnUQU3W/Xct6kVQRD+AE4AFYB9wHzgIJARqPPFta2Bc0AtYDewFLAFFgJbjDZpCb1y5H9Hee8bwtRVfciRP5PaceZoPvl71RkWrThOzXL5mT7oq/dgH5ArlCgUSvaeus9fG85w8OwDutYrS8fapTSan6k1Ux9aGZWQwMDt+1h2+Qqdi5akfNbkq980zJOfPqUr4BUeypRzJ3gcFMCKW9eYev4kWZycOdPtZ1on6ipgUPOJdMJb/8Qp4/GOcadxljaMLzKH/E7qt3u0BCStk9CVu2cfM6jaZN57hzBjbT8adFTfgAHmYz4B2LLrGj8PXENICveIjk0gKDSKNXuusmDNKY5efEzfllVoX1P9lpW6GC3N4VBDZHw8fbftYfv9BwyrUI3f6zT+6ppC6TIwvkotohLimXL+BCffeLL05hXmXbtAtey5ONutD2Uzf3IS3kLNmcY2ZppSF2+EPGDIrdmcDzwOYDEHFSStkzApoaEM/b0zfx0cReWGKVeJ+hJzMJ+IIixYdozNO65SslC277bQiYiK441vMEs3nWPhylOcvvOSkR1q0bp6cbXuZUp91McaMjZEzpPgAKbWqEeVbDm/et3e2ppZtRqSwdGJedcu8L8711l84xIr796ke/HSbG/TBY+k/VYLNGUaUxPNYR/V3JD0TsISqd2qHANndaBaE/X3HYWQCIPpoybrx5NnHzN1zh4K58lEtbJ5v3ldSHgMt594M/PvIyxYdYo7z32Y8VNjmlZSr8q0JeqiUhSJjI/HLsqKlc3aMqBs8vsEs2s3pKBHelbdu8nMS2f48+oFNj26y5DyVVjZtO3HwwqS+SRZ0qIOmkLrLGPVa2Siw2OY2GIuvq8DyV8ih8bxmj7IQfPFDmhe/WTDtis8vuYFQPuGZcidTb0kuV2EUmsDij4SaeawUQggfhjr81+b0LhY5lw+y7QLp6iSPSfrmrenbaFiONuoTv7deufLrxdO8vvV84CqvcBnGPBEN6Ru8wmYXiySSikPvjWHkPhAgDTZYkAQhF+AMcBaIL8oin1FUZwoiuIvoiiWAyZ9cq0r8C+gAOqIovizKIpjgDLAZaCDIAhdjP5NSOhMeFAkk1r+jjxByaz1/fHIqP7vpzG0U1Pd3H3gNlNm72bV7itqXe/lF8K/Oy8RGR1LndL5Uw74AnMwn+iqlyKw8MIlmq5ay2Wfr82arrZ2/FSyHDYyKzxDQ7CztsHOygq/qAg2PbrL+LPHuPPOj5YFvlhMWrhOpjXzCcBe37tEyMMYVHA8pd0rpBxgAUhaJ6EvfD3fM7z2VLYtPMj1U480jjcnvYyNS8A2LPmqkJ+StP/17M171u27SkxMPPXKFcRKppnu6LKJaOoEm1ypZMKRYyy8cJFHge8/e00AhpavSiYnZx4GvOdlSBAAb8JDWXPvFhPOHCMiIZ6/GrTA3voTTbRQ8wmknZPe7+OC8I5xJ6t9DiYV+4PcjupXBjQFktZJmBqlUmRCs7n4vA5g2qpf6DmmGTINtcIczCcAf68+y5CRG4mO/X5Mkkbef+7Lxl1XiYlLoEnFItjbqP+Mt8QkG0CcXM6wXQcZtHc/V3y/Xj+OrFidclmy8SQogP0vngDwPCSIFbevMu3CKbK7uHKm28+kd3BUBRhoj1WqfpL6kPROwlL5vccSnt3xYuySHhQpl0ejWHMwn1y48oIe/VZy7OKT716XVPHj+oM3rN95lei4BNrWKIGbk71a9zHVulFXXVSIIjvvPGTN/VtfvTawXGUa5CnA67AQ1ty/DcCz4ED+unGZOZfPUjZzVi736Ec6AxsyDYVkxNQ/ptI6yXjyDcKDIhlafQq7/z0DgMxK8x+VNhuChk6iAaRTWNOrbRX+nd6VCsVzqR2ni/nE1Mk00F/rHdVYH/8/xMrl3PD3Yd2D29TdtJJXYSGMrVyT6jk+/mxDYlX/rgIfDSxfYeFJNUi75hOA0IRwvGPcaZi5JeOLziW7g/q/W5aOIAh2wGzAC+grimL8l9eIovjpw6oDKkflFlEUb3xyTSwwOfGvAww3YwlD4v86gClt5+GW3plf/+uD8K26wt/AnJJpoFoQvb7rl+J1Sd9mcFg0oRExVCyckwLZ0mt0L9BdM81FL18GB39l1ARIUCr5+/Y1/rx6nurZc7G+RQdaFSyKo7UNClHkgvcbxp05xtDjB4AvzJoWrpOmMJ+YUh8D4oIZc3cR3tFv6J13GDUy1DfZXPSBpHUS+iY+NoGVk7cS8vwtNnbWtPqppkaaaW56mZL5RBQ/auVrn2Be+QRTuUguiufJotF9wLJbCwAsu3yVjVfufvY1O2trDrx8wvJbV6mQNTv/NWtH64JFsbe2JlqewCHPZ4w7fZQ+h3cTK5d/rY8WtqmYRFpKtHlF2yEIMoYUnERhF/UqGRgbSeskzIX3b4MYWWc6h9ecoevQxsxY1w87+29XDUkOTZNrhkqwqWPQ/FQjn71+z72nPlQsnJNieTKrfR+wXPOJUhQ5+uz5V+tHOytrngUHsv7BHYqkz8hvdRrTOG8BrGUygmNj2PL4HlPOnWDO5bMExUR/PqgF6mJa0kRTI+mdhCUTFxPPr23/JNAvjGmrfyF7Xs1a05mD+cT/XViK2qgUP2bR7j/35dItT8oUyE5+DfZaTbVu1FUX9z56TFzI5z8fOysrQmJi2P7kAXncPBhfpSb1cudDJgi8j45i/YM7TLtwih1PHxIc+8m/m4XpoaSF+sOUWicZT75DfGwCyuAQipTNzb9nJpKrkOabYubWPgAgMjKOgSPW8z44koXj2tGqrvoljlND6x198eWCSAACY6KZcPYYUQkJzK7diKxOnz+8kuQys5Mz5TJno2zmrORwcft4gYGTasbaPDRV6x1z4PR7T0BgeKFfKe5axtTTMRYNUYnSLkApCEJzQRDGCYIwTBCEqslcn9SI90gyr50DooFqieIoYYE8v/2KmT8sYd28Q4jiN+1238TQxk1NW9apYpJfECU58JO+zdKFs2Nva4OXfwiRfp8LlzFOrOkam4S+9PJLrYyRJ3De+w0rbl+jwZbVhMfHMa5KLSpl+1hhzjsijGi56t/nq/89BqwSltoSbEmYUh8j5FGMv7+Mh+F3aJejB+lsM5hsLnpA0joJg1GtZgEGzOxAzzHfbu2WHJZiPklKpiVpZf6cGXB1sic8KvYrrVQXSzefqMb5/DDDEc/nzLt2gTY7N+Jqa8fYyjUpm+lja517Af48DVZVWEz23ZWFbSomYezNRVPp4ru4IMbdXURg/Hu65upjkjmogaR1EmZDQlwCiwatYuHAlQT6hRGXQtWQ5DCkTurboCkkWgqzZXIjnZsTcoWChKA4je4Bpj3hrQ99/FQb4xRydjx9yNQLJ+l1cCelMmVhbOWalM6k2p9XiCIn33iy9fF94BsVpvWMZD5JNUh6J2HRhAVGMLnV7yCiUcudJMzBfKK6/vvamFQd093FgYzpnJEJAva2mj3bLdV8ohrjU01UsPnxPcadOcqAo/uolTMvYz7RxDiFnH0vnvBnYtcF2aeHWgywlyqZTywCk2mdZDxRg9BXvtg52DJn00Cy5tF8w9wczSf+78MZPGID1x94MeGXRgzoXEPt2LRuPrGWySiQPl3iWB9/hZI2/16HhXLV9y3p7B3I5+7xWWx2Z1f6lK7AkU4/sqNtV3a06crudt0YVuGT33MDmk8gdVc/MQeheB3tw6i783gf50/f/KOonfHr/u2pkIqJn2OB28AB4DdgEXBJEISzgiB8ar8unPj52ZcDiaIoB14B1oB516GW+C43jt3j5l5Vi5pCZTSvAGSO2pncgijJge9ob0Pj6kX5uV1V0ns4cfeJD/EJig/t6soVzM7BuT9Tv1xBte+XmswnyZk130aEMfzEQRRKJb/VbkQmx+TblNlZWZPRwZGMDo7YyqxUX7Rw80laar0Tr0xg2oPVTLq/kODElnQWiqR1Egbj9LbLHFp1mi5DGlGvnWatqSzBfPKpB7Vw3kx0aFyWHFncuf/M94NWArg7OzC+az3SuTiqda/UZj4BlT4+CHxHn8O7sbWyYlatBrjb2X91zTexwCQbpJ2WdCEJ4Yy7+xdTH/5tkvurgaR1EmbHkbVnWfDzcgCy581Ig46VNIq3FPOJUhTJlM6FpjWLkTurBw9f+BMZE/dBI61kAs2rFFXrPpauj8lVzjz79jU9928nnYMjk6rWwc7K6rPXv1lh2gJ1UUq4GQVJ7yQsHl/P9/StMJ7tK05qFW9O5pNvFf5UKEVsrK2oXi4fubKm441vMMFvwz+7Jk9mj+SDP0EXc6U5mU+SOPrqOd33byebswujKn2e1xVRaaIyuYOgFmY+MWbrnVSKybTOsBnuVIKv53vGN5vLvOOTmbt5EGPa/0WAb4hGYwghEYgemv0HtgqPQeGq/i9X0kM93k298pPRMfFMmrKDwX3r4/M+TKO52UUoiXPRzrdkEwkJzlqF6iUePj705c6an8Svlz8f/SpXYsqx4zx6H/Dh4S93VmItkyFXKgmLU51KcLT5+G+R3z0doyrVoGGe/ETEx7H05hWiEuIpmj4jg8pVIY+bB5PPHScqIeGjCDjLdftGv0FMpB0OzpqfnNCU0CgH3J0029zWlSShyOKkedJaX4TEhzH27l9MKNqNtjl+4EnEfd7F+ppsPkYgU+LnMcAjoCZwB8gLzAMaAduBOonXJZX5+daDJ+nr7vqdpoQpKFwgPQv2jmDToqNsXJicYfbbJC2CNNFPbbRTXd10crQlk60djtmcSe/uRAZ3J3JkdidnVg/y58xAjsTFzvHLT9h/5j5RMaoKdh6uDoxuXxs3Jwd+/6U5W8/c4c+tZ9S6py6al7Q40kUzrSMFrbQyCStBII+Hx4fWO3Jn1cZp0oivwkK45ONF64JFye+ejvfRUR9iXWxtKZ8lOwPKVqJo+kxExMfhFxnBXzcvc+7ta5VWGkAnkxZOhtZJY2ukf5SLybRRiZLnkW8Ad9pkL045jyqsebWUeKXh34voEUnrJAzK0hHryFY4O8P/6Iq/VxCPbrxSO1ZTvTSkVtraWuMm2mCT2QE3Z3vcXBzIkt6VbJncKJQnEyUKZMXB3pZHL/04dO4hASEqsbKLUDKkR3VaVytBw/IFmbjyMFcfe6V4P111UleNBO3WlDJBILe7O69CQrCJlJHwiT4KwMvQYC56v6FVwaIUSpeBa37enyXV0js40rVoKUpkzER0QgI+keEsu3WVWLncIPpoDG1MK7oYpYghKsa4a2QNkLROwmxRBofQZnZHWvSsQdFyefh76k4S4r9/UjoJTfdkNdFJTTTS2lqGQ7wV1pnscXa0w9nRjgweTmRJ70qhPJkoUyQHWTK44hsQxvHLT/BN3Ku1i1DStUNlBrepTuc6ZRi5fB+B4VHfvZep9VHbNWRud3fehIZ+po2g0s1HQQGcf/uaFgWKUCZTVq5+oo1JdyubOSu5Xd2JUygIiI7ihr+PwXQxtWiiKdeJJkTSO4lUQeh7lQmjYM0StPixJksmbEOpUP/QuCb6aChtFARwjAQxgy2OdjY42Nvi7uJAOndH8ufIQPniuShdJDsxsQkcPv8Q73eh2MlVucnqJfKweFAbdpy7y2+bT6d4L231TRdd1EUTc7m74RUahm2UFfFOH9/zWAkC9wL8Oev1ihYFilA1W04u+7798HrS3bI7u5LJyYk4uZzQuFh8IyP0rompRQ9TqRaaTOsk44maeD3xZWLL3/n90ATmbhnEqLaLCAvSzO5mjAQaaPZgVyhFFv99QvUXNysql8rNG98Q/APDvx/Ix8on2hhQdE2G6SOZBto9+BMUCnK7uzOuTi2WXLzCDR+fxLFUCTU7KyuqZVed7rdJPJltK7NiUrU61MmVl3NvX/Pf3Rtc8H4DQEZHJ2LkcloXLMr+F0849cbz480MlFSD1G0+AdOLRZwynukP11LI5SxRcpXpRIYMJdpXDNIXSoVME0dqBkEQbnzy939EUfzni2uSHgJyoJUoiq8T/35fEIS2wFOgtiAIVUVRvKztvCUsk8fXXnB8wwW6j2xKgG8ox7Ze0XgMQ24UQsq6WbVifpo2LEHO7Olwc3PAzdXxs5KFUTFxhEfFcujcQ67ef83lO68+mE5kgsCwHnUplDsTu0/cJUamoHOdMpTMm1WtDUMwvWFTl8RanXx5VWbN4yd4GhD4mfnEShBQiCLBif240zl8POGezt6BQeUq06FICVxs7bjw9jURCfHkdnVnTfP2/Hn1PCtuX7N4nUwrSbZPiZK7U9y1DIMLTuR/L/4kSqGH8jzfQM96J2mdhEFRyBXM6ryQReemM2J+N/rVnYNSqdlzV9ONQ0Bvm4cVyuSmUb3i5MyRDlcXBzzcHXGwt/3sGqVSyeNX77j16C2Hzz/ijW/wh9e6NitP62oluPPEG2t7K5YNbcfyfRdZdfh6inMzZXINtFtT1sidm36VK/Lr8ZMfzJmgOswgCAKiKH4wY+ZydeOan/cHU0q3YqXpXqIMhdNlIE4uJzIhHntra9oXLs7Ao/u4897foObM1LCxmIQ56KKuSFonkZZYNuhfIt+H0GV0S/IVz87sfqsJ9AtVK9aU5pMyJXPSsE4xsmfzwM3VAXc3R5yc7LCx/rxqh39gOMcvP2H3ibs8fOH34eu1KxZgUJtq+ASGkTmdCztn/MiYv/dz7cnbL2/1GZZmPqmSKyf9KlVk2olTvAkN/WztmHRy2ztCtV9dwCM9VxO1EaBy1hy0L1ycDkVKfBgvRp7A3uePmXj2uGQ+SQFz10NpH1NC4vsUKJCBJl2ropArWTpxm0axhjSfwLcPyRcvko0GdYqSLYs7Li4OuLk64Ohkh5ODDTbWH9PWcfFynni+Y8ex21y5+5oEucqAUcTVg0ld6yMi0qFWafJlTc+QJbuJS/i+KdVSzCcVc2Snb6WKTD95Cu+w8M80UZGoiW/CQwHI75H+M+NJQY/0NM5bkP5lK+FoY4NSFPGPiuR/t6+x/uEdyXzyDcxBC1PL2k4ynmjAiztvmNxmHs0HNiZKwxLFn2LoBBpoZj5JwjkaJv7SGFtbayYu2sftx95qxaWG6ieaPPhPe75i8cVLjKxZnfF1arH+9h3OeL4iLDaW/Nbp6F2tHPk90nHvvT+HPFVViUZWqk6dXHl5GPiOmRdP8zJUtclqJQgEREfx390bdC5akmb5CnHqjScyQfhYDsvASTUwzqluwCTVT0wpFiIiTyNeAe40zZKf5tk68M/L+ZbWaiBQFMWUar6HJn6+/YmAASCKYrQgCEeBn4FKwGU+uiPdSJ6kr4d+43UJC2PxkNWkz5WRob91IvhdGDfOPNZ4DFOZT6ytZdSsVpCaVQuhVCq599CbKzeuExoWzfuACDzDw4iOjSc2Xo4imVMFHRuXpWHVIpy48pRlm8+hVIp4vQvlx8YV2D3zJ6asOsKZuy9TnJ8+DJumSKyJQIH06RhXuxaLL17irp8/1pEyFM7KDwulclmyASpNTGJslZp0KlKSp0EBrL5/i21PHgBQNH1GxlSuSZ/SFbj9zo8rvm8l84mGmLoy2PF3FwlLiGBU4V6MKDyVFS/+ICg+wCRz+YKU9C408bOkdRIGIyIkil/bzsPKw01j00kSpkisWVvLqFe7KA3rFgfg6XN/zl96RlR0PO8Dw3kZEkpkdByR0XEEh0V/FV+pZG4GdKnJY09//tp4huiYBLq2q8jAVtUpljszo/8+kOLcLK1CmLWVjGKZMzG+Ti0WXLjI4/cBiXP5eMK7oEd6AOKVHzdP+5WpxKDylUlQKFj34Db/u32dGHkC1XPkZliFqsyr15RBx/bzNDjQos0nYLy1o6nXjEZG0joJi0apFFk9dTtPb3gy5t9+LD08hsk9VvDivnr7l6bQSCsrGY3qFadpg5IAvPYK5NLVF8TGJeAfFY2XXzBhkbFERsXyxi/kqzVl/pwZGNu7IX4B4SzbeJYQEujbogrLh7Vn4c5zbDxx67vz0kXjjG0+cbSxoWz2bEyoU4s/zp3HMzjkw9oxaZQsTqoJRcR/1KFGeQowolJ1Cnqk5957f9bcv0WMXE7lbDnoUrQUHvYOjDl1hMhILK4imGQ+0QppH1MizXF49Rmy5s1E51EteO8TzLZlJzSKN5T5BJLXRysrGc0bl/qgjf7vw3jh+R6FQklAVDS+78MIi4whPDIWT+9AQsJjiIv/+Px2c3Fgcv8muDk78O/2i4QpE+jTvDK7pv/E4CW7eeUXzPcwlfkE1D/U52JnR6WcOZhYtw6/nzn3wZCp+KRSZvrEA3zhcbEf4spnycbIitWpnC0nwTHRrHtwm1i5nFKZsjC9Zn1yuLox9/JZg5hPwPL10MK00GzXdtq5BdIwj6+9YN5Py5AnKHBL54Sji33KQcmgTY9RQ/fjjk9QMGL8FkLDo/lrYkc6NCqjdmxS9RNt0LV/qK7xoHm/tfW37zBgzz6yuLgwo2F9dnXvxsbOHdn+Q1e6FitNZHwck84dB6BOrrz0KlmOBKWCmRfPfDCdgMqdKADOtrYoRRGbxB6lX/VgM0BP0k8xRt9uMH7vbjCfHm3eMVZ42KRnVOEZ5HEsYOrp6JuniZ9Dv/F6Um+ypP8ASdcX+vJCQRCsUZX7kgOeX74uYZkkneJ+9diXiX/3IkNWd63GMWR/bkheN+VyJX8sPsLRkw+QyWT4+odx7eYrjp58yO17XoS9DiMmNgGl8msdLJovM4O71SJBLufyHU9i4hKIS5Bz6PBd5mw8yaPX/sz+uSm1SqnfBtiU/bpBc7089dKTZZevUi57NsbVrkXjQgWxs7bGKlJGZqUzYyvXpHSmrLwKDebY6xcA/FK6Ap2KlMQvMoKpF06x69mjD+M9Dgpg6c0reNg7UCNHbkB1+ptIa4NppTE0Mq3p47Xge0x9sARna1dGFJ6Ko5WOrijjIGmdhFHweeGP13XVf5+aLcogk2neF9qQemkblvCVXsrlSuYtOcqZC6p5P3ziw55Dd1i/9TJHTz7kxS0fvPxCkjWdZMngyqS+jVEolOw9dY+nr97z1j+EtVsvs2TPBaoUzc2u6T+S2SPl54QuvbuT4nVBE4089dKTf65ep1LOHIyuWYOaeVSaJgLOsbb0LlWO2rnyEhQTzXVfVYXNZvkKMapSdWytrJhz+SwLrl3ELyqC0LhYDr58yr93b5DXzYMymbN+vJGB9DG1aaO5rBnNAEnrJCyCS/tvMqTmr1w6cg/vl+81ijW0Rn6JQqFkwbJjXLqmWutcveHJqo0XWfLPKbZvvML5my+599QHT++gr0wnrs72TO7fBGdHW3Yeu83ZGy+4d+MNi3ae48CVR4xoX4tZvZuoNTdtNc7Y2rjmxi2q5s7FiBrVKZtNpWdWiVXBWuQvTJtCxYiTyz/srZbKmIVpNetROF0GVt+7xejTR9jz/DFHXz1n0fVLbH/ygNo581Ixaw7VTSxQFyU9NAiS3kmkOlZP3c7p3TfoNb4lddum5L36Gk30Udf9VoVCyaIVJ7h19w2iKHL89CPmLTnKtN/2sWzJCbYeucWRC4+5dOcV/oERxMXLSTqrZm0lY0yv+hTJm5ldJ+6wdu819uy/zZ9bzxAZE8fmST9Qr2zKuRdz18VTLz3ZcPsO1XPnYmj1qpTInBlQaaJVpIw6ufLSpWgpRFFUtdABcru6M6tmA6pmz8XBl08ZfPwAf1w9z183LzP9wikOez6jc5ESVM2WU3UTSQ+TJRVpocm0TjKeaEtoKLM3DmTG2n7YO9qmfH0yaLrYAf0k0b6Ht28IA4at5/KdV4z6qT4Tfmn0WTuB75EazCeaLIiueL2l3r8r2XD7LqGxsWR3c8VKJrD74SOG7z/E09eBuNra0aFwcWysrFh28yrX/D4/hSETBEQgp4sbMkHgfVTUt3/eBkyqQeo3n5haMO6HPWPsvfnEKWIYWmgS5TyqmHQ+euYkqn3yYoIgJKcrSbVOXyV+PpX4ObkdklqAI3BJFEXD94KSMBoxkbFMafMH/0zfrXY55OQwhvkkOe38bdFhlv57imYNSzJpdHPq1SqCnZ3qmWwdKudLvyCAs6Mdu0/eIzI6np/aVKF9wzJ4uDqiUIrcuvqKBStP8ufWMzz30awKkqWZT1beuMnIA4colCEDcxo3ZHu3Lixt3ZLtP3Shf9lKKEWRiWePEyuXUz5LNgaXVz0fZ146zTU/b+RfmHqUourved09gI+9SwHJfKIhptTGJxGejL+3kFPvDhFtwHY7ekTSOgmjUrJkFiau6EWfKW20ije2WVOpFJn++z7Wb71Mu5blGT+8KZUr5PuwQWgblnzJ4+L5sxAYGomtjTXli+eiVOHsWFnJeBcUwZ79t5i14QTPvAPJmyWd2nOzFPPJiqvX+PX4SSrnzMncJo1Z3aEdk+vVYV2nDkyuVheAaRdO4RcVQT53D8ZXrYWVTMYfV86z8+lDwhNPeyetHx8FvkcQBOrkyvv1zQy0qWhofZSSbUZH0joJi8H7uT+L+v5NbHQ89o62pM/yrcOZX2MKg+akmbvZse8mndtVYvTgRpQpmTPxegV24clrZJXSecjo4Yy1lRXOjvZkzegKwIv7fvxv/2X+O3SVwjkzqZVgA92SbLrooybauOjiJRacv0jjQgX5rUlj5jZpxE/ly7Ksbkv+rKt61Px57TwPA9/jamvHtBr1yOLkwuZHd/nr5iVehAQBqmqa4fFxbHtyH3tra2rnygMkHloAi0y2SQk3vSLpnUSqQxRF5vdezt1Lz6nVsoxWYxjTfBIfL2fU5G0cPfmAHp2rMrhvPYoUzJJ4reKrHFnSvmudSgWpW6kQCXI5r3yCsLJS/QqfPf2EP7ae4dZzH6b92IhKRXKlOCdzN5/8ee4Cyy9fpWXRIsxp0pBJdevQtngxZjSsz6J6zQFYeusKN/x9sJHJmF6zPoXTZ+TYq+fMuXz2Qy5SJgi8jQhj1b2buNrZUylbjo83sUA9NAapRAtNpnWS8URLlEqRTXN2UaRcHv46OJr8JXKkHJQMxjKfaGJAiY6JZ8rUnazdfIm4ePnX1Te+g12EUmsDij5OqBk7oRavUPDnufMM2L2XNus20nb9RsYdPsq5V68ByGXlRomMmXkaFMDe55+3lhBQVTZxsLZhSIWqANx975fyz1syn2iNqQXDJ+Ydo+4uwCvak155h5DbMb9J56MvRFF8A+wHcgHDPn1NEIRGQGNUzsojiV/eAQQCXQRBqPDJtfbArMS/rjDsrCVMQbB/GIeWHQKgdLWCVKpfXKtxDJ1Mg+SNmzv33aTv8HW4ONszclAjOrQqT8YMLonXf71ZeP2BFwvWnqLX5A0Eh0Ux5Ifa1K9S+MPrr32COXbsPn5B4RrPz9R6qU3lkwb/reLQ02fYWltROWcOMjs7c+rFS0YeOMRVP2/sra3pUbwMLrZ2rLp3kyOezz8bI6kVj5udqtrc23BVBcCvZmJA84kxEmzG1khTaqN3jD/r3lwCILdjfqqlr2uyuaSEpHUSxubu2cfsXn6Utn3qMHpRd60OO5iiUtiqDRcYP30nhQtkYVj/+jRtWBKnxLknp5Wnrz1n4MxtzFhxmNoVCjDu5waULaJaW8fGyTl96jF/bj3NlcdeGs3NUswnex89pvmaddzz96dwxgx0K1OaMtmyctfPj5knT3PwperQUZ/SFcjh4sb+F09Y9+A2ya0YXWxVGvUmLDT5m1moOVMynxgPSeskLBFlcAiT/+nN3M2DcEuvfhU9U2jksn9PMXveASqVz8uQvvWpU6MwtjZWiGLyGnnp9iv6/LqRLYdv0qttFUb+WI+CuTMCEPwmjHXHbjBl1RFO3X6h9rxMpY+aaOPaW7fpvGkLobExtChSmIl169C0cCECoqL4+8o1Vt1TtRcaVL4KZTJn5bqfN39cvUBEfPyHMZLaunrYqzQkaavV0IcWUosmpnY9lPROIrWSEC9nWrs/mdV3FQDObpo/M4xpPgH4ffERlv57iga1i9G/dx0qlFFVgrQOTb4NzLPX7xn15y6evnrPiJ71+LldVTImVsd8cNOLedvOMn/7WTx9g9Sak7mbT/537To/79gFQM9yZfi9aWO6lC5FglLBxtt3WXhdtac2uHxVauXMw9PgQGZcPM27qI8TTMo3Otuo1uWZHb94v2RheihpoXqYUusM278jlXNh7w0mtPidsasGsHDvCNb+cZBd/5xG1MCoAZr3GAXNe6nBt/uNJocoqjYNAXCzolDuTDg52nL7sXp9U+0ilMS5aOdr0rWHqK7xoHkf0vdRUQCExqr6qQmoFjOZnV3I5erOlkf3eBf9uRoljf5L6QoU9EjPDX8fDrx8iloYqF83GL4fWxLG7t2dhKn7tEXIoxh/bwXVM5TjTfRLk83DAAwCygILBEFoDtxGVX6rDaAA+oiiGAYgimK4IAi/oBKzM4IgbAGCgVZA4cSvbzX6dyBhNJTBIbTvV4+K9YqxZ+VZVs3ZS0J88qe8voU2/bkBnfuQPn/5jm4//8O0Ca3p07MW2bJ6sH7rZfzfhWEbpiDezeqrcQKCIxkyezvzxrRlYJeavHwb8EFPFUoRuwhRK800tV5q2p80NDaWKcdOkMvdDYVSxN3enofvVaWxrZFRMG96ymTOimdoMJse3f0sVuDj5uHAcpUB8AoPBUg2+WZonTSGRhpTH02tjd4x7vyQuz5V0temG7+YbB5qIGmdhFH5Z9wmIoIi6T6pLQVL5WTOgDW8eeqn0Rja6KWuWnn1hifd+/3H3KntGDmwEZkyuLLn4G1Cw6K/0kqlKBIXL+foxce8fBvIgrFt+aVDNR55+hMdo0ogRfpEgpF10pga+SY0lIF79lEsU0asZVa42dtx29ePyPh4rJFRJmtWqmbLhW9kBCtuXSXhq0pgqnt0LKI6sBQQHfXtmxlIHw2tjcbUxaQNRgvq7a1vJK2TsDg2ztzJnP1jmb1hAOM6LyVKzUSYKTTyxNnHeL4OYM7U9owa3Ij0Hk4cOfmAqOj4rzQyMjqOyOg4lmw8y0uvAMb/0ojQ8Bhm/3MUAEVgHE/iNGs1BKbTR0208bavHz227qBc9my42dvjYmvLdW8fvEJDsUZGyfyZaZAnP+Fxsfx59QJhcbHJjlM6k+r0vE/kNw57GEAXU4smpgE9lPROIlUSHRELEbHYZ8vEHzuG4vXMn8VjtxATpf5zSRN91HS/NTlt3LnvJm+8ApkxqQ0jBzdi1foLnL34DJLZZ/XyC8HLL4Qrd18zqV9jerSqRHBYNDuO3QbA51kAb94Fo1Cqn9vTVtv0sWZURxPPv37DHT9/KubITkYnJ5xsbbjq5c3TwECslTJK5MtE8/yFiJXLmXPpzIfWO0nIBAGlKJLbTVU5+kWoypSTlMMEPppP9KiJhtRDSQvVxiRaJ1U80ZG75x7Tv+IErh5/QO1W5bCy1u5HaozKJ6B56x1VjIL+nWvw18SOdGlaTu24tNZ651OSHtgeDqpT2Y99ApCHKj+c1k4qFVY7Zx56lixLvELOsptXNLuJ1HpHa0ztVpSLcs4GXMM7xp0s9tkZXXgGmeyyphxoxoii6A2UB5YCBVG5KOugclVWF0Vx5xfX7wFqA+eA9sAQIAEYCXQRNXXwSVgc0zvMZ/eyo7T5uTYL944ke75MGo9hqpZ1CqXIlNl7WLHyNDmze5CQoDLNZMzggkOk+NVpNUFQxRy58Bg7W+vPqp4kYcpqYbqiqVZ6hYbhEx7+wXSSFJ1VpjJrXvZ5i39k8mbN7sXLUDFrDl6FhbD50b3v38iAOpkaW++Yui3dn0+2s+PtUZPdXx0krZMwNkqlyMbf9jK+xe84utjjrsFJ7k8xxanuoOBI+g5bx/HTD6lUPu+HEsjOTnbYhin4sruoIMALrwAOnX9EiYLZ6NCwzGeva1tZ05I08tH7AO75+3P+9Rsi4+M/6GMGJ0dyu7lz8vVL3kaEJRvbME9+2hYqRnRCAvtePPn+jSxUG9NSRTBTImmdhCXy8PIzZnZdTK5CWZi+ui92DupXCdNGIzVtvfMlnm8C6dH3Px4/86Ne7aLY26vma2dnnaxGAhw6/4jjl57QrFZxmtYs9uHrlqiP6mpjvELBFa+3HH32nB0PHvImNPTDujC7lSu5XNzY9+IJj4M+N98kjV4kXQaGJLZxvenv8+0bWdhJb5CqgekDSe8kUjtxfgGc3nyBGs3L8Neh0eQtmk2jeEO3pfuSG3fe0HvQauRyJc0bl8LFRZVXc4hQJrvPCvD7f8d5+MKPAV1qUDjvx31l62+0ef0e5t6OLiIujlMvPdl67z6rbtzi4fv3H1qT57JxJ5erO7ufPeTWO9/P4pJMJ1mdXBie2HXhebDKePLNg3x6JDVUPgHL1UJTaZ1kPNEDESFRzOy0gHFNZiNPUODk6kCVRiVSDvwCISTC4Ase0M58MmPWXi7cfMmwHnWZMaQ59nbqPYBMbT4xRULtU3zCVY76TM6qTWIhQvjQYierkwvjq9Ymnb0D/969wVU/9arJfIVkPtEKUyfYkoiQZyKdbQZGF5lBcdeypp6OToiiGCCK4hBRFHOLomgrimIGURTbiqJ47RvXXxRFsZkoih6iKDqIolhSFMWFoihq/u5QwuJIiEvg77Eb+bX9AjJkc2fp4dHkLJBZ43G01U5N+JZubttzg/HTdhIUHEk6DyfGD29KraoFE2M+/jdOekv2xjcYgNKFs2Nt9fVbMFNppqm1Mukda043VV/2x97viZYnfNgwTDJrls6UhV9Kqyr9Lbp+EZFk2uwkh4Um2CBtmTOViGz02meSe2uCpHUSpuDu2cf0Kj6Ku5dULciqNCqhUVINTGM+AVXp5Klz9xIUHImbqwPD+jcgZ/Z02IQqktXKa/ffICKSP1fGZMfTNrlmqo1E0F4jk/SxTFaVQf2x93uiEj7+nJP0MYeLK4PKqRJrC69fJCA6KmV9NJA5M7W1pDOH9aIpkLROwhK5cfw+v/deQZHyeRg8u6NGsYZeUyankQlyBWN/3cFvCw8TFByJq4s9fXrUxMnRFpvQ5H91Lt32RCmK5MmW7qvXUqv55FvUypsHK5mMu+/9P9PGpNPbDtY2jKxUHVsra9bev83tdylUjDOQJkoJN/NG0juJ1Iwoimydd4CxTefi4GTHwn0jaNK1qkZjGFsb/d+H07P/Sv635izBIVE4OtjStmW5xOs/GjNFUbUWkiuUXL7jib2tDUXzZvlsLFOsG7VFV02sXyA/1jIZN/x9v9LEpOqYoypVx93egf0vnnDe+833BzSA+cRQemjM9aGlaqEptE4ynuiRqLBolMEhtOtbl6krf2HYH12M0osbtNsY1MSAEh0Tz9Tpu/l79RnqVS7Ef9O7kc7NUa1Ybd33oJ+NPlMm1B6/D+CGtw9tihWlXv58AFhFyiiRITNLG7WgcLoMHHv1nNX3bhEr16GMlYHNJ8ZKrqWlBFsSTyNeMfLOnwTEvaNv/pE0ytzKpPORkDA2V4/cYUClSWxfcYq3L95pPY6pEmpJbQBcnO2wtbVmYJ+6VKmYLzFG8eGEt421FQVzZ0JE5Nnr98gVyeuirpqpLaZMrCWRVOVEIYpYR8oQAatEZ76LrS3jq9Qip6sbmx7d5fSbV8A33PnJYUDzSWpLsoHptVFCQuJr4mMTUAaHkNHZikl/92bx/pHkKqiZYVPTxJo+TnUDBASq7pkjuwcli2Vn3PAmZM/mkRijwEqm0g8rmUC+HBkQEFAovr2nYSqd1AVdNDIwsXXOy+BgrCNlWAnCh5NrAjC6Ug1KZcrCyTcv2fP8MWBafTSWNhoLSRMlJCyH87uv83vvFWxcdCTli5PB2Ak2gLc+qsMJhQpkoVG94owa3BgnR9sP5sxPq59kzuCKTBDI4JF8BTRLSrKBbtoYFa9ah19/7o11pAxrmeyzlgFDy1ehQZ4CPAp8z6p7N9Ub1AJNmZIeSkhIpMSDi08ZUHkiD6950rxHdaxtvm4R/j1MoY1PnvkDULlCPnp3r0H3ziqT/afGzCRDRdIBhoJ5vj64YOx1o67mE211MSFx7Xzl6dsP60X4qIn9y1SiXeHivA0PY6m6XRckPUwWSQvVQzKeGIANv25hy5/7aNS5MksOj6FgqZwaj2GurXc277jGmCnbeeMXTFiEZvczdfUTXdHm4R8ZH8+iC5ews7ZiduOGbOzckY2dO7KtdRfKZs7GZR8vhp04RHCsHvqRSa13tMbUghEYH8KYu4u5FXKFltk7Uy19XZPOR0LC2AT5hbDh180og0PInDMdSw+PoUjZ3BqPYwzzybd0883bYDbvvIZMJuPn7jWpX7sotjZWWAWrri+QKwONqhdBQMDTOyjFe5kiqaaPeF0WSnd8/XgdEkLX0qUonikT1pEyFKJIRkcnFtVvTuVsObnm683ft68RmRCv+Q0sXCMl84mEhATAO69AJredh2s6ZxYfGEX99hU1HsPQm4ff0srHT/04eOw++fJk5Keu1SiY2GbPKkRlwM+a0Y1yxXKgFEWevQn47n0s1XyijUZef+tDTEIC3cqUJp2DA0KEgCxCNc60GvVpVbAor0KDWXT9EkEx0ZpPzEIrg0nJNgkJieQ4u+MqvndeIAgCtVuVRUiub813MEWCDeDxU18uXH5OpfJ56dqhMhnSO2MbpviQUMuUzoXCeTKhUCp5+EJVvSO5b80USTZTnPK+66dKSjYqWED1hfCPr/1cqjz9ylYiPC6WedcufLNN3TexwGSblHCTkJD4HmEBEUxqPpeJLX5DnqDA0cWeXIWypByYiKm08dkLf54+96dF49I0rl8ca2vZZ1Uz07k5kj9nBuQKBfee+iY7hrFb0pnClPk0IBCActlV7ZSECOFDdcz2hYszrEJVEhQK5l+7wIuQlPekP2CBemgMJC1MGcl4YgAUcgWrp+1gbNO52NnbsGDPCKo1KaXxOMZoHwCam09u3nnD9Bl7UShFPFwd+KFFxWQXO8lh6eYT0Pzhf83bm6ar13H+1WvcHRzI5urKy+AgJh09zph9R4hTyNVrFaAuFp5Yg7TZeidemcDsxxtZ9GwNV4PPmWweEhKmxkmQ4+zmwLxdw+g4sIFRNgr1Vf3k0tUXTJm9G3c3B0YMbMiUsS1p3awMQ9rVYELvhpQrmpObj7w4dumxWvcxVbUwU1UKexcZyZqbtyiWORPTG9bn1/p1mVutETtadaVu7nx4hgYz7ORBvCPCUx7sWxjQpCmZTyQkJIzF7dMPGVhlEs/uejF6UXcGz9GspQCYplKYUimyfutlNu+4RoM6xRg2oAGd2lQgRzYPmpUpRL9ONahdoSDPXr/n8p1XKd7DEs0noLlGvggO5uiz5zQtVIhh1avSoEB+OpUswcqGbelRogzRCQmMOX2Eh4HvtZ+UhWqjsc0nki5KSFgOlarkYfyyn/jl1zYax5rCoBkVHc+CZce4dvMVXTtUpu+PtahUPi9OUVApVza6t6xIg6pF8PIN5sFzlfFE/EZ5K0vTR22Mmff8/XkRGESX0qVoW7wYTjY2lHHOyoiK1ZhUrQ4A0y6c4tzb19pNysKSbSAl3CQkJL6PUikSFhiBMjiEnye2YsHu4eQpkk3teG2qZ6rLt/ZZffxCWbTiBPHxcn7pWYuOrSuQPp3KmJkziwctapegbuVC+AWE4/0u9Lv3SM3mkxs+PgRGRfFDmdJUy50LgNwyd7oVK8WfdZtgZ23N71fPccTzufqVMZMwwD6qZD5J/Qjit96lplHcrNKLVeyb6208Z3dH+i34ifXzDhHor6HD+hNED83/IytcNfsli3ez0fgendpWZFCfuly87cn05YeIiIpTOzbORXvfU0LyVSWNFg8gd9b8dyeriwthsbHIBIHI+I+nteXOSn4sUZaC6dIz8+IZ4hQ6tN1JwlkPY3wHB2f1/611wd1JD9VgNCSLk+YVh/RNIecEBhQYy36frTyLfATA0vKbboqiWEHXse1y5xSzjh+m1rVvBo7Ryz0lzA99650+cXJzZPj/+lGrZVmuHn/A7AGrSYjT7JlmSt10sLdh5OBGVC6fDxdn+w9fP376ITvO3fuwUaguptJLU2lllVw5mVinNlldXXCytSUoKppdDx+y6/UjXoYGf1ZCWScMqJPG0Ehj66OxtHFPjeV60x1J7yTMWev0gcxKRveJbQmPlrNvtXbGZU31Ul9aWaNKAQb3rU/mjK6fff3Bc1/W7bvGxdueat/DUnVSU42cWLc2XUuXwsbKStXTXKnkrOcrVj66yRXft7pN5lMMoI+pTRf1oYn60jtJ6yRSu9bpQv8/fqDtoMasn3+YTVq03zGVRvbpWZOOrStgZSUjIjIW98RW52/9Q1i16zLHLj1R+x7aaKSlaGPB9OlZ3qYVOdxciYyPx81etfb2jghj8Y3L7Hz6ULfJgMHWjIbURXPSQ0nrJPSJpHf6I2P2dCw6Ow0EGNl6EQG+IRrFa6KP+tBGVxd75vzajiIFsxIQFIHX2yDy5clIOg8nwqPiWL37MjuO3VFrfEvSRU00sXGhgvzRtDEJCiXvIyPJ6OyEm7090QkJLL5xiX/v3tB+IknoWRNTgxZmcYqQ9jGTwXClEfSIIAhNgMWAFfCfKIq/ffF6LWARUAroIorijk9eywX8B+REladoJoria+PMHCJDo5nfezmydB7IZALlahXhxhn1Tjp/ihASofGCxyo8RqMHe5KzUBMDyrbd14mNTWBo//qsmtWdCQv38cLr+2WRk7CLUGq9SWgTqdtDW9d4+Og81EQA/CJUb8hlX5zgt46Ukcnaie7Fy1A+czYGHz/Ay9Bg3SaY5EQ00CIpJtLOKBuIoVEORk+u+Ue5mNx8EpKQGVuZHQMLjmeP9ybOBGjXo1jCcrBkrdM3UWHRzO6ykPv9GjBoQU869K/H5sXHNBojyYWviXZqo5vJaWZMbAKz5x2kaOGsuLk6kN7DGW/fEB4/8yM+Xg5uqn6qrs72hEfGpnifJFe+Npqpi94lOfR10UtttPKK11vab9hEscyZiEmQE50Qj3dYYpUTZ9V/8JIZM3M/4J32EwOVTlqwRhpbH81BGyUsH0nr9ItSoWTdzJ0AyNJ5ULVxSewcbDmz56baY2i6ztSXVl648oJHT/2oVa0QWbO44eHmyKOnfly96Ymff9gHrRSEb5/oTsKUOqmrRmqij3NOn2XfoycUyZgBO2trXgYHc9/Pn6iEBEich721NbFyHbXNAPqY2nRR0kSJlJD0zjz437hNOLk60GNUU6IjYtiz8qxG8abSyP/WnefG7dfUrl6I3Dkz4ORoy50Hbzl38Rm3/TRbA2mz92op2vg8KIgmq9bQt1JFcrq7kc7Bgcteb7nk5cXjWNXetLVMhpUg0/6An4H2Vg2pi5IeShgLSesslwCfYCa1/oMFJ6Ywc10/RrdfTGSY+s8NTfRRH9oYHhHL4DGb6N65ClUq5KNEsRzYWMu4dus1R6890ciQaUm6qIkmHn32nKcBAfSvXIlsrqrDHdvvP+Cc52suhLzRbgJfoud1YmrQQqnySfKYvfFEEAQrYBnQEPAGrguCsE8UxUefXOYF/ASMTmaIdcBsURSPC4LgDGjf60UHlMEhNB/WgsFzOrFq7n62Lz+h8RjGMJ/Atxc+32Lf4Tu8fPWe6ZNa8+/0roydv4frD7zUirV08wlovmEIoExm93ThhUvc9PHlz+ZN2N+hB5PPHWfXs0fJRGuIhSfWIG2aTwLighl1dyHji3Slfc4e5HDMzVI2mWw+EoYltWidvtn3vxP4er7j/n3NKoR8iqk2CwEeP01+3rZhClxzu7J2bg8Onn3I31svJKsLX6KtZpqDXmqqlQlK5Ye+3Z+PIyN/rnTsbvcDx1+/YMypI0QmxCczgpoY0KSZ2pJsYHptlLBsJK0zLMrgEBp3rkLlhiUoVaUAf0/bRXysei1VTaWVwSFR7Dl4WzWHLwwmtmEK8pXJxoRfGjFlyQG8/FI+hWcKnTS2+eTBu3c8ePd10tE6UobgCutadOBVaAi/nj9BnEKRzAhqYiDzCRj2ZJuUbJMwByS9Mx9EUWThoFU4pnOl1/iWXDh0l0C/UI3GMJVG3rn/lvsPvVEoRZyd7IhMrDBtC8S7WdGidgmevXnPs9cpt1nTNskG2mmcMbVRIYqsuHrt6zFQfb+Tm9ShfJZs9DuyF99IHZ7ZFmbKTGo1YKykm6SHaQ9J6yyf1w+9md55EbP3jGHE/G7M7LNSo3hjm08ANmy9wt6Dd7C3s8bJyQ4v72CUSvGDNjarVZxzN14QGf39Z2tqNZ+8Dgll/JFj2MhkWMlkHw4kWCND7qykevZcVMqWg4XXL2k3GTCI+QQMs0Y0phZKfI72NWmNRyXghSiKnqIoxgNbgNafXiCK4mtRFO/xhUAJglAMsBZF8XjidZGiKEYbad5fcXDpIU7vvkHvCS3pMaqpVmNo2ksNNO/HDd/uq/YtHj7xpe/QdZy5/pynaix6PiWd3JpqxXNTrmB2nO1tNYrVtV+aTaRp+nR/i3OvXtNqzQbuvfdnXr2mFEqXQS/jGqpnN6jEwdB9SkElFMbs4Q2m7+Mdq4hj+sO1bPY6SDmPKiabh4RRSDVap29uHL9PnH8gzu6ODJjRHjt7zdvCGVo3v9Wn+3tE+0Ry9vxTerSqxPxx7XB1sk85CNP069ZHPOhPK196BfPn2fM0zFOAfR2660crDaSTxtJHYyK5+SV0QNI6AzOt/Ty2zNtP0x+qsWDPcNJncVM7Vhut1LRv9/e0Mjn/pUMcZPBwZuXMH6hSOo9a9zGFTuqjh7deNDIcrr7ypnPRkmxr05Vszjo+ry1UG42pi6ZeL0qYLZLemRFKhZLfuv/F2I5LNDadJGGM9WRyKJQqcYz8oq25S6zAT20r8/evXahdoYBa9zC2PupDG/XBpede5HHzYG/77lTMml23wQygi6lFEyUtTJNIWpcKuHvuMbN7LmX13P1axWuij9qsH5MjIjKWgKBIXnsFoVR+XETmdXRhfJ+G/D21C1kypPxM0kYXdckl6qKLmq4XE5TKr6pgWkfKaJAtP8MqVGNJgxbYW+ugaRamh8beN5VQ03giCIKdIAgtBUH4WRCEhoIgGPNfKjvwacNi78SvqUMhIFQQhF2CINwWBOHPRDemSVAqlPzRcwmH15yh2/AmjFzQDUdn7X6hDL0xCJqbT4JDopg79wCxPlFYW8kY+WM9MnqkbOWrXbEgv//SgsUDW7NkaFvaVC+h0X31YR7RV0JNHwujd5GR9Ny8gx+37eBZcCAAHvZ6+JWLtDa4AcUYmEIoTLmAEhHZ9vYQA25ON9kcJIxCqtE6Q1GsSCaa96zB7E0D8cjkqnG8uemmXK5k0YoT/LH4COWL5WTlrB/In1M9A4Uum4amTKyB/rRy5Y2b9Ni6HWcbW/a0+4G2hYrpPjkDJtiMsaFo7ESbhIQWSFpnYJQKJaunbmdS23lkzZWBP3cMxdndUe14TbUS9JdcS46Hj33pN2wtvu/DmD+mHT+0UK99sanMJ6ZOsonA4ouX6L97L/ncPdjfoQc1cuTWbVIGWkNKuiiRypH0zsxIiJfz+NQdAOq2KU+LH2toPIamBwH1lWBLjrg4OYNHbuTl2wB+G9maXm2rIKghIaYwnxgz0ZYcp1560mH9ZiLi49jUshM/FCut03iWqonGQNLCNIekdamEywdu4XX9KQANOlbC2kazfwpDmjM10UYfv1DGTNlOpnTO/DfjB4rmy5xiTFozZc4+fZbfz56jeYHCbG/TRbeDChamh5L5xLikaDwRBCEDcBfYA/wDHAGCBUH4VxCELJ9cN1wQhCeCIPgJgnBNEITZgiDkM9TE1cQaqImqnFdFIB+q8l6fIQhCX0EQbgiCcCNeNGxJdKVSZPHg1WyYs5sazcqQPou71mMZa2NQUwMKQNF06Wleuzhr5/agcqnvb3bdeuTFmD93s/P4HRJiEpjwQz3+N6I9mdw1qz9lDuYT0I8rXymKXPZ6i3WkjMpZc3Cxe1+6F9dxcZSEZD7RClMvoILiQ016fwmzRi2tA+Pqnb65cug2c3osJV+x7Cw9NJriFTV/i2GMqmGaaubBY/cYNm4zdjZW9GhVSe04uwilxSbWQD9aed3bh9ZrN3LvvT9ZnfTQOw8MatJMbdVPpFPeEkYmTWidvrhx7B7jW/zGmb03iQzV7PCguZlP3gdEMGTkRk5dfcbgbrVpVL2IWnGWatLUhz6eePGSdus2ERgTzfSa9bGW6aHQrYVtLCYhmU8kLBCz28dMVYSFUbt1eQbN6sjUVX1w9XDSeAhDJ9jU1cjgkChGjN3CsVMP6duxOjOHtFArTtt1pCVr48vgYNqv28x57zdMqlaHLLquHQ2wZjT0YQVjmk8kPZRQA2ltZ4YUKZSBUQt+YM6mgRrro7mYT27f82LQqI3ExctZPqUzdSoWTDHG2LqoD1OmLvx77Qb9du0ht6sH+zr00N18YgA9NBSS+cR4qLMDMRmVC1EAngGBgB3QG7giCEI+QRDGAPMTr8sMVADGA08EQZil4xx9gJyf/D1H4tfUwRu4k1jyS47KPFPuy4tEUfxHFMUKoihWsBUMv/khiiLrZ+/mx2IjeftC1Z+5dquyGrsJwTgbg6B5Iu3pC3/6DVtHcGgUi8Z3YFDXWlhbJf/fzT8wgttPvFmx9QJz/zvO/7ZeoHieLEzp0UAr80lqSagl8conhOt+3syq1ZDljVrhaquH/6NS6x2tkBZQEgbE4FoHxtc7fXNh7w2G1Z5GdFQcv20dTPWm2hnyzM188vCJL78MWcsfq04AkM7N8Zua+SVpvfVOQFQUP27ewb/nbwBQOWsO8rl76DyuZD5RH0kXJTRA0joj8vSGJ2smbAQge96MGlULMzejZmxcAjNn7WXmH/s5efmp2nGmNGnqgj5OeL8KCaHDuk303b4HuVKJnZUV7nbqtfX7JgYyn6SWk94gaaLEB1LdPmZqQalQ8mvr31kxZgPlaxVl+fFxlKlRSONxzEUj4+PlzJ5/kKX/nuLhCz+N7pHWzCcRcXH0376Xjhs34x+lmoyLrWbt3r/CwkyZkh5K6BlpbZfKeHTlOXN/Wk7hMrlZtH8kOQukXDHkU7SpDKYumqwd37wNYsDw9Tx/E4CdrfrPaUvSRV018bTnK9qv38S2x/fxjdQ8v/wVkvlE4gvUyWo0QVW19UdRFIsCWYBmqEwoOYGNqEwmAnAc6AtMAW6jci9OEARhsQ5zvA4UFAQhryAItkAXYJ8Gse6CIGRM/Hs94JEOc9ErYYERKINDKJjPg/HLfmLh3hHkKqjZAx203xg0dCLNyzuYAcPXs/fQHbo1r0DRfFm+ea0ssSak7/swNh+8ycGzD6lSLDel82fT6J5JmEtCTR9JteDoGPps3c1vZ87RME9+DnbsSbnMWXWfoNR6R2ukBZSEAUi1Wqdv3jz2YUj1KRzffpWH1z21HscYm4Wa6GZIaDRy/xgcIpQsGNuOZZM7kcFdvRMGlnqqG/RjPlGIIiJgG2XF7NoN2de+B60KqHci/rtIrXfURtJFCTWRtM4UhIUxfW0/ftsyCHc1el1/iqHXmJquL0+cfYxViJz07k6sndODskVyqBVnieYT0F0jYxLkvAkNxTpSxsSqdTjQsSelM317Ta4WFmrMlCqCSRgZSe/MnD3LjzG09lSiwmOYtWEAWfOo1/L0U8zFfAKwfc8Ndm6+BkDFErmpWCKXWnHaJtkstcWAUhR5GhCIdaSMzkVLcqJLb8rqur9qgeYTqfWOhJ6QtC4Vcmb7FcY0mYOdgy0L9gynbM3CGo9hSPOJutoYGhbN0FEbOXrxMQAlCmbFSo0DfpZmPtFFF1+FhLDg5EUAcru6M71GfeysdOh4ZUGVwExxoD2toY7xJCcgBzYAiCqOoCqH5QlUAtyBS0ATURT/E0VxtiiKFYAfgBhgsCAIVbSZYKLrcTBwFHgMbBNF8aEgCDMEQWgFIAhCRUEQvIGOwP8EQXiYGKtAVbLrpCAI91GZY/7VZh6G5OkNT6Z1XkTGbB78dWg0rX6qiaBOc84vMMfWO3FxchYsO8ZPA1dx/7nvN69TiqJqPjIBpShy5e4rBASalEm5HNa3MAfzCegnqSYC/12/QdfN2wAol0XdlolqIJlPtEJaQEnok7SgdfokOjyGRb/8TfAzL2RWMgbN7mi0zUJDmzYVSpHNW65QIHdG1szpQfliOVMOwrJb7+jLqKkURXpt3cmToAD+atiCWbUa6LZoAqn1jgZIiTaJlJC0zjQoFUoW9PuHTNk9+G3LINzSa1ZR0tBrTG1au7rGy7CzteaviR1o37CMWjGWrJH6YPftRyCKbG/Tle7Fy+g2mIG0UTJlSqQWJL2zDDzvv2Vw1cnM6bEUv9eBADi6aFYZypzMJ6rrFfRqW4WF49vTpWmyxQO+wtj6qI8WA/rQxvuv3hGnkLOldWc6Fimh22AWqokSErogaV3q5cn1lwytORV/ryC1zBrJYSjzCaivjUqliG2YguzWDiyd1IlF49rh4pTyszWtmTKtI2XUypiHH0uWZWvrLrq1o7MwPZS00HCo8+SQAXGimOgMSEQUxUDgD1TCALA0mWs2A7MSr/lF20mKonhIFMVCoijmF0VxduLXfhVFcV/in6+LophDFEUnURTTi6JY/JPY46IolhJFsaQoij+Johiv7TwMyeUDt+hXcQL3Lr1gwMwOjF3SQ6txzLX1zpu3QdiGKb4/D5mAQilia2NFjizuALwLjMAuQvmhIkoGNycK5cj4nVE+J7W13rnj50eLVetYeVfVUqBqtpy69WFLwsCJNan1joREyqQFrTMEWdPZUatlWf46MIpK9YunHPAFxjJtasLpC0/pP3w94ZGxLJ7Ygd5tq3zQwZRI6613/CIi6b5pO/9cu0734mXY2babWbeoS23mE5ASbRLfR9I60/Dg4lOmtJtP5lzpmbt5kFY9u80puebjF8qAYeu4eu81o3vVZ8IvjbCxTtloqEv/bktuLwDw4N07Wq/dyAXvN8yq1YBF9ZvhaG2j26BS650UkTQx7SLpnWUQFxPPhb03UAaHULxiPtZenkqDDhU1GkOb1gKGrA42YcoOzt94ybAedfl1QBPsbFJ+VltiZTBdtfFZYCBt127iqq83f9ZtwvQa9bGWaZdgBQyyr2poTZQSbhK6Imld6iXAO4gh1SZz44yqYkipqgWQaWhCMRdtDAiKZP6So5QukoN/pnUlWya3FGNMYcrUBV01cfPde/TfvZcCHunZ36EHFXQ59G4gPTQUkhYaBnWeFj6AkyAI+ZJ5bc8nf772jfgkt2INDeaVJgl5F8bklr/x19DVnD9wR+txtE2iGTqRpopRfDCgfJlEUyhVvqV6lQvRuHoxYuIS8PILBsAmXIFdhJIxneuwadIPjOhQS6P7mktCTR+bhlEJCVhFyrCPtubPuk043OlHmuTTvjLMZ0jVT7RC2lCUkDAdb5/6MrjaFPy9gpi+pi/dRzbVuGqYsRJqGvckHbaOk2ceU7dyIbUSakmk9dY7cqWSP86e55edu7n73o/w+DjdJwYWbdCUTnlLSEjcO/+EqR0WkC1vBnqOaa7VGOZkPomKjmfy1F2s23KJVnVL0rdjdbVjLTXBpqtGhsXG0m/bHuafv0C93PnIrMtptiQs1JgpaaKEhMSX+N335OVDH0Yt7M7gOR21WlNqgqGqg8XExDNtxm5Wrj9P05rFWf5rZxwdbFOM08WcqS2mTrSFxcbyy9bd/HvtBj+WLEulrOq18Psu0mlvCQmJVIJSKaIMDiF7BnvmbB7EzHX9cHLV7JlhaG1Ul6OnHjJq0jbSuTny7/RulCyYLcUYY+uiPiqC6cKJFy/psGETEfFxbG7VierZ1Wvb900k80maRh3jyWlUFUvmfvmCKIoBQNJufrJ9VERRDALCAT32BkndHFx5mgubzgLQpk8dRi38Qatyj+bYeudjnAKlKCII4OHqQLH8WejWvAJ/jGrDlP5NKZQnE4fPP+TCLc8PMfWrFKZ+2YL4BIXRoXYpDv/WhypF1X8A6sN8Yi5JNVAl1npu3cGr0BD+btyaObUb4qDrqTUwuPkkNZ7uBsvaUBQUqjJq6nxISFgC77wCGVFnGsfWn+OHEU0YMreTVuOYW+udmNgEZs8/yNBRG4lLkONob0OpQikvjkD7pBqYvlKYvoyapz1fMfXQKQDyunkwoWptbGVS6x1jYQ5VwSS9k5D4nDtnHjGu6W/8O3OP1mOYk/lEqRRZuf4CE2bsYt2+qxrdxxLNJ6D7WlIEVly5Rr1/VvEqLASAcpnVe2/xTSy49Y6xMKQeSlonIaEfAn1DGN94FtsWHKB5jxoMmt1B4zHMRSNFEdZtucyEGbt47OlPdIz6hQTSWosBhSjy+9lzNFu9jks+XgCks9dRHyxMEy0h4SZpnYSE6Xj7zI/Fg1dRqmpBFuwZrnGrc3Mxn9x98JYBIzYQFRNHjfL51Y6zJFOmrpr4IiiY9us2s+b+bW74J5vu1wwLqgRmim4KyZFa9E6d2S0A4oEOgiAcEQSh0hevFwV+EEUxNrlgQRCsASfAcNnsVIoyOARbpZy6bSuw/OhYSlRW/4GYhDm13hEEcHN1oFjhrDSuX5xhHWqycFx7Vs7szr/TuzGoay2qlc3L/ee+zPnnKP9sv0hAiOpJm97diUFda+IXGMay9WeZuuQgvkHhLB3ajmk/NlK7HKI+EmLmVP3EKzSMLhu28veVa3QpWooDHbrrr6WAhVc/kVrvSEikLeJjE5jf/z/+Grqaw5suaT2OObbeiYqOxzZMQe92VVn+a2d+bF0ZdQ7gaevOB/PRSn1gHSmjfpZ89CtTkR1tu5Lb1V33QSXzidpIuighYV48vvaCGJ93ODjZMWh2R5zdNH8mGNqoqenhhktXXxDnG42tjRXLJneiWS31Wu+ZokKYvgyauhIaG4t1pIxm+Qqxq103plavh40uLQbA4hJtYNw1o7RWlJAwf5RKkZVTtrF1vsp8Uq1JKY3HMBfzCaj08a9FxwDImcWDlnVKqBVnihYDuiba9NF6xzpSRrnMWbnQvS89S5TVaTxL00RzSbhJSEiYJ0fXnWN8i99wz+DCon0jKVlFszyluVQF8/YNof/Qdfxv6wUAsmVyU6u1uSWZMnXVxIi4OH4/fg5FmBJnG1vm12tKJkfNWvV+hgXqoYTupLizIIriY6AfoAQaApcFQfAVBGG3IAiTgCLAye8M0QGwAt7pYb5pji3z9jOy/kwUCiW/bxtM7wktsbHV7LSuubTeadeyPIt/68qi37oyfngz2rUsR4EcGfDxCebfHRcZ9ecuuo5eQ//pWzh8/hERUR9L4//SoRqZ07ty4MwDzt54wfmbL1m48iSrj1yjQbmC7JvdmyI5M6k9V3NIqIH+WgrMO3+Bntt2cNrrlf5aCoDFm09Aqn4iIZHWOLjyNE/P3AOg94SW1G+vWY9uMJ75RFMDyvo1Fzl55Rn9O9dgwdh2uLuo93xL6613ANbcvE3/3XvJ5erGwY49aVWwiO6DWnjrHWMi6aKEhPmRL7cbjbtUYdaGARpX1wTzNGo6xwgolEqm9G/CqJ/qYa1GH3JTmTTNpTrYqQeerLpxk16lyrGtTReyOun4vDbQxmJq0kVJEyUkzJ9Vv27j1/YLuHTknlbxmlahNrQ+2oYp6NSkLBP7NmZM7/pq66M2mFobdeW1dyiXfLyYUbM+C+s3062ytAEO9aUmPZSQkLAs7l94yrBaUwkNjKBQac3bsJiLMTMyKg7rUDkeCVb8PbUL88e2xcUp5WerKUyZuqAPTSzumIkm+QpyoEMP3apkWpgeSlqoO2odaRFFcS1QAziPqu1OFqA1MAM4CPgLguAlCMIuQRAmCYLQRBCEDIIgNACWo6roesIg30Ea4Mn1lwyoNJGja8/Rvn898hfXvOekNhuDoL9Emo21FXlzZyB3zvTI5Qq27r5Oj34raddjOaMmbWPT2ktcufuat/4hX8WWLpydlnVKohRFomLi8XB1BOD5mwA27bjKlNVHOHv3JbY2mhlyUlvrnSteb/nt2DkACnik5+/Grcjg4Kj7wAZuK5AaT3eDtKEoIWFqrCIjKFQmN6MXdWfgrA5Ya6gR5lQxLImYmHhmz97HvCVHKVs0J2vn9KBI3sxqxUqtd1T9Sluu2cDjoPf81aAFbQoW1XlMS269Y+xTbZIuSkiYF/cvPGXWD0vIXzwHs9b3x9FZ82eONu1dDZlci4yKY9yEbWzddZ0Ojcqy4tfOZEqn3rPHEhNsoPtaUq5UMuf0WQbv3U8Bj/Qc7NhDP/28LexkG0jmEwkJic+5euQOyuAQcuTPRNdhjbQaQ1PziaHaCwAsW3KCTTuu0q5BGZZM6vhhb/V7aGvOtGTzSXhcHAO272XB+Yu0LliU3e26kcfNXbdJWVCrAZASbhISEt/G1/M9g6tNZuf/TgOQI38mZDL1n7vmtHaMio5n9boLlC+ei/9mdCN3tnQpxqQ188lNH186bNhMtDyBLa0707Wo5pXgPkMyn6QZ1K6lKoriVVEU6wC5gZ+AZcBVIAaVGSUH0IaPZpR3wFHAHZXx5JIgCOplRyS+IjYqjkWDV9G3wkSe3H4DQM4Cmv04tdkYBP0k0hLkCuYtOcqSf05iZ2tNlQr5qF4pP9myuH+4xi5ckexYcfFy5q85yfX7bxjeoy6T+jWmTJHsAETHJnDp/HP+23iBe55+Gs8ztbXeUY0lo4hDBurkysvhTj9SN1c+vYxr6dVPpNY7xkUQhO6CIIiJH32+eK3OJ68l9/GbqeYtkXpIiJczvslsdiw+RMsfa/LH9iFkyZVeozHMpWLYl+w/cpdBozbwPjiC0IhoteOk1jvgFxFB943bmXXqNIc9nwNgpU7fopSwUPMJGD/Rlpp0UdI6CUvnyqHbzO6xlIKlcjFz/QA8Mmr3+2lOG4gKpcjylaf5dc4e8ubIwLRBzdSOTcutd448e07bdRt5FxWJrZWeNE0yn6QaJL2TSMvUaVKCnqOb029aO6ysNW9JZi7tBZRKkf+tPsuMP/ZTJG9mVs/uTo5P9mS/hyWaT3TRRhFYfuUqvXfsIrOTM83yF9ZtQmBxmpgW9VDSOgkJ9YiLjkcZHIKbjZKFe0fw6399cHFX//CzMdq2qsvBY/cYPn4Lzo52rJzRjRrlUs6jGduUqY92dLrwPDCIdms3ccnHi7l1GtGndAWdxrMkM2Zq1EJjaZ3G/8qiKL4F1iV+IAiCDCgKlAPKJ34uAzh/EiYD/km83h+4nfQhiuIuTeeQlnn71BeAko3L8/v2IWxffoJ1fx5CFEW1xxBCIhA9NNtUTHq4K1zV/2WzDUsg3u3zkoS79t/i0rWXTBzZjMF961OqRA72Hb7L3QdvkcuV2IapzCfxbh9Ppj959Y4nr96x68RdWtQuwdDutRnYtRaTFu8nIFj11I2MjsMOiHPRri+1TSQkOKd83ffiQbcxQCUEcmf1/y2/xZFnz3kZFMyC1s1Y3bwdmx/dY9al00QlaJbc/IpIa3CW6zy/5EgSCAdnPbYKSobQKAfcnTQ3U+mCf5QLWZy0qzpkiQiCkBNYCkTyuRZ8yVngTDJfv2CAaUmkQZQKJf9O3MLTG56MWN6H+buG8VP1GSTEqf8cS1oQaaObumrm93ju+Z5BwzcQ72aFIECvtlXZfeIuIeEpG1HsIpRa6aWuWqcPrUxaMOmilQpRZM3N2wA4ediws203tjy+x5r7t7WfGBhMI42hj8bWxtSgi5LWSaQWLu2/ydwfl9Hvzx46jaPpGlPT9WXSBqK6Wnn24jM8Xwcid1WtK+1srJErFCiU39ePpE1EbXVSF43UVR91XUe+Dgml9ZqNKEURnKFlgcJc8fUmIDpK+0ENoI2G1sWkDUZjrxnNFUnvJNI662buxNHFnraDGlO0fB7+GLIe39cBGo2hjUYaai158uxjvN4G8UOParwPUv/9uDZrSF3Wf/paO+qijRdev6HZqnX4C6qfU373dLwOC0GhwR78ZxhIEyU91B1J6yQkNCfkXRhrpm2n3+8/sOLEeBaM3Mitc0/VitU2P6nJ2lFdXXzw2Ie+Q9cxa1o7WtcrxYVbnmrFaauLplgv6qqH4XFx9N22hwFVKnHA84nW43zAgvQwNWmhMbVOZ3uRKIpK4GHix3oAQRAEoBAfjSjlgbKAK5A18aMZoNTHHNIij07e4ejas3QZ0ogsudKzYNQmjRNpmj7cQbvFD3y+Qej/Loyh4zbTulkZBvWpS/Ei2dm57yYnzj4mIDAiMU7xmfkkiQNnHyBXKJjSvyljezdg/MJ9KBQfHYbaJtNAP4saXTcMQT9JNYDnQUG0X7uJYdWr8kulivhEhLP01hXdJgcfXYkGNKAYw3wCxhWM1JBkU4fE5/9qIAjYBYz+zuVnRFGcZox5SaRtzu26xuNrL8lXpcgHrbSylqGQq+9QN/SiCDRPqqliFOTLk5GerSrStn4ppi8/zI2HXinG6aqX5qCV+jBq2sfY8DYijGk16lMrZ17GnD5CUIz6VWS+woAaaWh9NLY2WrIuSlonkdq4sPcGVw/fQeHsgkwm0GVII/avPU9EqGbPQ2Nppbo6+dYnGHzAFhg9tjHZM7kzdelBAkJSPjKmi0nTlOYT0G0dqUxMpqWXOzKndiNi5XJGnDzEBe832k/MgMbM1GTKNEckvZOQUPH32I08uPiU4ct/ZumRMYzvvJRnd1Nec32KOZlPnnu+Z9r0PcS7WeHsaMcPLSqwevcV4hOSr0SdhKVqoy66GBAVhRUyXOzs2NamCy9Cghh8/ID2pkwDrBeNYchMzXooaZ2EhPbs/+ckj66+YNzqgczeOJDd/53hv5l7UKZg9gftDvppaj4B9fZYAwIjGDJyIzbWMrCBDO5OxMbLiYz+/nPVFKZMXcwnoP1aUSmKLLt8FQDBGf6o24TtTx5wzc9buwkZSA8lLUweY2uddhmHFBBVPBVFcZMoiqNFUawriqI7KjNKF+BP4BQQZoj7pwUUcgWLh6xm5ZSt1GldnrmbB+Hq4aTRGKZsvQOw99AdOvX6H6/eBPJjt2oUL5JNNS8BHB1ssQ1TYJ9M2aqTV57xyjuQwnkz4+7ytdDo0koAzKOdAOinZHK8QsGf5y7QceNm/r17HYC8bh7YWX1t6tEYA7feSW3tBSD1tRj4BkOBekAvQIfjkRIS+iXAO4irOy6iDA6heY/qLNgzArf0mr1bN9fWO56vA+g/fD0RUXEsntCBvh2rY6VGj1Wp9Q6ExcYyYPs+pp04SfXsuTjS6Udq5sit++Sk1jtqYcG6KGmdRKojIV6OMjiEAnk96DqsMStOjKdCnaIaj6PNGtPQOglw5/prCufNxNq5PahSKo9aMWm59U5YbCydNmwhJDaGdS06MKpSdd1a00VaW1ybAUid5ZU1RNI7CYlELuy9Qf+KEzm65QovH2qXZNFUIw3ZXkB1vYKahXLzU5sq/D21C1kypPy+3NgtBnSNBf3oYkRcHHNOnaVUxiwc6tiTyllz6DaghWliKtdDSeskJHTg5d03DK42mb0rjuPobK9xvLmsHePj5URFx2MbpmDWsJasnNmNvNlTbtlu7H1Vc9DEzKIz5bJkY1OrTvQqWU63wQzQesdQWLgWGlXrDGI8+RaiKL4QRXGbKIrjRFFsIIpiyr+5Et9l24KDzOq+hIIlc1K9WWmtxjBmIu3LB31oWDRjft3OhOm7uHTtJQAdWlegS/tKgKoHaVL7nSTsbK1BEEjv5oSz47cfJKY2n5hLUg3grp8/ijARh2hr1rZoz74OPSieIZPuAxtoAzGJ1JZgS8JCk2wpIghCUeA3YLEoiufUCCkgCMJgQRAmCoLQWxCEggaeooQEAO+fepOrUBbm7RpG5pzpNIo1lmlTY/PJm0D6D13H4RP36dW2CtMGNVc71hSJNX3Eg+79u5PYcPsubddvIjg2hsHlq+g8HmBQ84kxEm3GNqBYCpLWSaR2nlx/ydDa0wgPiWLm+v4MmdsJe0dbjccxRu9uTbTy+OlH9Bu2jqDQKBaOb8+ALjWxskp5O8RUJk1z2FB8HhRE+3Wb2PngIUPKV2Vjy07IdDGfgMESbYbeYLTwTUatkPROQuJrAn1DWDF0JQnvg3B2d2ThvhGUqVFI43EMmWTTVB9Pnn3MhBm7yJnFgzWze1CppHomfG3NJ6YyZupj3bj30WPabdhEeHwcG1t1ol+ZijqNJ5lPTI+kdRIS+iE+NoHlo9ez4OflKJUirh5OCBqsG4yxdtSEf/49g6O9Lf/N7Ea9yinrvLFNmfrQRF0IjIqm/dpNnHrzkqk16rGofjPsrXXQNAOYTwylh5aohabQOqMaTyQMw/nd1/ml/HgOb7wEoPXGoCmrn9y+50V8vBwHexvSezjRtUMlenevgUuiS/JT80murB442NnwxjeYlPRL141CcznRrS8DSoJSybRjp3C3s2dPux8YVK6ybifXkkgF5hNTVD9JTQiCYI2q3ZoXMFHNsB+AJcBsYCXwTBCEHYIgeBhmlhISKq4eucOE5r/hls6JBbuHk7doNo3HMJb5RJPFUWxcAn8sPsLMP/az6/gdje4lVT+BZ4GBtFu7iaG7DwKQ3sGRfO46Po4MaNBMbeZMS9BFSesk0gov775hSNXJbFtwgCbdqjLl35+1GsfcjJpe3sEMGLaefYfv0LZ+KTJqUDHUUs0nuupjTIKcCUeOMeLAIY6/fvGhFY9OWKguWuImo7ZIeichkTIuMgWOTvbM3TyIPlPaYGOrWWVhcznhDXDp6gv6DltHYEgkC8e1p1nNYmrFWao26sLzQFVb86OvnlMqUxbdJgMGWS9KZkz1kLROQsIwOInxLD4wiv4z2mkUZ+jKmZro4oPHPvQduo6XXoHMHtaSgV1qql1ZWlN0NWVqi65rxcj4eAbt2M/88xdoVbAofzdurf1kwKLMmJakg6bSOsl4kkrwfx2AMjiEXAUzs/rir9RtU16rcUzdRiAmNoEDR+9x+54XbVuUpdcP1SlSMEtijILiGTLQrkFpsmRw5fmb9/i8U69bk1T95CPnXr2m2ap1HPZ8xpjKNdnRtise9np4WBo4uZbaEmxgMS0GMgiCcOOTj77fuO5XoCzwkyiKKT0QAoDxQEnABcgINAVuA+2B/YIgSPokYVAeXX3ByPozUSiU/LF9CG7pNGtXB9ovigxd/eTE2cc8vPoGgH6dqjO8Z11srNXbCDVl9RNd0UeCLU4uJyg6GutIGVOq1eFAh550LFJC98lZaJIN0pT5RB29k7ROIs2QEC9n5ZRtjG40h/XzDgFgY2eNtY3miTVzSq7Fx8uZv/QYPw1YhX+gal7F8quXOErLrXf2P37C2ku3AaiTKy+zajXAzkrHU20WtLmYhCVtMn4DaW0nIaEnfF74M7jaZPb97wTt+9Zl4b6R5CqYWaMxzEkffXxDGDh8A7sP3OLa/Tdqx6XF1juR8fEM23WQMXuPAJDL1Y1i6TPqNikLOu0NZq+HktZJSJiQiJAozu+6SqufatF3aluN482lKlhQcCTDx2xi78Hb1CyfX9WFQQ3SUusdEVhx5Ro/79jFXzcu6zYRsCgzppkYMc12H9NwZQokTELgUy/ePPNn7JKeZM2dgU2Lj2o8hhASgeih+ca/VXgMClf1f9mSHvLxbjaffd3bN4QJ03YyrH8D2rYoR+Xy+Xj9NpCQ0GiqVsxPOg8nXngFsP3YbRLkCgQB1Dl4lfTQj3PR/H1g0gM8wVnj0M/G0CUePgqB3Fm3k2ZhsbGM3HOYE4Vf0rREIUJjNa9c800ircFZrr/xPiEm0g4H5ziDjJ1EkmC4O+nxZ5IC/lEuZHHS3PSlNUqN3lQEiqJY4XsXCIJQGZVjcr4oiim+yxBF8SHw8JMvRQJHBEG4BNwBqgMtgb3qTlJCQhu8nvgyos50yreqRFiw9u0NtdFNfWnm92MUOIpWdGpSjtKFszPlrwN4vwtNMc4uQqmVVoJuWqcPrQXV801XnQSYd/ICmVs582fdJtTOmYeJZ48THq+DBiUtnvSskUmLKEPqoym0US/oUe8krZNIqzy8/OzDn3st7k3uQlmY1XcVMVGaPXM01UpD62RgUCS2QP0WJfl1QFM27L/O39suoFB8f6NQ1zWlLhqpiz7qax1pHSmjpEtmuhcvQ8WsORh8bD/PQ4K0H9AAa0dD66LZaaK0tpOQMBlxMfEsG7mOG8fuMfJ/v/Dj2BbM/GWlRmNoo4+A2hqpiT7GxiXw1/9OAiBzt2Zy/8bsP/2A20+8U4zVZg2py/pP17WjPnQxXqHAOlLG1Kb1qJ4jF1POnWT70wdaj2coTTSkHhpNCyWtk5CwKP6btAVrWyvaDmyMPF7Oqrn7NYo3xtpRHV2Uy5UsWH4cRwdbom0V2NlakyOzOy/fBn43Tts1o7ZrPn1ooi56eP51omHVGcZXqYWDtQ2zL50hXqn4fuC30LMeGloL9Uoq2ceUnJipjMjQaCY2m8PxjRfoMboZIxd00/hUGpi+9Y5CKbJg+XFGTtrK+8BwihbOSvNGpXBxsefE2UcsWX6CZzdUCx9RhJIF1W+TIFU/+cihp88YsvMAIpDR0Yl/mrQmp4ub7gMbuPVOajvhDSY/5a01ieW61gHPgCm6jCWKYjiwKfGvtXScmoSEWgT4BHNkheqkUskq+Wn6QzWtxjGlZn6PZf+dZsKMXWTL6MqaOT1oVK2IWnFS6x3wj4zkx807+fPseRrnLcihTj9SVNeTbCBVP7FAJK2TkFDx8vozSlcryO/bhuCeQfP3ruZYJez80UfsOXib7i0rsmJKZzKnV+/7ssT2AqAffVxx9Rq9tu8knb0D+zt0p1uxUroNKFU/MRskvZOQ0I6rR+7Qr+IElkzYBkD6LG4a6aS5VQcDyCTYUSx/VpZM7siPrSul2OocLFMb9aGLEw4e44a/L3/Wa8JvdRrpXhFMz6ThyifJImmdhIRx+HvMRvb/c5KOAxvQvGcNjeM11UZD6mJ0TDy2YQr6darOypndaFKjqFpxllQRTB9VpK0jZVjLZfxYsizb23Ylhy55RgurBGZumFrrJONJKkSeoGBe339YN3MnDTtWpm2fOlqPZczWO8k97G/f82LExK2MnLiVnwauov+I9cyZf5B7D70T4xTUrVSQf6Z3ZXK/xjg52Kp1P13NJ+aSVNOXAcU6UkZRh4xUyZaLI51/pHvx0ug8sgFb70DqTLBZSOudL3EGCgFFgVhBEMSkD2Bq4jX/Jn5tkRrjBSR+1rzviYSEDiiDQ2jarRpDf+vMDyOaaDWGubbeuXT1Bb0HreGFVwCT+jVWO6kGpm29o49NRF11UimK/O/adTpv2opPRBj+UXoQcJDMJ5aHpHUSEsDxDReY2mkhOQtkYsGe4WTNk0HjMcwtuRafoGDh8uNMm7uXfDnSs25uD6qUzqNWbFpuvXP+9RtarlnPNT8f5tRuRO2ceXQe09KSbZAqNVHSOwkJLQl9H07wMy+UwSEM+6MLy4+No2LdYhqNYU76GBwSRb+h6zhx+Sn9O9dk/ph2uLmk/MxLi+aT4OgYem/ZxdJLV+hStBS72nYls5MOZcoM1Goglbcb0ARJ6yQkjMSyketY+es2zu69qfUYmppPNG29owlbNlzhwXM/pg5sxoiedbGySjm9rq35xBJb7wD8duYcA/bsI6+bOwc79qB+7nzaD2ZBrXfMEJNqnWQ8ScVs/G0vk9rOY8/KMzqNo435BPR7kvu1VxBv3gbh+Trgq7Y6V08/Y92WSzSpWYz1v/1I2aI51LqXLqe5IfVVP7n0xovmq9dx09+XWbUasr5lR7I7u+o+cCown0jVT75LHLDyGx+3E6+5kPh3dZr9VUn87KnfaUpIpMyfPy3j2PpzdB/ZlMFzOiKTafd8NUb1E036kgIEBEYwYvQmhk/Ywrsg1fyyZVLPeS5VP4F7/v78sHE7EYFxWMtkTKtRT3eNNOAJb2Mk2ixsc1FXJK2TkEjk+tF7jG06F0cXe+ZuGoiNrebVNcE4yTVNdPL0haf8MnQt3u/CUmy38ymm0khzMGcGRkXz85ZdDNizj7NvXwPgaquj/ligLqYyTZT0TkJCD/w7dj2hgRHMWNePATPaY2uvfrtUQx9k0EQbY2LimTNnP/OWHKVcsZz8MbK1WnHaaqMpjZm66qJSFFl08RJ9du4mPD6OsLhY7SeThIUZMi1ICyWtk5AwEqIosm3+AcJf+WJjZ02N5mW0GseQa0dN1o2hYdGMGb+Vbbuv06lJOZZM7Eg6N8cU44y9XjS1+eT48xe0WbeRt+FhLGvUkowOKf+MvotkPtEGk2qd4TLCEmbBjWP3AHDLl52pK39h1dx9PLrxSuNxkh7umvRWA817j4Lm/bnlciUr11/g8rWXTBjTnOVTOvO/bRdYs+eqWvHa9CFNQtf+aUlj6BIP+uvZ7RcRQe8tu+hSuiQT6tZmWIWqjD1zVLfJwUdx0HOvUjB8D+8kjNq7FJX5JIuTdqYvYyKKYgzQJ7nXBEGYBpQF1oqi+N8nX68giuKNZK7vDnQG4oFtBpmwhMR3UMgVzO//H8H+oXQZ0wq39C7MHbAapVLzZ6um/UhB856koH5fUlC1sXv42BdboHK9QswZ3oqVOy+xft81FGp8j9rqpa5aqS+d1FUjkyhqn5H2hYvTrlAxJpw9zsGXT3Ub0AC9vMGwPUyTMLY2mgpJ6yQkPufpDU9G1J1BtlJ5SYjXsm8z2vXuBs3XlurqpI9fKAOHrVMddHCzokXtEtx85IVfQHiKsbpopC76CLpppK76KKLaWLRGRvZsruzr0J1/79xg+e2rKL88MaIuBlo7GloXU4MmSnonIaEf3jzyYUj1KfSa3pH2Q5pQulpBZvVbhffL92rFG3otqem+6/4jd3nyzA+lm+r5bGUlQ6lUfnUw8EtMpY2m1MUznq844/kKubMSJxsbuhUrzap7N1HoookWpIeWoIWS1klImIZWP9Wkz+Q2/DdrDzv/d1rjeG3WjoZYNyqUIsv+O83T5/7071MHR3tbgsOiU4xLMp9oqova6pqua0Vd9dArNIxO67dQKksWAmJUPx8XW1si4uO1G1DPemiMPVNTYmqtkyqepBEcxQQ8Mrnwx46h/PJrG+zUbEnzJeZQ/eRbPHrqxy+D1rL7wC2evwlIOeATzKH6iT7QV/WTLXfv03z1OuZcPgtAHjd3sjnroQqHVP1EIyy09Y467BAE4YUgCFsEQZgnCMJSQRCuAusBBdBPFMXXpp2iRFpm9bQdLB+9HnmCXCvTSRLm2noH4OHVN5y68pR+nWqwYmoXcmRxVyvOVFppLq13AB6/D6DVmg28DA1mWaOWLG7QHDc7e90GNWDrHanNgMmQtE4iVePzwp/ru1QHY1r8WIP+09tptcY0t9Y7SXmhdAprBnerxbq5PWlUvYhasaZsT6cL+tLHsMBYzni9YnTlGmxp3ZkcLnqoDKZnJE00CJLeSUgkQ0JcAv+M38SEVn8Q/D6c0CDNHtbGqqKpLs893/Pyti8AA7uoWu94qJHQs9SqYLpiHSmjRfYiTKpWh+1tupLTRb1qo8kitd4xByStk5DQkZ1/7OHs/lv0mdyGSf/rjVs6zbtTabp2NKQunjj7mG69/8H7XSgANcrlVytO24pg2mLKamDxCgU3fHywjpTRqmARTnfrQy1dWrRakBZaKHrTOsl4kkbwe/WeQVUnc3jVadr9Upe/T4ynXG31NtC+RJvNQTCO+SQ2LoFFK05w/cxzAHq2qsSQbrWxs1HvoaRrQs3USTXQ38ahd1g4kUHxWEfKmF2rIcc696JbsVK6T9BArQXAeGIhtd7RmRXAK6A6MAiV+zIDsAaoIIriGpPNTEIikb0rjvN7978AyFkgM4PndMTRRTtzgTm23omMimP2nP3M+GM/ubOlY92cnrSoXUKtWF3bCphyExH0s5H4NiyMrhu2sejCJZrlK8TyRi11n5iB9dGQpNLNRV2RtE4iTaAMDiG9uz2te9dmxfFxlKyi3obbl5hb653IyDj6Dl3Ly7eBTB/UnKkDm+KkhrHGUlvvgO76GBkfz6g9hxl98DBF02fkcKcfaVuomG6TklrvWAKS3klIfIdbJx8wvslsIkOjsbG1omSVAmrHGuMgg6b7rrZhCvxfB1OuWE7Wzu1J+WI5U4xJi613AHY+eMiw/Qcp4JGOQ516mq0mGopUpoWS1klI6IhSoeS3bov5b/IWKtcvzt8nJ1CisuHXjtroorraGJ+gwDZMQa0KBfhzdBt+G9kaV6eU945NoYm6oI991CdvAgiMjmJdiw6MqVwDK0HLMQ1kxpQA9Kh1kvEkDREdHsOS4WsZ1XAW8XEJtPuljk7jaZtIM/Qm4cc4BZkcHenWogJr5/agRMGsasVJ1U++ZuLB49x+58uc2o3Y2LIjOXRx6ichVT/RCEs0n4iiOE0UReHTkl2JX/9dFMWGoijmFEXRQRRFe1EU84ui2EsUxbummq+ExJcolSLK4BCKFc9Ck27V+N/JCVSqX1yrscy1+snJs4/pNWAVd5/5EJ+gWcnCtF79RK5UsvTyFdpv2PShQpijtQ0uttpVlfuAhZpPINVtLqqFpHUSErB66nZGNZqNKIr8sX2oTtVPNMHQOun/PpwRozexasMFGlYrwsqZP2Btpd4WiqnMJ+awqbjn0WNarF7P46AAymfJpvN4hjJmStVPNEPSOwkJ3VAGh9B5cCPmbhlE4y5VNIo19EEGTfdd9x66Q/8R64mKjuOviR3p27E6VrKU9cMSjZm66uLBJ09pvno9jwLfs7B+MwaWq6zbhCxQDy0JSeskJAyLUimyfeEhBtWYgtdzf957B2s9VodOFbDXYM1pyHXjlZNPWfrvKaqVycvauT0oVTh7ijG6mDK1QR+GTF3wDA6hw7rNbL17n0HlqrC5dWeyOOnQG08yn2iNMbROMp6kQR5cesbAShP5/celAGTM5kG9dhW0Gsucq58A/PW/k4yctBVbG2v+ntqFwd1qSdVPtMAnPJxeW3Yx6ehxSmXKwtHOP1Ius/luIkLqrX4iISFhfA6vPsPwOtOJCI1m+pq+jF3SU6uSkGCe1U8CgyIZN2EbZw4/BKBlnRI0qqZ+WwFTbCDqIx70k2B79D6Ap68DsY6UMaFqbY52/omaOXLrNqgBk2xS9RMJCQlD8ODiU/pXnMDu5Udp8WNNCpZK+QR0cphb6x2FUmTt5ksMGbOJjQeuI1eoNE/dBJuxT7MlxeuCPtaQPuHhdN+4nTlHVcbM4hkyUS17Lt0mZoG6KOmhhITEp2yds4NbZ58w/M+udBzYQKNYc9NHz9cB9BuyjkPH79OteXmyZ3ZXKy4tmk/8IiLosWkHf5w9z95njwHQaUQLa70jISEh8SVvHvkwpsFM/O97AjD8z66Uq1VY7fhWP9Wk98RWrD4wgqp1i6odZ0hd3L7nBgNHbUAuV7B8Sic6Ni6rVlxaar0TK5cz6dhxRh44RPEMmXTPL0paaLZIxpM0SkK8nLCACJTBIbT8sQZjFvdg9sYBZMruodV45lz95OadN/QasIoDR+7RuUk5cmdPp3asOVQ/MZfEGsDWe/dptmotu5894mHgOwDty2J9SiqofiIhIZG6eXbrFYOrTmLdzJ3UaFaa1j/X1nosY2qmptiGKWhYrQjTBzdn1tAWuDqr117IlK13zCHBlsSu2w+Jik9gfcuOzKnVECcbG90GlKqfSEhIWBBx0fH8PWYjP5cey4OrLwGo3LCEWVY/0XRd+fCJL0f33v1QSnnVrO7ky5Ferdi0Wv1EKYrEKxRYR8oYXqEam1p1YmLV2tjKrP7P3j2H15V1cRz/nqi2bdvm1LZtTm3btlIztZHatq3UQRvn3nveP9J0+s60zUVykazP8/TppM3eOR3kN+fsddYyflMb7H4iBZlCiBABvoGMqjuN4zuu0G5ITTqMqINi4DM1axq94x8QxLS5B2nd1ZlX7zwAyJouSajrIuPoHZ2qsvTSZT68D76IpVXr0L9wSRzsTDiasbE8FEKIX4llryFb/rRMWN+N7hMaEjX6n+8d02ZNTtNelfH56s8nNw9GzGpK3+H6j78Oz/vGR08+0LHHag6ffcCLt1/0/hqRbfTO7vsPKL/Umf3PHgGQP0ky4/NQRu9YJSk8EawYuJZ5vVeTNX9a5h8cQKHyxs2ctObuJ35+gcxccJjmnZbz6MVHAMoVzmQT3U9C9jBVWB2svfvmzej9x9B6qUR3cORAo9a0z10AO1MLUGy8+4ktPVBUdP/8exnaDyHEPzRBWtZP3kX34iPYPO8IABlypiRhUsPHj5krM40p2Bw4ZAtLVp2kdMGMrJ/SmuJ50+m1TrqfwO33H6i9ah1LL12mSfbcHGrUhtyJkpq2qY13P7EkyTshLOPd84/o3D1IEN2O4UvasfDwQKPmd5srJw2legaSMF4MVk5oQZNqBdDnNsiSBZqmCKt7yD4797Pu+g065S3ErvrNyRw/oWkb2uBhW3hlomSdELZFq9EypeU8di48TKVGRUiUPK7Be1jb6J13771w8tJSMn8GVk5oQf82f+n1vDUydj8BiO7niOdXf3oUKMrWOk1JGyeu8ZtFkgM3yTohIi6vT9/oXmwYW+fsp1qL4szd1x9Hp98XqnccUYfY8WOwbuYBRrddypaFRylZLQ9rDvQjR379uw+H132jr18gkyfv5ebZ5wA0rVaAEvnSh7ouso3e+eLri4O3HamU2Gys1ZhNtRqTLEYs4zeMIFkYUfJOCk8Eqqqyd9lRuhUdzsc3Hoxd3ZkytfRrBfUr1tz95P2H4JuhjLHiMLF3LVZPaknOTMn0WivdT/4rZoATr756MaJEOVzqNCFDXP27yfyWdD8RQli5l/ff4ucWfKjWd0YzlhwbavCc7hDWWLCp06lscLlI595r8Pzmx7T+dUmT3LBuYcaIKN1PArVapp48TeMNm/AM8MfD3/B/Xr9ko91PbKkwUwgRtj69+cKg6pNRFIVpW/+m8+h6Bnc/MddoAUNy8uLV57TtupILN1/Qq0VZ5g5tSOL4oT8ks1SBpjXcQ/prNIx2PUaHbTtIGD0Ge+q3IF8S/e7Df8sGRw1IHgohIPg57KIB6+haeCgf3wZ3CnFwNKwblLWN3gG4dvoJm7Zfon6lvCwf10yve8jI2P0kUKtlyKHD9Ni1h7Rx4rKvYSsaZs1p/AWBzeWhEEL8LNA/iGVDNzGg8kR2OZ8kKFD7y89r2b8aeUtm4vLRe+xeeQqPT9/YtuQYcwdt4st7T6Y5t6dF1/J6f93wzsXoPiqVSmRj+oC69GpRFkeH0LPelrqfhMUz1HffvBl44BBZEyRif6NWlEsdepHOb0kWWg0pPBE/vHv+kd5lRrFhyi4uH7tn0l7W3P0E4NUbd/oO24yTowOLRzWhR7PS0v3ECO5+fnRx2UW/fQfIEDc++xu2onPeQtL9RA7ZhIg0xjScyaObr+g9rSn9ZjUnSlTDR6sY+9AwvA/Wnj7/RJeeaxg2fgcv3dwB9DpYA+l+AnDd7R11Vq3n3bvgf7ajS5anYNIUpm0q3U+EEDbmztmHdC44mJ0LD1OnfRnm7OmLvYPhjyHMlZP68vTyZcSo7UyZc4DsGZKSL1tKvddaokDTGkbvAJx49pzqK9fgfOsqtz6+Bwibe8cwJqN3hBDm8OmtOzp3Dxr3qMjEjd2IEdvw7w3WNJpOo9GxaMUJBo50IUHcGKwc34LKJbLptdYWCzNNzcWDjx5Tc9Vabn18T79CJYjpaPhowv9jY3kohBD/dufcI/bM2YvO3eM/v5cifWIKlg3OFI1GS5a8aYgROxrenr6c3H2d+UNd2L/uHJ+efjAoG8NzJJ1Go6Nnn3Vs232VJtUKsHRMU1ImjRvqOnNnoqXzcN+Dh9RevY533t9YWb0eg4qUMn6zSNIJzNpJ4Yn4P4H+Qaweuw3vV+9xjOLAmFWdjGqLHMKau59cvfGSdl2d2XfoFs1rFGLhyMZ6tUkG6X7yb7vu3aeK82qOv3pGudTpUVU1TPaV7idCCGv3/sUnBleZwJpx2yhfryAzdvYmToKYRu1ljQWbQRot5y4+wclLS/YMSdk2uz3dm5bGSc838iJ795MQSXQxKZ8mPVvqNGFYsbJEsTcx32y4+4kQIvIJ8A1k0YB19K88gR3LT6DVBGfDn1oo/4o1jt7Zf/g2zTss49DZ+wAUypmG2DGihrrOVrufhEU2uvv6MePoWZRvCvGiRuNI4zbUzWzcuN8fbLQoUwghANzuviRrvrRM2dKDeIkMbzNvbfl48epz2ndfxd0nbgQGafReZ6u5aIp337xpvXEbTTZsxjsoEAc7O9M6gtlgNzAhhPiVfxeffHbzYP7QLcwdtJlcRTMyZnUn/qpfiOixgu+9nt17y4oJuzi8+QJgXGGmvgw5iwwM0jJ3yVGGjttOsoSxWT6mGdH16AJqi6N3TMnEl56eNFizkfXXb6AjDM4WpfjEoqTwRPxWXEeV5OkSMXlTdxp00b9F1b+Zu/uJITdDvn6BzJh/mL7DNrP54FVC6iWiOEn3E0N99vGlx7a9dHLZiQokih6DrvkK42Bn4rcZ6X4ihLByqqqyfvIuRjaYydtnn/jm6Wv0Xtba/QTg7b2P7Dt8mxY1C+E8rjmZ0iTSa510PwmeXVrDeS0bb96iY96C7G/YkryJk5q2qY12P5FcFCLyun3mIQcWHkDn7kHxKrlZfHQIOQob1krXHDlpaEa6e/jg5KUlbqA9E3rVYN2UVhTKqd98cUsdsln6oC1EzABHvvj7MeuvaiyqXIt4UU3Ih3DMRSGECE8nt11kZIOZJE+biBk7epMsTQKD97C20Ttf3L3pN2gzZ488AKB6mRzkzhJ698fIOHpHp6q88fqKg7cdrXPmY1vdZgwqUgpHU56nSh4KISKAn4tPAvyDeHzrNYc2XaBtibE8uvGKNoNqUKj8PwXs/r6B/7femnLx7IUntO+ximnOrvj6BV+nvX3o3+dtafQOmHafGKjVMsr1GLOOngOgULIUVExrfFMEKT6xHCk8Eb/14dVnehYfzrmDt2g/rDYjV3Qwqu1jCHN1PwHD31S7euMlJ/bfxclLS/UyOdg4rY1BDwul+8k/fIOCcPC2o1bGrAwqWprd9VuQI2Fi0zeW7idCCCt3+dAtJjSZhU6rI16iWNTtWBbFyPbx1tj9xM8/iJkLDjNw1FZix4rGinHNaV6joN7rLdn9xFRhUaTpGxTEqCNHab1lK1EdHFlUuTZOdoa97f9L0v1ECGGDPF+8Q1EUprr0pNWAatjZGfY91tre7obglxr6DNmMt28gc4c2oHercnqNc7XEIVvIelOE1UzvFutdmHLyFOXTpOdw4zaUT2PCXG+QwzYhhE26dvQOg6pNIkbsaEx1+ZsoerwN/SvWVpwJEM1bpVn1giwc0YhODYuH20EbWD4XTbXl8m223LpN1/xF2Fa3GenjxjN+Mxk3IISIAHTuHv9XgGJnp+D7zZ/Fo7bj7eVL17H1SZgs7m/XG1qYGZ6jdz59/sbpw/dx8tJSumBG1k9prddLfZYYvWNqQaapHLzt6JSnEMuq1mVkiXLGPz8N4yyULmD6kcIT8Ue+3/wZ33gWC/uvpVC57PSe1tSk/ay5+0kIt8efg1tgDW3AiC5ViBNLv4MR6X7y/1afu06XHbtIEC06u+q3YEixMkRzcDRt03DufhLe5C1vISIHnbsHFRoWodPIukxY35XkafXrDPJv1tr95OKVZ7Tr6syJy48BwzLDkqMFrKVI8+zLV1R3XkOX7bsI1GlxsLMzvUBTup8IIWzMnXOP6FxwMIfWnKLp35UZt7YrcRMaNl7AXMUnhmTk46cf6NRzNVt3XaFxlfysnNAi3LtpWjofTc1Gnaqy7NIV6q3dwGdfH5pmz23aBYGMGhBC2KSHV57R969xOE/cTYBfYOgLfsPaijO1Wh3deq/j8NG7tK1bjKWjm5A6WegFFZbqfmIKUzPRL0jD8MOudN25m1Sx47C3QSuqpc9s2kVJHgohbJCDoz2psyb/8XFI8YlOFzyywO3FJ+5cekasuNFJlTFJqPtZy+idEH4ffIke1ZHlY5vRpGp+Qntv0dyjd0xdGxbnjL127GPllau0y12A7fWakSFufOM3k0JMs5LCE6GXXYuO0PevcayfdQCAOPFjkCSV8f+hW3P3k7v33ejQbSVrN5+nUvGsbJrelrKFMum1Niy6n1j6wSGEXQGK65OnVF2xmm2379I5byH6Fiph+sWBTY/eAXnLW4jIYPN4F2b3cCZzntQsOjKIJj0r4uBoXHW2NXY/+frNn/Hjd+OyPnh2aplCGenUsAROev4ZI3v3E+/AQO5++PijnfLu+i0YXLQ0UexNzDcbLc6UXBQicgrwDWR2d2dmdl1OjsLpyFvS8IMVcxZp6iswUMO8pcfoN2wzR84/ICBQE3ytekSHreZjWGTjw0+fqb9mI4N2HwIgVaw4FEoW+liGP5LuJ0IIG/P6oRtHnV0BKFktD1O29CBFOsNfZLC20XR+foFMnn2AUZN2kSJxXFZOaEE8Pbtq2+Kb3qZm4pHHT6juvIbzb1/x8qunSXsBkodCCJtTolZBBi7vTOb86X78Wkjxib1D8LG277fgzNL3eas1jd65eec17buv4sLNF/RqWY5Zg+qTMG6MUNdFpoLMQK2WCcdP0nn7TpLHjMXeBi3JGj+h8RcjxSdmI4UnQm8Prz7j2bl76Nw9aD2wBouPDqFxD9MO0qy1+0lgkJbla07TvucqXrq5E6TRGvT1ZPzOP74GBDDs8BGabdzCwusXAUgdOw7xTZnfDeHe/cQch2xy0CZExHZg5Qk65BvIhSN3aD2wBs16VzZ6L2vtfhK8RkveNMloW7coaya1Io8es7tBup+E2HblLltu36FLvsIcaNSKgknD4JDNRrufCCEip0NrTtEu1wBO7LwKQMZcKa129I4hGXnlxkvWrzqLk5eWnJmSsXpiS7KkC73DlS3no6nZGKjV8jUgAAdvO/oWLsHm2k2+F2aaMJpOup8IIWyQzt0Dxc+PdNlSsPDwIBp2q/DjoM0Q1tb95MSZh7Tp6syMlUfx+P51okcNvTtyZOx+8tHHh84uu3j44jMAA4uUMm0cnYzeEULYEH+fAJKkTkS7cY3IUzrbj1/XuXug1eiIETsahcrnMHhfc4ze0TcXvb76MWLUdqbPO0SeLCkomDO1XuvM3f3E0qN3jj59RvWVa1l68woP3YMz0egdw2H0jvgvKTwRRlk/ZgtXjt+nzaAaLDg0kNzFMhq9lzV3P3nx6gt/913P5ROPAWhVqzDt6hXD0SF83+iGiDd+59KbN3z7HPwQcXr5qrg2aUu9zNlNv8BwKj4B843fEUJEXO7vvZjQZBbD601n+7ITACRLm5CYcYz7b98au58ALFpxgn7Dt+DoYM/iUU0Y2K4CMfScS27Jt7ut4Q1v78BARhx2peVmFxzt7NlSpwkd8xQ07cLAJrufSCYKEXl9dgue3Z0gmsL07b2YuLE78RLHNmgPa+x+EiJGoB1xY0Vj+djmdGxQHAf70B/FRObuJwCj9h9l081bdMlXmF31W5A9gXGjC3+Qt72FEDbm5LaLdCowiEvH7tFuSE1m7+5LhhyGF6kbm4+GMCQb3T18cN17GycvLfmypWTH3I5UKpFVr7WRsfsJQKwAJ8qmTodztXpMKF3BtFHmUowphLABFw/eYNUYFzLmSUunyc2o0roM8ZPGASBN8ph0G1mLhMnicOvcEy4fu2fw/taUi3sO3qRFx2UcPHMfgHxZUxItin5FmYayVEGmqXn4yceHecfPY+9tR7IYsTjYuA0lUuhXqPNL4XimKKTwRBjp01t3xjWcwfB603F0cmDKlp5Ub1XS6P1M6X5ijre5g9dpSZ0sHh0bFGft5Fbky5ZSr3XS/eS/Rh04ynMvD2b+VY01NRqQKlYc0zaMAN1PhBAR2+VDt/j67C06dw8GzmnJ0uPDKFM7v1F7WWv3kyvXX9C2izObt1+mVvlcFMmdVu+1lnq7OyzWQ9hk5PlXr6nuvIY1165z6d0bwIQK/hA22v1ECBF5fXrrzrxeq8iSNzULDg4kX6ksBu9hjd1Prt96RZuuzrgev0e7esVYMa45GVOHXkgRmbuf+AYFMfLIUdpv3UH8qNHYWb8FZVKlNe2ipPhECGFj3N97Ma7hDMY2nUv8xLFJmTGJ0XtZ0+idEO4vPHn+1p0x3aszoVcN4sQK/fmYpXLRFKZmol+QhgZrNrL00mWaZs/D/oatyJ8kmfEbSvcTIYQN2LfiOCPqzSB6rKh0n9mS+WfGMv/sWGYfH0nZhkX58s6TOYM2Gb2/Obqf6OvTF2+cvLTE1zowbUBdVk1sQbb0oWe+LRZkmiqOJgr2isL6Wo0YWqwMTnZGdseU4pNwI4UnwiSXD92ic4HBrJu4g/OHbgEQO14Mg1sjhzD3+B1DTZ26nwEjXXBwsGPhiMYM61SZ2DGj6rVWup/84/GXLzRZu5lRR46SL0kyDjduY/poAQj3ApTwZM7RO4r2n3+nW5/5wQABAABJREFUQvshhAh7c3us4ONbdwbPb824tV1ImjqBUftYY176BwSxcMVx2nR15tjFRwCUyJeeBHrMKQXpfuIbFMT4Yye4++wjAONKV2BK2crEdjIxg2w0G00leSeEbTqy7gw9S43Cy92b8eu60LJ/NYP3sMbuJ97eAUyatZ8hY7eTIG4MiuZJq/fayNz95OTz51RzXsP6uze4/O4tYGJhZgQbvSNZJ0TkcHb3FdrlHsDxlUcB+Kt+IXIUMnzsirV1P3n33ove/TeweOUJShXIyPoprSmeN12o6yw1eseSb3sHarVMPXmaFptccLSzY2X1+sRwNKHzCdhMHkrWCRF53b/0hLa5BrBhym7ePHqHk5MD3zx82LXoCJPbLMTtxhN07h4AlKyWh+JVchv8NcIzFw0tyvT2DmDI6G04OTqwdHRTWtcugn0oZ63mHr1j6lpT8/DJF3fqrFrP+hs36ZS3ENvrNSND3PjGbRaO54nGiCh5J4UnwmQBfoGsnbCDz/dfAjBsSVtm7upDxlz6dQT5FWvufnLp6nPadnFm3ZYLVCyehSQJYum9Niy6n1j6YA3CpjJRBdbfuEmVFavZ/OA2tz+9ByCqQxh8o5fuJ0IIK/b05kt6lxrJgn5ryV4wHYuPDiZ7wdAfrv2KtXY/efXGHScvLbH97RjRpQobp7WhZtlceq2V7ich+9jx7VsA9bPk4EiTtlRKa/xYQyBcu58IIURYe/XAjb9LjODw2tNEja7f6LZfscbuJ+cuPqF15xVs3HcFgEI505A5TeJQ10Xm7iee/v5MOHSSQE8tUewd2Fm/Bc2z5zHtoiQThRA2xs/bP/gvPD2p37k8U7b0oG7HskbtZU3dT3Q6lY1bL9Gp1xrcvXxIlTSe3l8nMnY/ufTmDTVWrqXztl34BAX/PU4d24RO0tL9RAhhAzZO3c34lvPpWXo0f5cexeKB67lz7tGP39e5e1CtRQlGLGtPv1nNiRHbsDMWc+Sivm7eeU377is5dukxXRqXZOGIxjg6hN7Vw5ZG74Bpeeiv0TDqyFG67NhF8pix6JS3kGkXY0XFJxGBFJ6IMKVz92D/UlcSJY/H7D396Dq2PtFj6dcR5N/M3f3EkG/+AQEalq0+RaM2S3j88hMATaoV0PvmKCKM3wmr7icfvL0Zf/AEWi+V6A6OHG7chuHFyxLdlHmlIN1PhBBWTadT2b34CB3zDeLA+vM8vvUKwOjDNWvsfgLBHVC69V3H45efGNqpEvOHNSRl0rh6rY3s3U8AZpw+S/21G/ji58vSqnWYX7EGCaJFN21TGb0jhLARAX6BzOq2gqV9VgGQJW8aCpTNavA+1tj95Ju3P/YeGpy8tPRoVpoV45vTqWGJcHugCBGj+wlAnMAoeAX4M6FMRVZVr0/i6Pp1VfulCNb9RAgROeh0Kn3Kjub84Tt0GlmXgfNaESWq4c/QrK37ybMXn+j691p2brkMQMn86cmTJfTuyJGx+4l3YCBX3r7FwduOhllycqRJW7rkLYydYkLOSvGJEMLKff3iTYBfIO7vvX75+8NqTGbdxB2Uq1OAhYcHkbdEZoP2D+9nq4acQXp7BzBhwm7GTNnNtfuvCdJo9Vpn7u4nlh694/rkKTVWrmX8ueNAcCFmvKhGnqtZWfcTW2YThSeKolRRFOWhoihPFEUZ/IvfL60oyjVFUTSKojT4xe/HVhTljaIo881zxZHb8S3n6ZB3IPuWHaVG65Isdh1M2qzGz500V/cTMPxAzdPLFycvLUmUKLSrW5S1k1vRtm5RvR4Ygozf+TdHHzvOPHtJhzwFOdy4DWVTG9cB4P9I9xNhIyTrIqfPbh4s+nsFAe8/Ey1GFBYfHUK38Q2MKtq01u4nb9w86DtwI1PnHCRz2sSsndSK+HH0K54w9e1uSx6wQdh0P7n78SP1Vm9g5umzlEiZhmimFmaCdD8RFiV5Jwylqio6dw+a/l2J8Wu70mZQDezsDX+UYY3dTwD6DNrIkeN3aVu3KCsntCB7hqShronM3U/c/fxov2k7o12PUiRZSg43bkONDFlMuyjJRBHGJOtEePPz9mdcwxk4j9pCmVr5mLGjN45RjPteZk3dT4I0WlQVnLy0tK1blIUjGtO9aWmcHMO3MNNYls5EgBP3nnH0xTMGFyvN5tqNTe9+IoSeJOuEtdFqtKydsIM+5ccR4BfIpE3dSZs1uUF7WFtR5rFTD1i57BQAOTImY2q/OnqNM7el7iemni9+9PHBz12Dg7cdcyvU4GCj1pRIkdr4C5IsNJnVF54oimIPLACqAtmBpoqiZP/Xp70C2gAbfrPNOOBUeF2j+C8fL18W9FtL77JjeX7fjfevvpi0nyndT8zxsNDD05dWnVdw6uoTOjUswZpJLcmbNfSqfIgY3U8gbG6WfIKCGHnkKE02bMZfq2FV9frMqVDd9PE70v0k3CmKMkVRlKOKorxWFMVPURR3RVGuK4oySlGUBL9ZU1xRlP3fP9dPUZRbiqL0/v59P1KRrBMAWncPzu26TLUWJVh6fCglqhrXQt4au5+oKuw7fItWnVcwY9Ux3L18AUgUL6Ze6y2Vk9bS/USj07HwwkXKLl7Om2/Bb3b0LFCUZDH0H/f3S9L9xCCSdaaTvBOmGN9kFvtWHKNxj4pM3dKThMniGryHOe8p9eXtHcDkWQcYONKFmNGjsHRMUzKmTqTXWlstzjQ1G1Vg3fWb1Fq9jhdennTIU9C0t7xBup98J1lnOsk6YU6bp+9lRL0ZnNp7naAAjdH7WNtBG0DfgZvYc/AmLWoWwnlcczKlCT0bLdX9xBSmZqKHnz9/b99L3737yRI/IQcataZ2pmzGX1AkeeNb8s40knXCmj28+ozuRYcxpf1iXjxwAyB2PMO6JFpTUWbw52tJEys2hXOlZt2U1pQtlCnUNeYuyLR09xOAEQdd+RYYwPpajRhSrAxOdkZ+e44gOWiprLP6whOgMPBEVdVnqqoGApuA2j9/gqqqL1RVvQX8578kRVEKAEmAw+a4WPH/Hl59xohaU/B98wGnqI6Mdu5ItgJpjd7PmsfvuHv4MH78bgaOdMHJ0YFp/esSI5r+IxMs+VZ3yB6mCqvuJ1fevqXWynXMOXuOuFGi4q8x/ub5/0j3k/DUB4gBHAHmAOsBDTAauKUoSqqfP1lRlNoE31yUBnYA8wEnYBbB3+cjG8k6gb9PAIsHrqdXmTF4fPzK8KXtGDi3JU5mbJsc3sWa7h4+HN59EycvLdkzJGX7nA70bF6GqHq8nSfdT4ILNB287cjkmICu+YpwuEkbmmXPjUk7S/cTQ0jWmU7yThgt0D+IuX+vYlKbhaTLnpwFhwaSIn1ig/cx1z2loRl58epz2nZZwdzFrjx5FTzONW6s0O8BInP3k+ceHjReu4nOW3ehU1XiRolKqZRpTLsoyUTJOtNJ1gmzunLkNpvGuQCQo1B6Gnb9y+i9zFF8om82+vkHMXPBYQaOdCFOrGisGNec1Mn0H3FuDEsdtoHpmbj7/gOqr1zD1fduePob/hz8PyLIodsfSN6ZRrJOWLUAv0CObTqHzt2DNFmSsfrCKFr2q4q9g/5H4ua6Z9SX64l7dOi5GrePXkzqU4sRXaqEev5oawWZpp4v3v/4iTqr1rP++g065y3E9nrNSBJDv5ce/yNiFGJaJOtsofAkBfD6p4/ffP+1UCmKYgfMAPqHw3UJAyWMYUeaLMmYtq0XHYbXJnpM4x++WPP4nYtXn9OmywoGjnTBxy8QgDIFM6LvC1iWHL9jTd1PArVa5p27QIfNOwBIEiMmS6vUJkPc+KZtbMPdT6xcbFVVi6qq2k5V1cGqqvZUVbUQMBFIDgwJ+URFUWIDywAtUFZV1faqqg4A8gLngQaKojQx/x/BoiTrxA+Prz+nZ4kRrB67jRixo5n85pqhzJGVAO8efGLfoVs0q16Q9VPaUCyvfuPVbP2ALSwy8rmHB9VXruHWx/dMLFOJDbUakS6Ofg9hf0u6n+hDss50knfCZCdcLtCjxEiO77jCuxefjN7HGruf+PgGsnPfDZy8tKRMEpdtczrQu1U5outRhGqrxZmmZqNWVfni64uDtx1d8xdhbc2GTCxdkdhOJuRPOHU/sRGSdaaTrBMWoXP3oHTNfLQbWovhS9sRL5Fx3RHN9QKDvi5efU7brs7MWXuCV+88APR60c/WDtvA9Oep7755027Tds7efwVAhzwFpfvJ70nemUayTtiMD3efc2r7JZr1rsKsXX1Jn12/SQVgnhf7DMnEV2/c6dFnHas2nKVSiWxUL5NDr3W2VpBpSh76azSMcj1G5+078fD3w8PPxGJM285Bi2SdLRSemKIbsF9V1Td/+iRFUTopinJFUZQrgWqAmS4t8nnz+D1dCg3h4KoT1O1YlmUnhvFX/UIoRrbEtebuJwEBGu7ed8PJS0vxvOmY3Lc2y8c2J2emZHqtjwjjd8LqcE0FHLztyBYtEUWSp+Jgo9YMLlqa6A6GdwD4Pzbc/cQaqarq/5vf2vL955/7vzUAEgGbVFW98q89hn//sGuYX2TEpVfWgeSdLdFpdWyYsouRtaeiqiqJksdjyMI2JE39yy54f2St3U98fAOZufAIPQduIDBIw8yB9RjZtare6y15wGYNGfnay4vWG7cx9OBhciRMzOoa9a1yzADY1GHbH0nWWZzc24kf3j55z8KeK9B8dideolgsPDLIqBF11tr9BODbq68cPHKHhpXysWFaW0oVyBDqGlsvzjTVnGPnWHrpMo2z5eJIk7ZUThd6C+o/ioSjdyTrLE6yTphkQY/lLBm8gULlsrP0+FCqtyxh1HNXc43e0Tcbv37zZ8/Wq8GjBpLHZ+e8TrSrWxQH+9CPNmzxsC0sMtHJx56KaTMwp0J1lleta/wb32Drh26/JHlnUfIcU5iV71c/ZnRexpgmc0iQNA5z9/WjfudyBu0R3veMhmSiVqtj5fqzdOq1mq2HbgCQNV0Sokv3k/9z9Okz2m7aTqBOSywnJ2b9VY2UsWIbt5mNFmJaKutsofDkLfBzu5eU339NH8WAHoqivACmA60URZn8709SVXWpqqoFVVUt6KRY90MAW+fn7c/cv1fRq8wYPr71oFqLEibvac3dTwCunHzCuKl7SBg3BsvGNGNElyokiKvfTDkZv/OPMy9eUnH5Snbeu0+XfIU52rQd1TNkMW1T6X5iDjW//3zrp18r//3ng7/4/FOAL1BcUSLVN+RwzzqQvLNFqqqic/cgbarYFCqfnSVHh9Csd5UINX7n1t03tO+2kiWrTvLw+Yd/vraeDxEtccAWFushbA7Ztty+Q6UVq+jtug+dquJgZ0eBpMlN21S6nxhKsk5/cm8nwlx0NOi0OoYvbcfY1Z2NLtI0VHi/4e3nH8Scxa70GLCebz7+TO1Xh4m9a+pVZGiJh4oh601h6r1joFbL1JOnqbduA598fVhSpTYd8hQ07aIid/eTn0nW6U+yTljU9nkH6Vx4KI9vvabHxEaUqGZ4YWYIa+t+AuDn5sO5G8/p2LAEqya21OslP1s7bAPT7xV1qkrLDVsZf+w4JVKk5kjjNjTMmtP4DW300M0Iknf6keeYwuac23OVjvkGcu7gLWLpeT73M2vrfvL0+SccPDXE8IGp/euwfkpriuQOfeyoLRZkmsLB245c0ZNSMW1GDjYKHllutIiTg+GadbZQeHIZyKQoSjpFUZyAJsBufRaqqtpcVdXUqqqmJbh11xpVVQeH36UKfT269pzepUcxqt40VFUlbsJYdBvfgDgJjKu+NuVNNXO8reZ68j6tOi1n7ebzVCiWhdmD6xu0XsbvBHP39WPIwcM0XL+RL36+lEut31iGUIXjG942+lDRaIqi9FcUZbSiKLMURTkNjCM4wH6+gQipGHr07/WqqmqA54ADkD68r9eKSNaJP7p44AYd8g7k/OHbtOxXlcWugylUPrtRe1nj+B2NRscGl4vs2HwZgPJFMrN2UisKZE8Vyspgkb37yWcfX249/YCDtx3NsudhW91mzChflfhRoxm/qXQ/+S3JOpNI3okw9/qhGz2KDWfxwPXkKJyexUcHG/wGG5jvntLQ+8m7D9zo1GM1i1eewO2jFzpV1XutJfLRGrqf3P3wkfqrNzDx+Al2Pb4PQNwoUTFp14jzkFEvknUmkawTFuf29AODq0xgRL0ZnN1/E4Cs+dMSNXroI2r+zdq6n3h6+TJhwm4Gjd5KjGhOLBnVlN6t9Mt9WzxsMyUTdarKqqvXqbFqLfc+f2Ji6YpWOaLVkiTvjCZZJ2ySt6cv4xvPYtWQdQBkyZuGlBkSG7SHNXU/AQjSaBkxbgd+/kHMHtyAIR0rhTqSztYKMk3Nw0tv3lB95RpufHzHxDKVWFOjAcljGjeS0BYLMc2ddVZfePL9D9QDOATcB7aoqnpXUZSxiqLUAlAUpZCiKG+AhsASRVHuWu6Khb5UVeXrF2907h7kzJmUqs2Ks/zkMGq3K4O9g3H/alrz+B0//yCWrzlN6y7OTHN2BSCKo4NeVYgg43d+dt3tHfVWb2DcweMA5EiYmCHFyhDDUcbvhIOEIS0Nv//oFMrn9wdGAb2BkgRXSFZSVfXTT58T5/vPXr/ZI+TX4xp3ybZHsk7o47ObBxObzmZQ9ckEBWooVimX0XtZa/eT4DVa/D764uRkz/zhjRjTozqJ4oVemGrJLmHWcMgWYtuVuyw4f4GaGbNytGk7095qg3B709sKc9GQvJOsM5LknQgvOq2OHQsO0SHvIK6efECGHCmN3suc43f0pdXq2Lj1EksXBd//5MqUnGVjmpIpTaJQ11py/I4pTL131Koqzleu4fHRDwVYUqU2LnWakjGe4R1xfrDBh4z/IllnBpJ1wppcOnQT7Rd3nAJ8Gbu6E0uODaVoJePuDwzNx/DufnLh8jPadHFm+56rBAZq9F5nqcM2Sx64vfT0pOUGFxpt2MRzLw8AiqdIbXxBpnVnoTzHNAPJOmHrdLrgDtM9Jzdizt5+Bo9ttbbuJ/cfvqNTj1Ws23KB6mVysGFqG72mLthiQaax3n79SpuN2xhx2JUCSZMzsoThL6v8H8tnodXe2ymqAW/LRAZx7BOoRaNWt/RlREqpsiSn2+w25C+dlRcP37F45DZunnts0p5qPMOr1rSxjXs7ODCO4UUPtRsVpG/r8py78Yw5a0/w6p2H3msDYhlfNxZkwljPsFgfQhMzbL7/tC9YgCHlyvDe+xsTz59k95MHpm8aU/+bVkNEi2n8PMoH9UZfVVXVxF7REDNBKjVX5d56fe6Fjf2N+pqKoiQBihNcNRkLqKGq6rXvv/eI4PlxmVRVffKLtWe/ry2uqup5Q7+20I/knW1zcLTHMYojAU7RyJw3NQXLZMNl8VGCAgz/3mVMVoJxeWlIVjo5OdC0fmGaNyqKRqtjztoT7DlxW+/1xuakNWRkWORjxgTxGVu1AoWTpWT93RsMO+Vq+oWZIRvDKusg/PNOss76SdZFXo5ODmhjxiJVxiSkypiEcwdvhb7oF8yVkYbeSxYpkI7B/aoRO2Y0Nh+4yvJt5/DX4/8BLHUPaWo2hkUu1smRjWHlyxLD0YlF1y+y4OpFAnVa4zc0MRNfthpiE/d2knXWT7JO6Ct7kYz8vbAD6bIm59yhWywasY3P7zyN2sscz1sNzcbAOPbkz56K+hXzMHP1cb54+oS6xhL3jNaQibkyJGFHveZcePuaQScO8fKrp/Gb/SEPbSXrQPLOFkjeifCQMHk8hm/qTbYC6XBZdJRVU/ai0xpWjGFtmZg1U1LKVMjOvA0nAbBTFL26Zpo7Ey2ZhylixyYgmob3Pt4kiRETOxTe+Rj+8gnAy24D5Dnmv1h9xxMRebx+6MaQqhMZ3Wg2UaI6UqVZMZP3NNf4HTB8JinA3m3XmL/sGLkzp2DdlNb0aFaa6KG0wQoh43f+seLKVeqv28BHXx/mVqzBxlqNyGTKm2wg43dMpKrqB1VVdwCVgATAmp9+O6Q6Ms5/Fv7/r3uGz9UJYfs0QVr8vP3RuXtQuHwOWvavFjx+p5zh43fM3S1MX4GBGlZvPEfrriu4cf81fgGBBn0tW+9+YmpGPvniTvN1Wxhy8DBbHtwBIIajI1HsTcg2Gb/zfyTrhLBeQYEadO4eNOj6FyOWtefvKY3NNl4AwnfEAMDFq89p2Wk5B47cpnmNQmyY2oaiedKGus6S3U8s+aY3wM6796m8fBUHHj6iV8Hi7G/UirRx4hq/oe13P9GLZJ0QEce9i0/oXmQYy4dvIn/prCw5NoSESX/3n++fmWv8jmGfryVNrNiUyJeBjdPaULt8LpRQoiMydj8BuP30A0MOHiZ7wsQcbNSa9rkLYBfa36zfiSBZKHknROT02c2DARXGsXuJKw27/sWE9V2JHtOwZ1TW1v3kweP3LFl0DCcvLSmTxGXTjLaUzB/6JLDI1v3k8wdfHLztGF2yPIcat6GRqR2jbYC5sk4KT4TVOb/vGh3zDWJ+9+UApMmSjGa9q+AU1bgxKuaa1Q2GPzDUanW47LxCi47LOHzsLs1rFGJklyp6r5fxO/+4+e49DdZsZPjhI2RLkIhqGTKbvCcQruN3IgNVVV8C94AciqIk/P7LD7///J9/SIqiOADpAA3wzCwXKYSNWzN8A4NrTEGr0TF2TWdGLG9P4pTxDd7HWrPy3Xsvho7YxqmD9wBoXqMQo7tX07tlpCULUExlaj6qgMvtO9x99hEHbzuGFSvLocatKZVSvzF/vyWFmf9Hsk4I6zWn02I2TdtN5SZFmXdgAJnzpDZqH3NmpL68vQOYPu8QPQasxy8giAypEoa+6DtLHLSFrDeFqfeO7n5+9Nt3gHYu2/no68MHn9Dfhg9VBDlwC41knRARg1ajxWXWfjrlH8y6mQf4/D74jCFe4thG7WeOgzZDsnHf4Vu0676SRy8+MrhDJRYMb0TqZPFCXWdKLhrL0qNaXW7foarzas69fcWIEuVwrlbP+M0iUDGm5J0QkU9QoIYFfdcwreMSvrr74Odj2ItvIcL7hQVjxpnHCFAIDNQwrX9dRnatSuwYUf/4+ZYqyDRWWJwtTnE9xd3PH5hargqrqtcnaYwwGvdgxcI766TwRFiloIAgvnn4oHP3oEiFHLTsV5Wlx4ZQvEpuo/c09xvdhoSAh6cvU+ccpHPvNSzbeg6AeLGjkz1DUr3WW/JgLWQPU4VFSOhUlU03b1Nx+UqWn7kKQJlUaamVKatpFyfdT0yV/PvPIb2sj33/+VdVVqWB6MA5VVWNn0skRCRz/fhduhQczPLhm8hXMgtla+c3ah9zF2sayslLi2OgSrnCmdg8vS1NqubH3i707LDVN7wh7Ao0AfbfeYRWVVlbsyFzKlQnUbToxm8WgR4uhhHJOiGskCZIy8rRWxlYdRJOURyZsaM3eUsaV6Bu7+VN+TKGrw3vQ7bb997Ssfsqtm+8BECZQhmpXzGvXm8uW+IeMqyy0RSnXryg1YatBHlqiWLvwKZajamcLpPxG0aeTJSsEyKC+PDqM9um7EDn7kHqzElZdXYkXcfVJ3qsPx9I/Yq1dT954+ZB34GbmDLnABlSJ6JwrrR6r41s3U8+eHvT2WUX/fYdYMejez9+3T6Sdz9B8k6ISMl1w1kmNJmFqqokTBaXyk2KGryHubqf6JuLz19+plPP1azacJaKxbKwYVobShfMGOq6yNT95I3XV1pt2MoY12MUTpaSw43bUDhZSuMvxnaEW9ZJ4YmwepvGudC/8gR8vQMYsaw9Ezd0I3WmJEbvZ83jdx48fs/rOx9w8tLSunZhVoxrzrBOlYkfR7+DIUu/2W0Nb3cDePj5E6DR4OBtR+NsuZlboQabajUmc3z93wL8JSlA+SVFUTIrivKfFlyKotgpijIBSExwKHl8/62twGegiaIoBX/6/KjA+O8fLgrnyxYiwtEEBb+91iHvQHYsOw5AwbLZKFg2m8F7WetoAYANLhdp09WZm4/e0qtlOVZPbGlQoaYxrCEjw6IA5cKr19R0Xsucs+eokj4TR5u2o0yqtKZdWMR5uPhHknVC2LbbZx7StfAQdiw7zt3LwS/oKAYeqtRuX4b+c1qwam8fylfPY/A1hGdGajQ6tDoVJy8tZQtlon/bv1g6pimZ0iQKda0lizNNEVaFmUnUGMRycmJJldosqVybJKa84WbjmShZJ0Tk9PHuc/Y5H6N6y5IsOTaEfKWyGLWPNR20Aew/fJuWHZez/cgNAErmz0DOTMlCXRcZu5/suneffdeDX3Ruki0X2+o2M36EuQ0UY0reCSH+ROfuQa22pek9rSl9ZzYzahKDuZ6p6kOj0bFy/Vk691nLF08fCubQrwtoZOp+ogJrr9+gxqo1nHz1ggdfPhl/IVbCklknhSfCJtw+85BuRYYyv89qMuZKSbl6hUzaz5rH74RYueI0G7ZepHLJbGyZ0Y5m1QviYK/ff7Kmjt+JCIdrIXrv2MewQ0fIkiAh+xu2YnjxssR0NHy++/+R8Tv/Vg14ryjKEUVRliqKMklRFGfgMTAUeA90DPlkVVW/fv/YHjihKMpyRVGmAjeAYgSH3GYz/xmEiDA+u3kQ8P4zOncP6nYsx7i1XRixLGKN33n7zpMhw7YybNx2okV1Mujw0NJFmqYyNR8DtVrmnbtAjZVrufzuLU883AEwaVcbeLgYBiTrhLBx3p6+LB+whoB3n4geKypz9/WjdM18eq2NGTc61VuWJMAvCM8v3+g/oT4LN3Uld6F0Bl2DOTqETZq0l3HT9pIsUWxWTmjB3y3KEC1K6A9LI2v3E7ev36i/ZiNTTp6iTOq0HGnclhY58hifi7adiZJ1QkRCvt/8WTxgPb3KjMH3qz8TN3Sjw4g6Ru1lru4n+majp5cvDp4anLy0dGxQnCWjmtK3dXmi63GIGNm6nwTvYcdXzwBSxY7D3oYt6Za/CA52Rh4fWXcWSt4JIf5oxYA1rJ2wg4oNizBzR2+Spja8GM/aup88efaRLn+vYdmS4JcVc2RMRqUSoU8KMHdBpiW7n7zy9KLPzv34ugfhaGfHxlqNqJ8lh/EXY1kWyzopPBE2Q6fVsWfpUdrlHsCG0cH/fucqmpFilXMZvac1j9/x8wtkycqTtOnqzI0Hb+jZvAxdG5fSe70pB2sQNuN3rKEARaeqbL51m4rLVrLl1m3a5S5A+bTpTbswkO4n/88VWAEkAuoBA4D6gDswBsihquq9nxeoqroTKAOc+v65PYEgoC/QRFVV1VwXL0RENrL2FFaM3EL+0llYcmwITXpWxNHJ3qA9rHn8zpkLT2jRfimPr74BoFuTUjSqkg97PQo1I/v4neceHnR22cWH98EXs7xqXQYXLU10B8Pf5PjBtg/bQiNZJ0QEEiXQn8AADUMWtqHfrObEjBPtj59fv1M5kqVJwP715xjRagmLRm5Do9EyeVlbxs1uRqp0oXcW+Vl4F2i6nrhHy07L2XfoFk2rFaRwrjR6rbPVbDQ1FzU6HcsuXaHayjXc+vSe+llyGNwR5z9sMw8l64SIxB5ff073YsPYufAwAX6BJu1l6D2kOe4d/+6/gR17r1G/Yl42TGtLyfwZQl1ja297g+kFmYcfP6Gq82qOPH/KwCKl2FGvGVlM7SBtfSTvhBB/pKoq6ybuYES9GSROGZ+5+/qROY9+nUL+zZq6n2i1OgICggsy6/6VmzHdqzO9fx0Sx48V6lpbysOweG4aTxMNezs7ZpSvyoqqdU3rjGkZFss6RTLx/8WxT6AWjVrd0pch9DR6xwCKVcrFjbOPWD5uJ0/vvjV6LzVe6N9cf0Ub+88PKX8nMI5hBztFCqbnwecvfPLwJm2K+ERxcuDh8496rw+IZXydWZCJ31NNXQ+giRk236syJUzAff/gVlm1Mmblnc83Lr8z/t+bH2JqTN/jX6LF/P9xaQ/qjb6qqmrB33y63mImSKXmqtxbr8+9sLF/mHxNYX0k7yKXRCkT0HlGK0rVyMe4jis4d/CWUfuYMysNzUk7O4UJ4+pTPG96Xrz9wtz1Jzl/47lea205I8MiH53s7RldoTyNcufinfc3Jp0/ye4nD0zb1MhcfNlqSJjljuSdkKwTf2Jnb0fTgbVoPqQO3l6+jGq9lIc3Xv7n85KnTcTMnb0J8A+id80ZeHz6hp2dQurMySj8V3ZqtCpJgH8QHevOw5jnK4ZmpKH5mC5NQh56BnfP/atoFp68+sRLN3e91hqbj6ZkmzXkYpyoUfni4EuCaNFpkysfi65dwldjeBfTH36RiWGVd5J1QrJOhBe7+PEoWDYbRSrmZPW0fXh7+hq1j6H3kOF975g9SzL6965ChtSJaD9iPfeevtdrXWTMxMqZMzG6Ynn6HTvAqdcvDF7/stsAyToRZiTvhKUkS5eYdpOaM3vARvx8AlAUxaj7PjDuuWp43S/a2SnUrZGfjq1Lo6KybOs5XA5eQ6sL/c9mTCZaKg9NyUIFaJU/H/1LlyRIp2PS+ZNsun+Ln3cMq6yDiJN30vFE2LRxDWeyoO8a0mVLztz9/ekzvSnxk8Q2ai9zvtENhlfmX7zyDK8XXt/bQ5Zg1YSWjO5ejWSJ9PvzWnq0gDW83Q3w+PMXHLztcPS2o3uBorjUacqiyrVIFes/484MI91PhBBW7NObL4xvPIteZcf8KDrJXyYrsePFMGgfax5Vp9OpDBm2lSFjt2NnpzBzYD1mDa5PisShf3+3dEaaIizyMVCrZeihIzRcv5FPvj7MrViDLXWakCKmcf9PBUT07idCiAhAp9WxftJOuhcfwc2zj3n56N0vP692+9LEihedr+7eJE4Rn8Qp4qHTqbx44Ma2JceY1G0VU3qsBvev2Hka/k09PNspAzx/+RknLy0xfKBXizKsm9yKns3KED1a6ONHbXH8Tljkope/Pw7edlRIkoGeBYpxpElbKqfLaPyGkolCCBukc/cgTZq4VG1enOUnh1GlaTGjukFZW/eTew/f0bHHaoZP2PGj6KRA9lQ4Of65M6ilup9YMhMPPXpM+aUrOHf/FQBtc+WnbGrDxgwKIYSte/f8IxOazMLn9XscneyZu68fDbqUxyGU3PgVa+p+otOpbNt9lTZdV3D93mt6tShL1VL6jZWJLN1PVGD1tetUX7WGO58/0CpnXuyNHUEXicjfIWHTtBotu5e40jZnf7bNPUi5OgUpWDabSXta86FaiOnT97N283nKFsrIpult6dm8DLFjRNVrranjdyJKAYoK1F+9gVlnzlImVTpcm7ZlYJFSxHQM/QHsb8n4HSGElXtw+Sk6dw+cAnwZPL81S08MpUKDQgbvY81Zee7iE9p2cWbe0qNkSJUQrU7/3LPVEQMQNvl43e0dDdZsZOjBw8RydMIzwPDC2v+QgzYhhJV7fuc1E5rMwvfNh//8XpSojrx8+J71sw4RL3Fsprr0pM3gmmTImRIArUbH/asveHI7eOSbqqpmG+dqaD4GabR06LGag653aFKtAFtmtKNa6RyEdoZoq9kYFrm47c5dGm3YxNfAAJZUqYNztXqmvbAgmSiEsDEus/bTvfgIXj1+T6+pTZi1u4/ZRg2EZ2GmVqvj9LnHOHlpSRA3BjMH1WP9lNYUzxt6UYUpBSjGsuT4Hb+g4K5dUX0daJQ1J6uq12d+xZokjm7YSyxCCBERRAny56ObB+2H1WbBwYHkKmp4cbox94vhmYnvP35l6IhtDBjpwoHTdwHIniEpMaP/+TzK3HkYFveHxnrl6UWrDVtpvXkbGp2O2E5RaJc7P/amjmeNoKTwREQIPl6+LB+2ifZ5B+LqcgmACg0KUbFREezsjPuP31wPDMHwh4Y+voEsX3Oa5h2WceT4PZpULUD9Snn1Xm/Kw0Mw/YbHWg7Y/DUaFpy/SMXlK9l7/yFd8hUmS4IwmFsqb7QJIaycv08A/SqO482Tj/Sb1YLJm3uQMkNig/cxd1bqS6PRsXXXVZq0WYz7U08Axv9dg+Y1CuLoEPobCZZ4my0s1oPp+ahTVbbcvkPNlesI8NDiYGeHS50mtMyR1/gbKslFIYSN0Ll7oHP3+PFxgH8Q+9edZcPsg7QoOJJ1Mw9QvEouuoypR4Kkfy5AMGeBpr48vXyZNu8QXfqsxe2TF8M6VSZ9Sv3ufyyZjaYw9Z7x2ls36q5az8TjJyicLCV9ChU37YIkE4UQNub5ndf0Lz+WyW0XkTBZXNJmTW70XsYethnC0Ges315+ZdCorWi0OmYMrMeUvrX16i4d2bqfaHQ66q/ZyMzTZ6mYNgOuTdrRIkde7OTQTQgRiXz94s2YetMZWX8mTlEdmerSk4FzWxIthuEvDZvjftGQPLx09Tn2Hhpi+MDkvrXZNL0tFYtlDXVdZOl+AuDu64eDtx01M2ZlZIny7KrfwviLicCk8EREKB9efUbz2R2duwelauSj74xmzN3XnzzFMxm9pzkqEEMYenP06Ys3U+ccpH2PlWw+cBWAIrnTUKVktlDfXAPLjhYI2cNUYVGA8sHbm0EHDlFhuTNX37sB0D53AYokS2naxVnpA0VFo+LkpdXrhxAi4np57y39yo9hdndn0mdPwYKDA0M9RPsda+1+otEEZ1xsfzuiRnGkR7MybJjWhjKF9HsjwdZH1IWFBJroBGq1jCtdgb0NWpqWjWY+bJO8E0IY6+fik5+5LDrKqsl7yV4wHe2G1Ap1H3N1CDM0Hx8+eU/PPuvoMWA9T19/BqBa6RzEix091LWRcfyORqfD+co1Kq9YxaTzJwHIEDc+JVOmMf6iwohknRDCXI5vOU/73AM4tPggABUaFqZG65LY2Rt+vGBt43eu3XxF+24rWex8goI5U7N2cmu9Okvb6vgdYwVqtSy8cJFqK9dw8+M7RpUoR9o4cY2/GD1J1gkhrM3FgzfolH8Q6yfvImHyuAT4BRq1jzle6DOmU+aQUdv48OUbY3tWZ/bg+qRMEvePa8ydh5bMQoDNl27TfdeeMO/+FVHyTgpPRIQ1otYUJrZeQMw40Zi8uQejnDuQPG0io/ez5gKUZy8/o/ngj5OXlmqlczKqWzVWTWhJoZz6tb+0ZAFKWByuQdgcsL3y9MLB244Y/k60ypmPzXWasKhyLdPbKVtpAYoQQqiqyoFVJ+iQbyDz+qzmy3svAKPy0prH7/gHBDF0+Fb6Dd9CQKCGyX1qs2B4I5ImjBXqWktnpCnCojjzi68vrTduo9vO3cRyisLmOk2YV6EGsZxMHE0nhBBWTFGU/3Q/CXFyz3Xev/pC8Sq5SKhnwaY1jt9RVbj7wA0nLy3J7KMxqH0FtsxsR5Oq+bEP5RAxso7f+eDtjccnfxy87eiWvwjrajZkfsUaJIkR0/iLEkIIG+Ln7Y+qqujcPShULjvdxzdk7r5+5CiU3qj9rGnUgEajY+O2S7TqtJxpzq589fEHIHOa0DuD2tr4HVPz8KWnJ203bafu2vU88wz+f6XambIRzcHR+IsSQggbE+AXyJpx2xjw1zh0OpU48WPQb2Zz4iUK/Vnjv1nb+J3HTz/QvfdaZi86Qs6MyVg3pTWpksYLdZ0lxu8Yy9QsPPToMRWXrTT+AiIwKTwREdrJrRdpn2cgK0ZsJnfRTCRMZkIBwXfmOlQDww/WACZO3M3YqXuIGT0Kc4c2ZNbg+mRIFb7tk8E6ClDC4oANIECjoZrzGmaePkuZVOlwbdqWwUVLE9PRxEM2OWgTQlgpz49fObzmFDp3DzLkTMnSE0PpP7sFcRIYfpBizQUoV66/oGO3lcyYf5jYMaPy1Tv4YaI5uoQZy1ry8fDjJ1ResZrZZ86RIlZsfIIM+/+T/5BcFEJYqWTpEpMu5z/dnVQPzx8FKHZ2Cr5f/fjk5klQoIbosaMZtLe15uMXd2/adV/Fncdu9GpZjrWTWun1EkNkHb8DMGKfK7POnKVi2owcbdKO9rkLyIxvIUSkMqHJLMY1n0esuDGYvr0X/We3IF7i0EfU/Ju1jd/59MWb4/vv4OSlJXfm5Kye1JKp/erI+J1fePDpMw7edmSNkpA5FapzpHEbyqU2rghJCCFsVUhBZsYM8SlVMy+LXAdTsloeo/Yy18sK+tDpVHbsvU7LzitYtvUsr98H3xMnihf6s2Jb635ibBaa/Gw0gpLCExHhBQUEsWXmPppn7sWNfZcBaN6nCvU6lcPRyd6oPc15qAaGtYdUVTh68j6tOi5n/rJjZE+fhCzpkhj09Yw9WIOIU4ASoNGw8MJFKixzZu/9h7TJlY94UQ17sPxLctAmhLByLy4+YOOUXZSumY9lJ4ZSuUlRFCMOUozJSTD8hgkMy0mtTmX3gRu077oSzQd/7O3tWD62GW3qFCGKY+jfny15yGbpfAzQaJh//gKN125Cp6rEiRKVHfWaUT6NCQ8XJReFEFamfONiTD0wlLINiwLBDxLhewGKTiVW3Ohky58Wp6iO6LSG54G5CzT19fqtO4OGujBkzDYcHe2Z3Lc2sWOGPmYAIuf4nUCtlgXnL1J15Wouv3vDiBLlaJMrv/EXJIQQNujMzst0yDuADVN2UbpGPtJkSmr0XuboMm3oy31Prr9l0YoTFMiRio3T2tCuXrFQ7xktNX7HFKY+Q33yxZ0mGzbjp9Gwsno9FlSqGebjB4QQwtpdPnSL7sVG8P7VF4YtacfAuS2JGcfw8yRr637yxd0bl/UXcfLSkiJxHLbMbMfwzpWJG+vPfzZLdD+x5Pgd8Q8pPBGRhu/3b7yqhyfpc6Sg44g6LDk21OjqQ7Dut7qDNFpcdl6hafulHNt3G4B6FfLQrUkpYkaPEup6U97sBtNveqzhgA3go48Pgw4cotxSZ15/Cx5BMaxYWYomT2XaxckhmxDCSgUFBLF2wg66Fh3Oiwfv6D2tKePXdTVqL2vOyRBxA+358OUbnRuVZNOMtlQsllWvdbbeIcwUKuDgbUdyYhHTMQrO1eqxvGpdUsc2cTSdEEJYgQdXnuH1+RutR9Sn6cBapMgYfIimqirxokDLvyvg4GTPuYO3efP0o9Ffx1wZaWg+nrv0lLadV9B/+JYfncFqls1JFKfwOWgD2y5AeeXpRcctO+m8fScb7t0CIFuCRGHz0oIQQtiAAN9AVo/dRstsfbhx9hEAtduVIU/xTAbvZW2HbRqNjk3bg8fvnLr6lI4NirNkdJNw7Zhpq3l45e1baq1cy4zTZ6iQJgPb6zYz/mKEEMJGvX70jt6lR7F67DZK1chHu6G1jN7L2jIR4Ourr7jsvEKlEtnYNL0tNcvmDDUTjclDSxVjhtVEhchOnvCKSEdVVcbUm07+v3LSaWoLhi1px91Lz5g7ZDOvHr03as+QAFDjGTa/zf6rH1oD2zOHBEFgHP3mZnp7B3xfpyVN/Lg0qFGAWuVysWrnBbYduUmQRvvH9SGhEBDL8Dq1kG/yQSaMvHb0Nm09/HPApompGr3HJx8fHLAjfrRoVE2fiY55C3Lg2SMmnT/Jq69exm0acsgWU2P0dQkhRHh5/dCN/n+NpWKLksRIkuDHrzs62RMU+Ofs+DdTchIwKCsNzclv3v6MHrOTHTlT0b1Lecb2rE7DKvkYPHMX7l6+f1xryYw0NR/DIhsff/lCrZVraZU/Hz1LFONI47YsuXGZ2VfOoVON2FeKT4QQVuCq620mtJpPp0lNaTWiHkWr5cPt2Qe8vnyjWLX8JE6dAM9PX1k5dH2YfD3F45vB+QiG30sako+BQVru3nfDCchcMCVDO1WmXb1izFt/kmMXH/1xranZaMlcNCUTjz59Fnwd2DGncXUSRY/BlAun2Hz/NsbvKoQQtsPjQ/CzMUcnB2q0KkHKDEk4uecay8ft4vM7T4P2Mub+MTxz8dMXb8aP382+3DeInTwWqho8rjVx/Fh8+PLnQ8Eo33QGZ6Ip94qm3meakodBOh2LLlxi/4NHpEpu+rh7IYSwRTqtjg1TdnHp0A2++ATfGyVIGgdvLz8C/AIN3s+Y+0VjMlGfPAwI0LB8zWmOHL9Hn16VGdqpMlVLZafnxK1oQ+kGas48tGQWCul4IiKxa0fv0K3QEGZ3dyZxynhogww7RPsVc1QhhjDmze4Fy47Rsddq7j//QK+W5dg0vS2Fc6XRa62l3mALi/UhwqJa0d3Pj8orVjPj9BlKp0rLkSZtGVy0NDEc9Tvg/CUZMyCEsGJH1p1h54xd6Nw9KF4lN0uODSV/Gf26gvybNXdAuXnnNV3+XsPkWfvx9gnA8/vXc3QIfSyfqRlpLGvoDhak07HiylUqLV/JwUePyZYgkXFFJ0IIYUWe3XrF4OpTmNx2EXb2dhSpkpcqrcqQOHUCTm27xOxuznx4+Rmde/Cc66rNi9NnRjNixY1u1Nez1vE7AI+uvKHnwA189fZnQq+aLBjeiAypEoa6zlKjBiz9hpsK9Nq5j0fun5lctjLb6jYje4JEJu0phBC2JChQQ9fCQ1kzbhtFK+Zk6fGh1OlQFjt7w48hrG38zvVbrzh58C5OXlqqlszO5hntaF8/4o3fMTUPX3p6cubeS+MvQAghIoAnN17i8fg1OncPBi9ozcLDA8lWIK1Re1lb95OXr7/QZ8AGpsw+wI0Hb34UndiHkvWW6AZmLOl+YjwpPBGRmk6ncmDVCVpl7cPrq8Fvbg1e0JqOI+sQO57xsyjN9dAQDD9Ye/LsI4OGbKHf8C14+wb86Hji5Bj6wRrYfgFKWARGgEbDoguXqLhsJXvvP6R+lhzY6dNnMzRSfCKEsHIez9+hCdIyYV1XRq7oQIr0iY3ax5ichPB/kKjTqRxwvcPgYS44eGqIFSMKW2e1p2fzMsQJZXYpWOaBYlisB9OLMz/6+NBv3wF6bt8LQNo4cVlTvT45Ehr374gQQliDk1sv0rPUKIbUnMqAKpPoWWoUk9os5OLBGz8+R+fuQawodpSvW5Dlp4ZTq00p7B2Me9RireN3bt19Q+eeq5k+7xDpUyVkar86et//RMbxO4+/fKH5ehf67ztAqthx2NOgJfmTJDf+goQQwsYE+gexfvIuOuYbzK3zT2g/tBZJUsYzai9rO2wLceP8c05eeUKH+sXZOL0NFYplCXVNZBu/I4QQItiqEZuws7Nj+vZe/D25MXHiG3f2aE33iqoK+4/cZtXy0zh5acmdJQUuM9vrnYeGMjbTwiILhWGk8EQIgttfAShfv+Ln8Y3a7crgfGYETf6uRJRoTkbtac631sDwm6Qr11/Qqccq7l4IrkDv1rQ0K8Y1o0juyNEBJSxunj76+DDowCEqLV/Ft8BA7BUF52r1qJMpG0bvLN1PhBBW7O75R3QtNATnUVvIUywTi10H06RnRaP2+ndOKgYU8IV395MQ0Xzg6rUXNK6an22z2tOuXjGiRw29w5Wt5mNYZKNGp8PB2470jvHJkSgJexq0ZGrZyiSObnxBrxBCWNrDq894fP05T278+u3dTdP30L3ECJ7dfUvXcQ1YdGQwuYpmNPrrmXIvaQhD8lGnU9lz8CYtOixjzKRd6FQVRwd7WtYsTPRQ7pkt2RnMFKZm4s5796m0fBUzTp/l+gc3AHIkTIyjnTyKE0JEDh9efWZk7Sl0LzGCdy+/ANCwWwVSpDO8E5S15eLnL95MmLCb3kM28c0ngHE9azC6ezW91tpiRzAhhBDGu33mIV0KDWHnwsNUbFSE5aeGkzWffudw/2bOe0WDeATy1duPcT1rsGhkYzKn/fOLaOZ+ec+ULJRCTMPI3a4QP9FqtMzqtoIuhYdy8/xjWg+ojvOZEUa3wALrLkAJ6YTv5KXlyR034sWOwezBDVg0sjH5sqbUaw9jHyCC5Q/YIGxC41tAAA7ediRVY5E4egxmV6jOvoatKJ0qrfGbSgGKEMJKBQVq2Dx9L21z9+fQ6pO8f+0OgIOjPY5RDP++FZKT/cfXp0O/KsTUcwapOcbveHr5Mnn2Adp2W8mlOy/p2KA4W2d3IF7s0EcpWLpA0xRhkY2nXrygwrKVLL98hdqZs3GiWQe65Sts2oUJIYQVe3H3DYMqj2dkg5koSuhthvVhjePpvnn78+DRe5y8tBTKmZpuTUuxdVZ7GlTKi0M4tla21be9vwUEsPTSZey97Yjp6MT6mg3ZUKuR8RckhBA26MXdN+jcPYgXBZr0qMjio0PoPKouMQ0cUWeuEeeGjt/p3HM1k2ft5+DpewBEjeJA8sRx/rguso3fEUKIyM7P258lgzbQpcgwLhy+w/P7wYXphmZhCGvrCPbg8Xu6/L2GqXMOkjpZfFaOb0HP5mVCXSfjdyIeOdUU4hdePXBjTL3pZC+SkSZD6/P66UcA4iWKhednb9SQig0DhISAGi+WQetCgkCr50FciJBACIwT+pvZAIeP3+P46YdUr5yLFk2KsXBkYxZtOs2a3ZdCXRsSDAGxjHu4GvLNPiimUctNXg//VO9rYhr+zzbEB29v6q5aT/WsWehbpgRrajTgzJuX9HLdxxc/X+M2DSk+iakx+rr+TdGqRr35L4QQP/P8+JW5vVYBYBc/HrXalqZWm1KsmrqPk7uuGZSV6bOnoEyVXNjZK9RtUZyD266wZuFRvNx9Ql1rTE4ampEvX39h9JidbMqUlCKlM+HxNfh7eq5Mybn39B1a3e//rKZkpCn5Zg3Z+C0ggKknT7Phxk36ly9JQjN3PZG8E0JYwsUDN7h8+BY6rQ67+PFoN7QWMeNEY+30/Xh8MryQxJz3kYbm45WTT+j0dg2dO5WlX5u/aFQlP4s2neb4pcd/XGdsNloyF8PiftHfQ0PfPQdwjKXfmFt9SNYJIWzJl3cetM3dn1bD61GzbVnK1y/E+pkH2LfuLFqN/odOxmSjobloSCaGjGwFcAKatSlK6zpFcDl0nVU7L+LtG/DbtaZkoq3moaEk64QQEcnrh25MazMfgCjJEjF3bz9ePnrH0rE7effis8H7WVMm6nQq+w7f4sSZB7RqWpwP3v9UeTjY26HR/j7rjclDYzMtLLIwPHIwouSddDwR4g/uXXzCyNpT+PrsLYqiMG5NF+bs7Ue+UqHPKfsdS3RA0VeQRsvOfTdo1n4Z85cd+/HAMFXSeGRO8+fWWGDa290QMTqgqMDeBw+psnw1Y48ex0Gxw9M/+J9bFHsTav2k+4kQworp3D14eOYu3l5+DJrXitl7+pKraAa919fpUAY7e4XdzqfYsew4pSrmYOOxQfQeXYf4ifS7cTJHB5QHj9+zekXw7NIkCWKxcEQj1k9tQ9lCmUJda8kxA5bOxjdeX+m9Yz+TDp0EoGjyVOxp0IKiyVOZdmFCCGGlQka56tw90Pn6UbFhcDvlxj0q4qTHyLZfMed9pCHZ+PDJe/oO3MTAUVsJDNLSomYhvddGxg4op1684Ojtp8ZfgBBC2DjPj1+Z+/cquhcfwZPbb2jetyrRYkY1ai9re9s7xME9Nzl64j5NqxVky8x21KuQB3u7P2eHMZlo6TwUQghhGu1nd/YtcyV3sUwscR1C28E1iRYjilF7WdP4HR/fQBatOMHOzVcAqFQiK+umtKZY3nShrpXxO7ZPCk+E0JPO3YNts/cSO150Jm7oxsQN3ciU2/gDE3M/ODTkJikwUIPLzit8ePgZJy8t7eoVZfWklkzsXZP0KROEuj4sClBMYep6MP0GKlCrZc2167TY4ILyTSGmoxMnm7VnVIlyxI9qWPcaIYSwBbfPPKR70WFM67iEeIliMdXlb1oPrB7qurRZk1Gqel5ePf7AkjE7WDvjAFN6rGbPqtMULpGJdUcGUKVeAb2vwxw5CeDxzJNRk3ah06lM6lOLFeOaUSB76P9fYIlDtrBYD6Znowo4eNsRLdCBeFGjs6l2Y5ZUrk3aOHFNuzAhhLBiziO30LHAYK6ffkibQTVYdmIYuYpmNHo/c91HGpqNF688o0O3lQwfvQOAuLGiMaZ7NdIkjx/qWkuMprN0JgohRGT3/M5rhlSdQLeiw/H29MXOTqHr2PqkyZLM4L2sLRc/f/Fm8uwDdOq1mmevPzOgXQUGta+o11pbKsiUQzchhDCNJkiLy6z9tM8zgOMu52nUvQLLTg4jaerQz+B+xRoLMp28tHx9G3xNMwfWY8bAuqHeI9rS+B3xX1J4IoSeVFXl6MZztM89gMUD15M+Rwrm7utPgbJZTdrXmgtQQsydcxjndWconDMNaye3Zkz3aqRKGi/Udaa+3W0NB2xhdQMV1c+BE0+f0ypnPk4270D3/EWI5mDc245CCGGtVFXFdcNZ2uXqj/OoLVw5fh8InlcaJ/6vx6zU71wep6iOHN8RXAUf6B/EtVMPWT11H5N7rGHbkuPcuvLC4GsxR06eufCEdl2dmTxrP/HjxGD24Pokiqdfn0ZbLUAJi2w8/eIllZevYvqp05RImYYjjdvSp1Bxk/YUQghr5vb0A2MbzGBAlYm4f/DC/eNXAOwdjH8kY40FKDqdyhd3b5y8tGRPlJAS+dKzbkprBrT7i/hx/jy73BKdwawhE4UQIrL79OYLOncPkieMSrm6BVlwaCA9Jjb87f3j75iSi4YwJBcfP/tI34GbGDpuO1sOXQcgYdwYob7UZ6mOYMaSPBRCCNO4v/diRudl/F1mNBcO3+HDa3cAYhgwNvVn1laAcu3mK9p1dWb+smPkypScdZNb0bxG6J0yzdkNLCzOE0UwKTwRwkBBgRp2LDhEm+x9WTZsEzfOPAIgR+H0xEsc2+h9rbkAxcc3kNUbz9Gk3RI2bL1IqQIZqVE2p97rpQAFPP39GX7YlWor13D+7SsGFCnFiWbtSRDtzw9gbYGiKA0URZmnKMppRVG+KoqiKoqy7jefm/b77//uxyZzX78QIuwF+gexefpebh+6is7dg6Z/VyJ2/P8WZCROGZ/MuVMDUKRCTup3Lk+WvGmws7fDzyeA2xeesGbaPt7dfGFURkL452TIPO+WHZYxYKQLnzyCQ6dZ9YKkTBI31PWRtQAlQKNh8cXLVFjuzLY7d3H3M3ycoLlJ3gkhTHXr9AN6lRrJ6ysPARi8oA2DF7QmccrQu4L8jin5aAhD7yGvXH9B0/ZL2bXvOrXK5sJlVnva1y8W6rrIOH7HmkjWCSEs5dUDN9rm6s/epUep0rQYK06PoH7ncjg42hu0jzkO28CwsXRnLzzh1e3337tKF2PN5FYMbFeBeLHDpyhT8vDPJOuEENbq4ZVnzO2yBO0Xd+ImjIXzmRF0G98gQhSgaDQ6XHZeoUXH5ew/cps37z0AcLC3w04JfRydoaQAxXJ55xB2fwQhIhffb/5snb0fADs7hQGzWxAnYSx2rjjJ1kVH8THwhiVESBCo8WIZtC4kDLQGhFBIIATG0a/zxtdv/ixbfYqtu64QFKQFByiaOy2lCmZg1c6LfHL/83fkkIAIiGV4zVvIN/sg/V4kD/P18E87ZU1M1eg9nrq7023rHvKnSE75bOn54ucLQKZ4CXjs8cX4i7Os4UAewBt4A+jTBugmsPMXv34n7C5LCGEtNo5zwVv9b9YE+AWyaNQ2okZ3olab0rQbWpOnd97iPHE3N84GF3ZqgrQ/Pt/YjATjc1LfjAwM0nLt5iucgIQJYtK+fjG6Ni7J7hO3cd5+gS+ePn9cH+WbzmL5aMls/Ozjy/DDrsEfmHAdZiJ5J4QIM6qHJy9uPKNBn+oUq5SLHctPsGXBEXy9Awzey1rvIb2++jF3yVG27b5K+w5lSJdC/5bRxt47mpKLpmZqWNwvWgHJOiGExXxz92Zh/7XsWXaUTtNaUqVpMXauOGnUXsZko6G5aOhzVQDn5SfR+gRRp3o+KhXPyupdl9h88CqBP933/psxmWjpPLTyLJSsE0JYPf93Hzm68Qy1OlekZLW8LBu380e3aEMpHt+MulcMj2eonl6+zJh/OPiDOPa0rFWYsoUyMXvNca4/ePPbdabcH1oiC62ERfJOCk+ECAM6ncqgqpNoPbI+TXpUpFrz4mxZcITdq04TFKAxak9rfXgI4OEZXCzhBGRMEI+aZXNRvXROdh69yZrdl3D38v3jeilAgWtv3bj21g0H7EiSNCZ7G7bk1sf3TDp/imsf3Iy/OMvoQ3BwPQHKAMf1WHNDVdXR4XlRQgjr8fXLP4WJdvH/GdXm9cX7R+ewC4fvkKtoRvrNas6IZe0Z3W4Zty88+eV+phaghGdGQvBM7+YdltGycTFqVc1DtVI52HzwGmt2X8LXL/C36yyVj9aSjQ7eVt+MUfJOCBFmVFVl7YQdHFh5grZjGtK4R0UqNS7K+E4ruHfluVF7Wus95Nt3nowdtwt7ezuIqZA2RXzG/12TxZvPcOba0z+utdUCFCs/cPsTyTohhMW9fujGiFpTiBUvBlrFiajRneg7oxmbF7jy9M7vD6V+xdjDNgifApSv3/yZt/QYO/fdoHPncnRrWoq4saMxb33oBTbGvKxgqTy08s4nknVCCKvn+82fxQPW47r+DD3nt2fg3JZUalyEka2XGHXmaI0FmU5eWl49+kjscrlYOLIxRy88ZP6Gk7z//PsuLZYoxrTh4hOL5J3VP90Vwla8e/6RyW0X0a34CB7dfEWH4XXIUyyTyfta8wgegC07r9Ci4zKOHL9H/Ur52Da7A81rFNRrrYzgCfb5gw9jXY+TJnZcttdrxpLKtckQ1/h22+amqupxVVUfq6pqs09XhRDmo3P3QOfu8Z9ft7NTuH3hCS6LjhI1hhOVGhVBCaXVojVnpLuHD3MWu9Ky8wpOXXlCnfK5Q20dGcJS+RhW2RhRSd4JIcLDZzcPpnVcSo+So3h04yWvn3wAIHrMKEbvae581JdWq8PJS0s8nSP2dgrT+tdh4YhGZEufJNS1lspFY9nKuIF/k6wTQliTbx4+6Nw9SJUkBrmLZWLuvn70md7U4FHnpuSiIQy5Z3z91p3hI7fRZ+gmNu4LfoM9Q6qEZM+Q9I/rLDWSLiKRrBNC2JInN17Su9QoZvdw5vWTDz+KTuzsjLvXsKbxOwCnzz2mVaflOK87Q4l86dk0vS3VSmUPdZ0xeRjZxu9YKu+k44kQYezpzZcMqz6JzPnT8eSFJwB1O5bF56s/R7ddQqsx7oHZzxWJMWNHw1vPb/Tm6IDy/uNXps09yIatF2nTrDg+vsFvczs62BMzuhMeoVxrZO+AolVVNt+6ze7792lboAAdixRkf+pWFFu7BHd/40Y22YDkiqJ0BhIAX4DzqqresvA1CSHMQFEUVFVF9fBEVdUfHVB0uuDvoddO3sf941dyFslAlGiO+Pv+vkPIjz2t9A1vALf3nkyYuIeYMaPgba9BUWBavzqcvfGcvSfuEKQJ25bKIUypyJdRA2FK8k4IoZfH158zss5UAOwTxGfy5p54fPqK86Q9vHz4zqg9zZWPhmbj3QdutOviTPXKeWjbsgTO41uw98QdJiw99Md1lshFyUS9SNYJIcLVw6vPaJurP00H1qJOt0qUqpGPrYuOsmne4R/3kfowx9veYFguXrv5KngN0K5XMcoXycyR8w9Y5nKO1+//+8JGCBk5YHaSdUIIi1FVlQMrT8DKEwCkK5adkcvbs3rqPk7tuW7UntbUESwgQMPqjec44Hqbjp3K8fTNZwCiR3XEP0CD7g91E+bsBhZJstDkvJPCEyHCyaNr/7RHLvxXDvKWyEzjHhVYP+sgJ3ZeNejG6GflymRmwJwWnD58l21rzvDozlu91pnjcO2tmwcTpu8L/iCOPdVKZ6dPq3LsOnabDfuu8OHLnyspI3sBil+QhoUXLrLp5i2Kpk71o+ikefY8uL58ykvjL80aVfz+4wdFUU4ArVVVfWWRKxJCmEXWQhnQBGl5fD04J0O6n4QUoPh89UfVqThFdSRB0ri8ffZR772tuQDF2zsAJyBe3OjEihGVge0q0KZ2Edbuucye47cJCPp9m0xLjBkIi/WR5LAtNJJ3QgjDeXlxYvNZmg6sxcLDA3Hdepl1Mw7wye33B1B/Yo0FKFqdyu4DNzhy4h5N6xdG4/TPG3tJEsT6472jrY7fgQibiZJ1Qohw5+Ply/Jhm9i3/BgdprYkZ5EM6OYY9z3VnAUohoxsnTptP28afKZhnUKUK5yZ/afu4rz9fJhnoqXHtNooyTohhNWw9/PF3zeQIQvbULdDWZaN22nUuFZT7hPDIw8/fvrGhAm7gz+IY0/vluXIliEpCzed5vyN3//5LFGMGYFz0OS8k1E7QpjBoErjGdlgJn7eAQyY05JFroPJUTi9UXuVrZUfgLyF0jF7XWfmb+5GyYo59F5vrhE8Tl5abl18ieuJ+9SrkIets9ozrFNlUieLF+raiDCCxxTufn7sf/gIB287UilxGFWyPKeadTDtoqyHLzAOKADE+/4jZL5cWeCooigxLHZ1QohwZWenULxWAWa6DqNGx7+I/tNNSsgInsJ/ZSdB0jh8cvMwqOjkZ9Y8gsfD05eefdfTZ+gm3n70ol+b8myd3Z60KUIfsWaJtsphsd5Wxw2YSPJOCGE0rUbL1tn7aZOjH9vnHaRc7QIsPzmM7AXTmbSvufLRkGz08wvEed0Z1jifxslLS6Gcadg6uwNDO1YiWaI/j1Kw1LgBycQfJOuEEGb37vlHxjWcwYjawV3CEiWPx4JDAylZLY/Be1nbuAE/v0BWrD1D0/ZL2Ln3GlVKZqNKydDHDYBxI+ksPabVRkjWCSGszpMbL+heZCgzui4nUYp4zNjRm/6zWxi9n7XlYfDna7l8/ilRnRyYObAeC4Y3CpdxdJFt/M4fhFneSccTIczk4oEbXDp4kxK1CtBiZAN8vvoDEC1GFPx8AvTaI3Pe1BQsl41HN14xb8gW8pTIRKnqeRk6rTHun7zZt+USm5afRJ+RXebqgDJ1zkFWbThL47qFqFElD+lTJaD9iA16rbflDihh9Ubbu2/fqLRiJTWyZjFpn58pOp0h/1OQUFGUKz99vFRV1aXGfm1VVT8CI//1y6cURakEnAGKAB2AOcZ+DSGE9VJVuOp6m3zlctB8SG0y5knDmV2XeXzjBd/cfajQvCTNe1UGYPvS4yZ/PWvugHLt5iuu3dxAnpypqFojD6/fewKQNV0SXrq54xfw+5sx6YCiH8k7IYSt++bhw7Khm9i16Ah1ulXi0fd2/GmzJsftxScC/Q17OSCEMW2VIfxH8AC8vveBHXuuUataXqqWys6eE3dYtfMiH90jVgeUsCJZJ4SIrIICgiDAg1ipY2Nnb8ewJe14cO0FKybu5s7FpwbtZU3jBiD4ZYV5S4+xecdlvn3zhyhQpmBGsmVIyvq9l/n2m+fIlsrD8M5CyTohhPgvnU7l8JpTnNx6gQZ/V0WJ9k8eRY8ZBV9v/c4cf2aOjmCG5OHJs484c+EJNavkpnXzEqwY15xpzq5sd735x3Xm7gYWViJK3knhiRBmpKoqZ3Zd4cyu4O8HdvHj0XNyY1KmT8za6fu5fPzeH9dXa14CxU5h75ozPLv3lpeP3nHJ9S7ZCqajRNXcVKqVl+1rzxJgwANIcxyuffz0jXlLj7Fm03kSxAsuiosdMypDO1Vm0/4r3Hjw53FBkb0A5Y3XVxZfvGz0ehN9VlW1YHh/EVVVNYqiLCc4wEojN2xCREiqqnLjxD1G1JtBs4G1qN6hPCXrFMLfxx9VhYQp4hHoF8TRjec4sfrYj/E7ANValODErqv4fvM3+OtacwHKzTuvuXnnNfaAEt+Raf3r4OBgz/q9l9l2+IYUoJiP5J0Qwmp9fP2FpUM2AhAlaULGre2MTquydsZ+jm27bNQYV2OzEcL34aK7hw/zlx1j0/ZLNG9YlJpV81A0T1oa9F7xx9neELkLUPQkWSeEiHCe3HhJ14KDqdCiFK1HNmDa1r85f/g24zuuMCgfrXEs3cdPwdfk5A/ZMySlVe0i1KuQh/V7L7P54DX8A349rtXceWhlWShZJ4SIVAJ8A1k/edePjwvXK87Aea3YOPcQe1efJihQa/Ce1lSAotXq2LnvBoeO3qVR3UKcufYMgJRJ4+LnH8QXT5/frrXVAhQ9WW3eyagdISxI5+7BlX1XiBknGmPXdGbmzt6/bZ+cOGV8ytTKh9cXH14/+UD0mFHQanS8ff4JV5dLzB28hVFtlhL4zh07T8N7PJkyXkDfFlleX/149vIzTl5aMseNR65MyVk0sgmLRzWhWN7Q20bb8gieCNZSObx8+v6ztKgUIoLz+ODFgn5raZGlD/udj/Pm8XtePXTj5qn7zOiyjGXDgg/XQsbvZM6Tmp6TGrH6/Cia96lCDAMKQX5mzSN4AOzcgxgxbgf3n76ne9PSbJvTgeY1ChEtyp9vwiw5gscUkTgbJe+EEEYLeP+ZKW0W4v7Ri34zm7Pg8CAK/6X/6NV/MzYbgXDNxs9fvJmz2JVmHZYyddYBdKqKvb0d7eoVI36c6H9ca6kRPOL/SNYJIcwm5K3vtjn7sWLEZtxefP5RdBIz7p8z49+scSwdwIqlJ2nbfSU3HrylS+NSbJ3VgYrFsv5xjbnzMAKOHQiNZJ0Qwuq8v/eCB9df0GlkXZYcG0rJ6nmN3suaRvD4+QexeuM5PJ974uSlpV/r8rjMbE+nhiWIEc3pj2st8cw0gjEo76TwRAgLc91wlva5BzC7hzMJksYlccr4v/y8XEUy4PbiM7HiRmfo4rb8PbUJBcpmxcHRHgCPj195/eQDEPxGuTkP18DwG6Zbd9/QpO1iZi86QpIEsZg5sB6rJrQgilPojZikACXCKvr952cWvQohhNm4v/fEeeQWRjaYydimcxhcfQqntl/C69P/59eD4zfpVnwEt84/oUXfqqw+P4qW/aoSNfqfbyx+x5oLUO49fMfgYS507buWh88/0KNZaXJmSqbXWksdtEkBisEk74QQJrl1+gG9So5kXPN5ODjaM2ZVJ3IWyWDSntaajR8/fePazVc4eWnJlywJbesWZdvsDnRvWpo4sf5ciCoHbhYlWSeEMLtA/yC2zNzH0j4rg19gyJuadZfH0G5orQhRgPLsxSeGj9xGt/7rePXOA+X7yY69vR3KH26nTMlDY0SiPJSsE0JYnVcP3BheYzJDak3F3zeQYYvbMmJZe5P2tLY8BJgz9winrz6hbd2ibJ3dgcZV8uPoYP/bzzd3FkYwBuWdjNoRwgpoNVoOrDyB6/ozaDVaiBv3P59z5cR9bp1/QpRojlRsWISqzYuTq3AGVk/bx+HNF3+7tznHC4BhLSMDAjTs2HudPQdvUqFsdtJnS0pAYHCbyPzZU3Hr4Vs02t+HQWQfwWOLFEXJD9xQVVX3r1//C+jz/cN1Zr8wIYRFBf40Ik5RFNRftNJ/evMlY+pPJ33u1DQf0ZCKjYqwaf6RP64JjTWP4Ln38B2DhrqQIV0i7ru7A9C6dhE0Wi3bjtz4bVtlsN1RAxGp+ETyTghhDmd2Xub83msUq5GfOxefAlC2TgFePHDjxYN3Ru1pzdl46+4bWnVZQZumxWlWvSB1K+TB5dA1Vu64QGDQ71tIR/KRA+FGsk4IYe2+PHzNqW2XqN+5HFWaFmPLgiPsXnX6/+4/Q2NKLoZXJt6970bv/huCP4hjT5Oq+alUPBuLt5zh/I3nv10XwUcOhAvJOiGErbp29A7dCg+hYotSBDkEv7hnZ29HouRx+fDa3eD9rC0P3d57MmHiHrZkuETHDmXo3aoc9vZ2bNh35Y/rJAt/LSzzziYKTxRFqULw3CB7YLmqqpP/9fulgdlAbqCJqqpbv/96XmAREBvQAhNUVd1svisXwjBB34sucPcAwC5+vB+/5/Xln9K6lZP3cMTlIsOXtKPX1Ca8e/mF2xee/HFvay5A0Wh0HHS9A653cALiZ4jH3KEN+Ozhw4Z9l9l9/Ha4HLCBFKCEBUVR6gB1vn+Y9PvPxRRFWfX9rz+rqtr/+1/PBDIpinIOePP913ID5b//9QhVVc+F6wVbKck6IYKFVkDy7NYrxjWcQfRYUQlyjIaDoz3zDgzgkutdti89hpf772d7/o41H7I9ff6JkL4uWdMloWzhTDSrXoj1ey+z3TViFqBYK8k700nWCRH2tBotZ3ZeBsDB0Z4Ow2uTIEkcTu65xoZZB3n1+INR+1prNr5182DCjH2s23Kelq1LUjxvepa6nAXATlHQ/eH/I6QAJXSSdWFD8k4I6/DprTvTOy1l29wDtJ/UnPbDalOhQWG6VZryYxSPvozJRXPcLzp5aXn/zJ3ofzkxc2A9rt9/w+LNp7n1yO23ayL7oZtkXdiQrBPC+ul0KofWnPrxcbW/a9BlTD32rTnDxrmH+ephnmeoxuahPln4+OkHBg7ZQoG8abjhFnzvWyhnauzt7bhw88Vv10WGLLRU3ll94YmiKPbAAqAiwX/Yy4qi7FZV9d5Pn/YKaAP0/9dyX6CVqqqPFUVJDlxVFOWQqqqe4X/lQphO94sCFAiuTHzz9CO7nE/SY1IjarQqGWrhSQhrLkAJ4f7Ug4Ejt9K8WTH6tCpPmzpF2XLwGi6HruPjF/jbdVKAYjF5gdb/+rX0338AvOSf789rgbpAIaAq4Ah8ALYA81VVPR3eF2uNJOuEMJzvN3/An6gJYvLigRsNupanZptS7F1zhm1Ljv1fwaa+rPWQLcSoMTvIkS05rVqVpGfzMjSvUYiJSw9x9vqfOx1KAUqYyYvkndEk64QIf5ogLZ0LDKZezyrU6V6ZUtXzcnLXNVZP22fUW21gvdn48rU748fvxsnJATUaxIwehVUTW7D72G22HLoWamGmsfeMkSAT8yJZZxLJOyGsz/M7rxleczJ5SmcjcZZUP4pOchXNqPfz1BDWWIBy+vxjzl9+SvVKuWnVrDhLRjdl5Y4LPwozfycyHLr9Rl4k60wiWSeEbTq35TTpMyakZtuyVGxUhC0LXdm14iQBBnQCC2GOPDQkC6/eeBm8BmharSDF8qbj6t1XLNh4ivvPfv8yRgTPwrxYIO+svvAEKAw8UVX1GYCiKJuA2sCPEFNV9cX33/u/FjCqqj766a/dFEX5CCQCPMP9qoUIA/YO9mg1WnTuHiiKghIvLgC67+NnHlx/ieenbyRMFpcYsaPhY8DMNFMfIEL4HrBduf6CK9dfkDNbCpo1K0qbOkXZe+LOHwtPQkgBinmpqjoaGK3n564AVoTn9dgoyTohjPT1izeTms1hXZbkNBtUi3qdylGzTSl6VZ8e4d7yhuC2yoOGbAkuQGlZgrcfPQGIHyc6vv6B0gElHEnemUyyTggz+Obhw+qx29ix4BANe1ejZueKbFl41OR9rTUbAwM1OAVCHEd7Xrz5QtcmpWhUJT+rd11k59FbBGl+PYJHMvHXJOvChOSdEFbq5qn7cOo+AMWblGLUio7cOv8Y54l7ePj9wEpf1laAotHo2LX/BoeO3qV+rfxcfRHc8SRurGhEi+rIu09ff7s2gh+6/YdkXZiQrBPCBrm/92Lu36vYufAw7SY1p+3gmuQskoGRrZYYvae1FaAADB+1jZpV8tCqWQmcx7fg6IWHLN5yhjfvPX+7xpQstFaWyjvDT2XNLwXw+qeP33z/NYMoilKY4GKnp2F0XUKEu0KVcpO9aCYgePSAzt0DPD1RlODiBHsHO+ImikWsuNEJ9A+9IONXFI9vP8LBUPZf/f6vEEUfTl5BP4JCH3fuv2XoiG00bb+Uz57Brb+m969Dz+ZlSBz/z2EW5ZvuR2AYytHbtOAwdb2Dt/KjCEVECpJ1Qpjo9UM3prRbTKcCg9k+98CPopNC5bITL5Fhh2QhjM1Ic+Tj3ftuDBrqgtu9TwD0bV2ebbM70Kx6QaJHc/rjWmPz0ZRsMzUXRYQgWSeEGX394s2KEVtolqEnz87dBaDfrOb0mtqExCnihbL696w1Gz99/sbQEdvo1m8dL95+oW/r8mye0Y7YMaL+cZ2lMlFEaJJ3QtiAi1vPMa/XKlJmSMLsPX0ZtrgtKTMkNngfY3LR2EzUh39AEOtdLvLg8mucvLS0rVuUzTPa0bd1eRLF+3OViLnzUNg0yTohbNirB26MrjuNfhXHs3HOIQBixI5GoXLZjd7THHmo7/2hRqNjx97rNGu/lJXrz1IsbzpyZkym19cw9jxR/MMWOp6YTFGUZAS3iWmtqup//q1RFKUT0AkgqhLDzFcnxK/ZO9hT4K+c1Oj0F6vGbOXElgt8ePU5uB3kF3fsHeyp0aoUAJeO3SMo8NdvcunL2DfYwDxveH9x98YJcHSwx8fTn8ZV89OoSn6OX3rEpv1Xuff0/W/XSgcUERmElnXfP0fyTkR4bx6/Z/XYbQA4RnGk/5wWOEVxYPfK02xbcsxs80vBfCN4nLy0bN96hdhtotKzeRna1CnCzqO3cDl0nU8ev38KKG97C1sjWSeE4YJH0wWPcf363p0aHf7ir/qFOLD+HJvmH8Hj4+/fgP4Ta83Guw/c6DtwEwXypiFfkXR89Qn+8xfNnZabD9/iF/DrB5WWyEQhfkeeYwoR/rQaLXuXH8N141nq96xCgz7VSZ8jBR1KT0BVDX8GZ41vfANs3nCBKDo76lbOQ+3yudh38i5r91z6bQcUyUNhLnJvJ4Tl3Tn3owERNXpUpM2gGty++ISVk/Zw/+oLo/a0pjz08wtk1Yaz7Np/Ha+vfhDLjoaV85EySVzW7bn822emppwnCtvoePIWSPXTxym//5peFEWJDewDhqmqeuFXn6Oq6lJVVQuqqlrQSYli0sUKEVZ0Wh2ndlzi7rlHNOpTnb/ntaVW5wrkK5eD7EUyMnRNdyo2KozvVz92O5/8sS56zCjY2RnfKcPaO6AEabRMmLGPZu2XsnXXFYrlSceKcc2pVDxrqGtD3mazRAV/WHRAERFauGcdSN6JyCcoIIheZUZzbs9VGnQtz+oLo+gyph4JksYxaj9LvOWtb0beuf+W/oM207nPGi7eekmzGgVpUDmfXmulA4owE8k6ISxsyaANtM3Vn8NrT1GtRQmcz4ygSMWcJu1prR1Qrt54yfIlJ3Dy0hI/TnSm9a/DjrkdaV+/GLFj/r4LiqXuF0WEIs8xhbAh/j4BrJ+8izY5+jG51QJUVcUpqiMdhtcmYbK4Bu9nTW98A3z+4s3MBYdp3nEZB47coXqZHLSvVyzUdZKHIhRybydEBOMyaRvzeq0iRbrEzNzZh5ErOpAmi35dQn7FmvLQw9MXnU7FyUtLkujRqVchDy6z2ofaDcyUiQqRmS10PLkMZFIUJR3B4dUEaKbPQkVRnIAdwBpVVbeG3yUKEfZUVeX2mYcMrjGF6h3K07B3NfKVy4HyU/3B4+sv2LvsKO9vPwPALn48Oo2qR45C6dg49zAndl1DpzXuG+PPoWDOt9hAv8r99x+/smjFCVZtOEfVCjk5cz3470G5wplInjgOu4/f5ptPwG/Xm1rBDzb8prdGZ3RxkQg3knVChBO3px+Y0m4xG6fupmHvalRrUYIjLpf48t4LO3s7o3LS1Le8Ifze9H7w6D3jxu9iWZI4+PkFggJF86SlcZX8rN1zmWv3Xv92bYTrgCJ5Z20k64SwAp/dPJj79ypcZu2n6cBaPLrxEoBkaRPi9cX7R4cUQ5kzGw1949v71Tf+HrSRJk2L0qF+cZpVL8iuY7dYu/sSHr95sGkzb3xL1lkjyTshbJDX5294fQ7+fpqlTDZqtS1NrTal2bfuLFsWHMHjk2Hfa63pjW+A9x+8mLngMGs2nfsxwj1z2sS0rFmY1bsu8uTVp1+uC4vnpyaTrLNGknVCRDCaoOBOYEc2nKFut8o06leD1gOrM7b9cpP2tbY8XLrqFLsP3KRlo6LUrZCb2uVzMWftCba73vztGrN1QIkgeWf1hSeqqmoURekBHALsAWdVVe8qijIWuKKq6m5FUQoRHFbxgJqKooxRVTUH0AgoDSRQFKXN9y3bqKp6w+x/ECGMpAnSsmvREXYtOkKhyrnJXiQTgX6B+Hz149yeq3x28/jxuTp3Dy7svEim3KkYMKclzftUYdO8IxzbfhmtxvjKPHO2UQbDgsLPL5Dte64FrwMK50pDnb/y0K5eMfafusuWg9d5/d7jt+stNYbHKgpQhNWQrBMi/L164MaMLstZPnwzXp+/YRc/Hr2nNSFeoti4LHTl1vknBu9pqSJNfR8sQnA2xsWRTGkSs2B4I+49fcfa3Zc5deUJut+0kLZkAYqIuCTrhLAu755/ZGbX4IeIdvHj0XdGM9JkTorLomPsXnmKAL9Ao/Y15whXQ0fwjBi1nXRpEtK0fmHq/pWHzQeC7yPt7RS0OuvJRGHbJO+EsH03T96nXe4BNBtcm5qtS1GlaVH2rDrNmun70QQZNurcnAduEHomfv4SHFBOQIa4cSmWJy0VimXh1JUnOO84z8PnH3+5TsYOiJ9J1gkRcQX4BrJp+h72OR8navTgTkPJ0iakZutSbJ53GC93w0eYQ3AehvezU33vD99/8GLavEOs3XyeFo2L8fzNFwDixY6Gg729jOAxkWLMzMKILI59ArVo1OqWvgwhTKIoCkWr5aP58Ppkyp2KTfOPsHrK3jDb35iHiCEMLUIBw2aXAmRMn5gGtQvwV5lsONjbs2HfFRZsPKXXWlNCw5SHivqsfTi271VVVQsa/1WCxXFKohZP0kSvzz34Zm6YfE1hfSTvhICGfarRoHd14iaMxcMbL3FZeJTzh26h+83hkz6MzcjwzkcnR3sq/5WTJg0LkzJpPC7fecnfE/V7ucjYbDQmF+/MDJusA8k7IVknhCEy5k1D67FNKPxXDjw+fWPz/CPsX3+WoACNSfua897RkFyMGTMK3t4BBMaxZ8bAuvgHBLF61yUevfj1gVuIsMrEsMo7yTohWSdE+EqeIQkthtYhafpk9K8/B8DozplgXC6G571izJhRqF+zAA3qFCB2zGgcPveAUfP3hbpOnzy8vlSyToQdyTshLKdK6zL8Pa8tAb6BbFtyjB3LT+D3h2kDoTHXs1NDzxW79axA3b9ys/v4bdbsvsQn9z+/JRcQyy7Msg4iTt5ZfccTIYThVFXl/L5rnN93jcKV8/DybXB1fea8qcmUKxWHN18gKNCwCv2fmfMtNjB8DM+TZx+ZPOsAS1edolbVvDz88BmAWDGiULpgRo6ce0Dgb95Q+HlmW4QZNSCEEOKXXGbtZ9eiI1RsXpIGfWswfGk71s44wIbZB43e05xdwgx50zswSMuegzfZd/gWZYpnRhPDHgBHB3vq/JWbfafu4vubt9uNzUbJNiGEsB1PbrxkRK0pZC+SkTbjm9JlTD10Wh17Vp82aV9r7YDi7R38oDTKVy1PH36gbs38lC+ShQu3XrBm10Wu33/zy3WSiUIIEbm4Pf3A1PZLcHC0h1ixiZswFnP29mPvmjNGdQgzRwcU0D8Tvb0DWL3xHC47r1CvZn4Cnf75vcxpE/+2IFPe+hZCiMjj4OqT3L3wmDbjm9KyfzVqtinF2hkH2L/urFH7mevZqaEjWrdvvkQ01Y46FXNTq1yuUAtQfr43FP+QwhMhIrhLh/6ZTVa6Zh3qdypHk56VcFnoysFNFwj0D/rD6j8zdwEKGBYW7h4+rNoQHH5OQPnymRncoRLdmpRmh+tNtrvewN3L97frLT1qQB5KCiFE+Av0D2LfiuMcWHmCknUK8fBRcLFijkLpyV4oPfvWnsH3m7/B+1qiAAVCz0edTuX4mYfB64CCZdLRt3V5OjYozg7Xm2w+eC3Ms1EO24QQwnbcu/iEgRXHkadMNh4+Dm45XLJaHuImisWRLZci3AgeVYXla06zcetFalXLS8O6hVg4ojETlx5iz4k7f1xrSiYKIYSwLZogLbh74BjLnuf33Wg3pCZ1O5Rly4Ij7FtneIcwaytA8fULZN2WC8FrgIJlMjJjYD0u3X7Jiu3nufXw7S/XSQGKEEJEDq8fujGu4QyyFEhPu0nBY1pDKIqCMdNVrK0A5f3Hr8yYf5j1Wy7QvFFR6lTMTfSoToxbbPwLipGR/B+BEJHI0j4rGVhtEu9efqbruAasPDOCyk2Kmryv4vHtR0gYyv6r348fhnDyCvq/gzZ9HNhxg95DNnHv6Tva1y/GjrkdGd65Mvb2f/5WGOWbzqjqRUfvf34Yw5S1QgghDKPTqZzafokPd56hc/egQNmstBtSk7UXx9BuaC3iJ4lt1L4hGWloThqTjWB4Pl45+YROvdZw6fZLWtQszPY5HRnYrgJRnP5cn25MNpqai0IIIczn5sn7+Lt9ROfuQZGKOek+viFrLo6mZf9qxE1o/PicsLh31JchmejjG8jGrZdo0nYJM+Yf5vilxwAUzZOWSsWzYm+n/HatsfeLQgghbM+Hl58ZWXsKvcuN5cUDNzqPrsfyk8OJFiOKUfuZ617R0PvEOxdesnDFcTKmTsiSUU2YN6wh+bKl/O3nh2Sh5KEQQkRsD68+Y1Cl8SzpsxKAXEUzsOjIIMrWKYDdH+6Z/sTYe8Twuj8MKUBp3nEZK51PAZApTSL6t/2LxPGNvxeOLKTwRIhI5ubJ+/T/ayz9K0/g5aP3Px4a2jvYkThFPJP2NvZwLYQ5bpyu33rFsBHbaN5xGXsP3iJ6NCe03+eypkuR4I9rTbmBkgIUIYSwLauGrKd7iRFcPHidep3KsersKNoMqmHSnqY8VAzPfHz45D1jx+2iReflHDp6h4xpEhEQGPzGXvw40f+41tTiTCGEENZvWuv59PlrHHcuPqVJz4qsPj+Khl3/MmlPSxSg6JOLgYEadh+4QeA7X5y8tFQvnZMxPaqzcXpbqpfJ8ceXFuTATQghIo/7l54wuMoEBladxMGN5/DzCR7hlqtoBuxCecHtV6ytAMU/IIjN2y/TpO0S5i87Rtrk8RnVrVqoL++B5KEQQkQGmiAtOncPFN/grsmD5rViyfGhVGhYGHsH40oPrLEA5eOnbzh5acmdPAm1yuVi6+z2UoASChm1I0QkdfvMQwZXmYCiKCjx4lKscm6GLGzN5aP32Lf2LFdP3kenM7w9Vghzj+ExZMwAwBs3D+Ysdg1eC8TPEJc1k1vx9NUnNuy7wtELD9H+5s9vShtJGTcghBC248mNl0xus4hVo7dS/++qeH4J/iZuZ29HxlwpeXTjlVH7WmIMjz7Z+NbNgxnzD2Nnp+CkU3FKFp0tM9px79l71u6+xOU7v//zWmI8nRBCCPO5d+ExY+pPJ0XGpNTrURm3F8Gj6WLGjU7aLMm4c/GpUfv+/GDR2nIRYPyEXZwomomWzYszvHMV2tcrxvwNpzh28dFv18jYASGEiDxunrrPzVP3AUhVMAuTN/fg7bNPrJt5gNN7bxg8esCYXAzPPAwI0OCy8wq79t8gZfJ4aLU6HB3sGd29GruO3eLS7Zd6f00hhBARz40T9+hcYDAlahWg2bB69JvZnOotS9Cn1iyj97S2ETwAew7e5NLV5zRvVJRalXJRq1wuNh+4xoWNBl1ipCB3wUJEcqqqonP34N7RG2yatodMeVIzdk1nlp8aTsOuf+HgaG/S/rYyhufby2/MmH+IqFEcGdOjOi6zOtC4Sn6iRvl9fZ6lOqAIIYQwr/cvPrGg7xq2T92Bzt2DUjXyMmdPP6a69KRIhRwoinW3kgTDsjGk8NTOPYg1G8+TNkUC5g5tyMoJLfirSOZwGTcguSiEELbh7ZP3zOu9mtPrT6Bz96BKk6JM2/o3s/f0pVSNvEa95R3CGnNRVeH0+cd06rGaQaO38sXTh6hRgh9MRnF0+ONoOnnjWwghIpfXVx4yrtk8tFodQxa2YcGhgcHZaKbRA8Y8RzWkI9izF59w8tKSLkYscmRMxpwhDVg6pilFc6fV++sJIYSIeFRV5cyuK3QrPJSRDWay+/t4Gjt7Oyo1LoLjH87Y/sTaOqB8+PSVmQsO06zDUvYfuvWjW7T4f1J4IoQA4LObB6vHbqNl5l5MbLWAT24eVGxUBE2QFgCnqPq9EfY71j6GJ0ijZf/h27TutJwhY7fz4ctXejQvQ8xowTNa9XmgKAdtQggROZzfcobFA9eTJFUCRq/sxGLXwZSvV9DkB4rW1FoZgtsrb9p+iaZtljB1zkGiRXFkfK+apEkeP9S1ctgmhBCRw86Zu5n790pixonO0EVtWXJ0MOXqFjRpT1MfMIbHQ0aAC5ef0b33Oo7uvQVAvYp52DWvE12blPpjq2XJRCGEiDzO7blK14KDmdRmIY5O9vSd2ZzY8U1r72iOe0VD8vDFqy80b7eU6fMOkTBuTGYNrs+aSS2JZ0DHFSGEEBHTxQM3OOrsis7dg/ylstBnejOcT4+gessSRr/kbm0FKB8/fWPmwiOscT5t8DVFBjJqRwjxfzRBWk5uu8jJbReJFjMqOm9/YqRKyvJTw7l87B4uC115+/yTSV/DUmN49GmZpapw7uITzl18Qork8fjq9hXi2DOtfx2iOjmy8+hNjl54REDQr6sZZdSAEEJEfP4+AexYcIjdS1wpVbcQTQbVoXGPihzfcdXkvY3JyJ9vogxtJwmh52OQRsu+w7c44HqbXNlT8uzNFwCGdqyEYqfgcug6j158/OXanw/aZOSAEEJEPIH+QexbcZwDK09QvGYBmg+vT7m6BTi+44rJe5vzvtHQe0YnLy33rr3mWubkNK9RkGbVC3Ly8mO2HLzGrUduv1wnI3iEECJy0OlUTrhc4NS2i6TNkRLPz8EP/frNas6NM484vvMqOq3hBYmm3CuGdR4GabTsOXiTA663qVg2O/mLpMfj+9cqnCsND55/0PsahRBCREyXtp9j4GcPWo1pRI+JjWjY9S82zDmEq8ulH92WDWGNI3jEf0nhiRDit/y8/QGw8/7Gqa0XqNquLBUbFebMvptsWXCEp3ffmrR/WDxIhPA5ZAN46+bxfZ2W82efULtmPkZ0rUqvVuU4cOou211v8uqdxy/XmlqAAuFYhKLVoHP/9XULIYTQn1aj5YTLBU64XCB+0rioqkK0GFGYtasPR1wusn/dOfx8Aoza21w3U6D/DZVOp3Lzzmucvn/s7xVA9Sq5qVEmJzcfvsXl0HVOXH6M9jcPUc1+4CZ5J4QQZqPTBbdXPrv7KjHjRgfFiSSp4jPKuSMuC1w5ufuaUQ8XwXoLUO4/fMfoMTtJmjg2dWrkp3qV3MSJFY2eE1yCr1sJLlL5tzDtfiJZJ4QQVkunU3l2+zUAseLHJF225FRoUJimvSqzad5hjm2/YvMFKBqNjgOudzjgegcnQEkUhYm9amJnZ0f0ZT30vr4/kqwTQgibdfPUffqVG0OBCrloPaYR9TuXx9Xlkkl7/tz9JDxf3DN7AUoEyTt5zUIIEapvHj4sGrCOVtn6smXGXgqUycr8gwNJnSlJmOxvyggeCP9RAwDbdl+lVcfl9Bq8kctXnlOvYl6K500HgIO9HY4Ov24TJmN4hBAicnB/74nO3YOYioYvH77SYXgdVl8YRfM+VYIP4IxkrnaSYHg2zl92jIatFjF/2TESxInO+L9r0LZO0VDXycgBIYSIuFRV5ZuHT3Am2ulAhYHzWrHk2BDK1Slg9Fg6MO2+0dg2y/rk4vuPX1nsfIJGrRYxbcYBABLFi8n2OR1pV7eojB4QQgjBN3dvuhcZxujGs/Hz9qffzOYsPzmM1JmTGr2nOUfw6HufqH4KoHv/9axaf9ag6xJCCBGxXXW9zd8lRjCw8gR0OpVoMaIwdWtPStXIi6KY/x7RkDw09HlpZCcdT4QQevP8+JWVo7eyZdZ+itXIz6vHwW0T67Qvw+unH7h64oFJ+xtbqRjCHF1Qbtx+zY3br4kbJzqBgRpwgkrFs9KjeRn2nrjDrmO3ePvR65drw6ILihBCCOv24dVnhlSdQJaC6WkytB4t+lalXqdydC43kc/vf50P+jC1mt+YDigQejZ6+wTgsvMKW3ddoUjB9Dz47A5AoZypqVoqO1sOXv9tm2UZwyOEEBHb4+vP6VpoCCVqFaDFiAYMnNeKht0r0LPqNLQa4wsQLdU5M7RM9PMPws/fCycgdkx7nr35TMeGJWhdpwiHzz1gy8FrPH5p2thaIYQQtktVVc7vvcb5vdcoUjUvNbtV4f2r4DGmKdIl4sMbdzRBWoP3NUcHFNA/D5+//Mzzl5/13lcIIUTk4fEh+NlogsTJiRMvJkMXteXZvbesmb6fi0fuGL2vOTpHG3qWGFlJ4YkQwmA+Xr64rj8DgL2DPTValyJFukQ8uf2azfOPcO7gLaPbKIcw5WEihO8NFICnl2/wGj9we/yZmw/e0rR6QVrWKszFWy/Y7nqTU1ee/HJtRD1oUxQlJTAWqAIkAN4BO4Exqqrafo8wIYQwwMMrzxhTbzppsqegePX8P4pOytTOz4NrL/jw2t3ovU15sAjhk42qChcuPwteA6SOFZvSBTNRtVQObj9yw+XQdY5feoQmlDE81k6yTgghDKOq/4zgKVGrAClzp/tRdFKubkEuH7+Ht6evUXuH1YsL4TGG5/Vbd4YM20rqlPGpVzM/lSvkpErJ7NTusQR3L+P+vOYiWSeEEOHv4oEbXDxwAwCHRAkYt7YLdnYKm+cf4YjLJZMKUED/XAzv56fWTPJOCCEs59UDNzoXGETZRsVoMbw+o5078vDGS4Y2XYCvt3Fjy8F8o8ttpQOKJbJOCk+EECbRarR0yjeQ8k1L0HhALYYtacfrJx+YPWAj9648N3l/SxaggH43UfcevmPUmB0kiB+T6pVyUaNaXtrXK/aj8MTRwZ4gza9vGI3tgmJtFEXJAJwDEgO7gAdAYaAXUEVRlBKqqn6x4CUKIYRFvLz3lpf33gIQLWZU/p7UmCjRHDm+8ypbFrjy+smvu4How1w3U2B4Nu45eJOjJ+9TtUJO6tTOz9ie1bn7JD8dRm4w6FqtiWSdEEIYL6QAhV1XAEhdKAsD57YkwD+QU3uus2/NWR7eeGn0/mHRBSU8ClBevfkfe/cZHlXRh2H8nvSQAKH33pSiqCgKYkOaIPbX3kXFrti7Yu8dFXsXpYiIAioKqKgISBOkQ+iQ3tu8H3ajAZNt2Zo8P6+9kuzOOWeSyD6Zc/5nJo3nxn3LG+/N5cBebSKh6ERZJyISZCW79vDSDe9w/j2nc93jZ3HWdUOY8PIsZn46n+Ii7wtQwPtcDOSMYOFIeSciEnplZZbvP/mZHz6bz/HnHkmvY3v9U3TSqmNTtqzb6fO+g3nONFyFKutUeCIi1VZSXMrM9+bw7QdzOfLkQznjlpGk78oCoFOPViTUiWe58y5oX/mrAAUCV8W/Jy2H9z75hQ8nzKdRo2QAkhLjmPDMpcxbuJbPZixizabKp1auAQUor+AIsOustS+WP2mMeQa4EXgYuDJEfRMRCQv5OQVcdtBtnHb9MIZfeizHndqHn79ZyluPTmXbBt+nIq7uYAoCk415+UVM/HIhk6Yt5LCDOxDbKMFx3OgobrzgWKbPWc6Ktdu96nOIKetERPxk0++rGH3E3Qy/5FiOO7s/g87oy9plqTx+7XshKcqE6t3l5snSdD/NX0Oc170KOmWdiEgILJi5hAUzl3DI8b04757TueaR/5G+K5ufv1lSrf0GYxmeCF16QHknIhImykrLmPneHGa+NweAZj07Mm7mbaSu38kXb/7I7Cl/UFTg2wwjtbwAJSRZF7FXOEUk/JSVWeZM+o1rj7ibLYvWUJaWzumjB/LUpOt5a949nHvjUFq0a1StY5j07H8evorOyt/rYpsn4jKLPZ4+q7TMsnNXNnGZpSQXGOb9/DeD++3H+49dwCv3nMmxh3UhOspUum18dtk/j0jhrJwcDGwAXt7n5fuAXOB8Y0xSkLsmIhJ29mxL5/XbP+L8/W7k4ye+pNfhnRzr1ADxidW7HFWdjAxkNloLv/6xnnkz/yIus5T9UhowtP/+vDn2XN548ByG9N+f2Jhor/scTMo6ERH/W7dkEy/e8C7ndLqO5699m/y8QnZvywCg1+Gd6bB/S5/37Y9M9CYXvRkvhitlnYhI6P3x7VJuPPo+xgx+mF9mLAVgxIVHMvKiAcTG+34PsS+ZGOjzp6GivBMRCW9pqzfz/HVvUVZaxg1Pns17v97PhbcOp35D39+Wgzk2DAehzDoVnohIQD076lWeuOxVtm3cwzk3DOGtefdy64sX+GXf1S1Cqc4JRU8HUekZeTz90kxOv2Acr7w5m2aN6vLIDSNp38p9AU4EFaAc6/w401q7V4ettdnAT0Ad4PBgd0xEJFxl7cnhvbETObfTdWxZ5Fia7Y6XL+SpiddzxJBeRMdU78/0UFxs8zQbV6/byWkXjOO5cbNIrhPP/VefwOQXRtG8cT2v+xtEyjoRkQDJzylg+luzGXPsA+Ru3k5ZWjqX3jWSV2bextOTb2DgaYcSl+D7HdT+GDN6KhIuuLmgrBMRCRPLflpF6Z40ytLSOWhAN0aPPZ23f7qXky45ulo3LFSnAMXbPAxjyjsRkTBWXFjMrA/mcdVhd3LL0EdY/ts6TrviOOLrxAMQHyFjwxALWdap8EREAqogt5DvPv6ZO4Y9zPndbuTNez5l2a9rAYiOieLGp87m0OO6h+wCW7lAV/Fn5xTw6aTfOffS17n21o9Yu9mxpMJdlw/hriuG0LV90yq3jYAClG7Oj39X8fpq58euQeiLiEhEKS4qAaAsLZ3fvvqDxi1TuPeNy3jv1we45M6RtOrQpFr7D/aAytNszM8vYvK0RVx4+RuMuXsCPy5YzfbdWT71M0iUdSIiQXTXiMd49dYPqZtSh5ufO48Pfn+A4ef3r9Y+gzkzmLdFmWFCWSciEoYeOPUpbhn6CKlrd3LlA6fy/m8PcOzJh1Rrn8GcKTMMKe9ERCLEkrkreeC0pzi36/VsX+K4rnjnqxfz1KTrOXJ4b6KifbuuGOxZo0MgZFmnwhMRCZrdW9OZ8MxXTHthGmVp6bRslEDf43vy4LtXMObZ8/xyDH/NguINb04qlpVZlixPJS6zlLjMUvIzCjiub1fefeR8Dunexqc+h4H6zo+ZVbxe/nxK4LsiIhK5po3/jou638S9pz/DykUbOHXUMRw5vLdf9h2uSw5YCwsWbeD5Z2cSl1nqdd+CSFknIhJE2em5TH55BpcdeAu3DH2EP2YtJTsjz2/7D8el6cKAsk5EJEwtmbuSWweN5caBY/lj1hJ2pKb5Zb++jhMj5KJbVZR3IiIRJnOXI6fK0tL5ffpCGjSpy12vXsyld42s9r6rMzYMYyHLOt8XBqyhssrScmbmvb8q1P3wUGNgd6g74QH1078ipZ/grq9/wPjGjwXu6KketwzZz3TOdK+ad3PfxL2ssrQZM/Peb+xh8wRjzIIKX79urX3dH/2Q0IqgvIuU9zz10/8ipa9+7+c3E2HsRH/uEajYT8/zMRT8knWgvBNlXYBESl/VT/8KaT9nzoCnZnjU1Pt+hi4TNbYTv4igrAO95/mb+ul/Yd/Xmd/Dc9/fG5h++j8TlXXiNxGUd2H/PuKkfvpfpPQ14vs585n3ueaZABzRtxzUecx9qPDkv1ZZa/uEuhOeMMYsiIS+qp/+FSn9hMjpayT10x/7sdYO9cd+Kiivjqxfxevlz2f4+bhSPRGRd5H071P99K9I6av66V/+yjrwe94p6yKTss7PIqWv6qd/qZ/+F6ZjO2VdZIqIrIPI+TeqfvpXpPQTIqevkdRPf+xH5zHFKSLyLpL+faqf/hUpfVU//UvnMf9LS+2IiEh1lVebV7UeXBfnx6rWkxMREQl3yjoREanplHUiIlIbKO9ERKSmC1nWqfBERESqa7bz42BjzF65YoypC/QH8oD5we6YiIiInyjrRESkplPWiYhIbaC8ExGRmi5kWafCk/8KizWQPBQpfVU//StS+gmR01f1sxqstWuBmUB74Op9Xn4ASALet9bmBrlr4lpY/v9UCfXTvyKlnxA5fVU//Sss+6msi1hh+f9TJSKlnxA5fVU//Uv99L+w66uyLmKF3f9LLkRKX9VP/4qUfkLk9FX9rAblXcQKy/+fKqF++lek9BMip6/qp3+FZT9DmXXGWuvvfYqISC1jjOkE/Aw0Bb4A/gL6AsfimK6rn7V2T+h6KCIiUj3KOhERqemUdSIiUhso70REpKYLVdap8ERERPzCGNMGeBAYCjQCtgGTgQestemh7JuIiIg/KOtERKSmU9aJiEhtoLwTEZGaLhRZp6V2KjDGDDXGrDLGrDHG3B7q/lTFGPOWMWanMWZZqPtSFWNMG2PMbGPMCmPMcmPM9aHuU1WMMQnGmN+MMX86+/pAqPvkijEm2hizyBgzLdR9qYoxZoMxZqkxZrExZkGo+1MVY0yKMeZzY8xKY8xfxpgjQt2nyhhjujl/luWPLGPMDaHu176stZuttRdba1tYa+Oste2stTdosBZelHX+FSl5p6wLDOWd/yjrxN8iIe+Udf6lrAsMZZ3/KOvE3yIh6yAy8i5Ssg6Ud4GgrPMv5Z34k7LOvyIl75R1/hcpWQeRkXfKuqppxhMnY0w0jqllBgGpwO/A2dbaFSHtWCWMMUcBOcB71tqeoe5PZYwxLYAW1tqFxpi6wB/AyWH68zRAkrU2xxgTC8wDrrfWzg9x1ypljLkJ6APUs9aOCHV/KmOM2QD0sdbuDnVfXDHGvAvMtda+YYyJA+pYazNC3C2XnO9VW4C+1tqNoe6PRBZlnf9FSt4p6wJDeRcYyjqprkjJO2WdfynrAkNZFxjKOqmuSMk6iIy8i5SsA+VdICjrAkd5J9WhrPO/SMk7ZZ3/RUrWQeTlnbJub5rx5F+HAWusteustUXAJ8BJIe5Tpay1c4C0UPfDFWvtNmvtQufn2TjWjmoV2l5VzjrkOL+MdT7CsiLLGNMaGA68Eeq+RDpjTH3gKOBNAGttUTiHVwUDgbUKMPGRss7PIiXvlHW1V4TmnbJOqisi8k5Z51/KutpLWSe1VERkHURG3kVK1oHyrraK0KwD5Z1Uj7LOzyIl75R1tVeE5p2yrgIVnvyrFbC5wtephOEbbiQyxrQHDgJ+DXFXquScCmsxsBOYZa0N174+B9wKlIW4H+5YYKYx5g9jzOWh7kwVOgC7gLed06C9YYxJCnWnPHAW8HGoOyERS1kXQOGed8q6gFDeBYayTqpLeRcgyjq/eQ5lnT8p66Q2UtYFSLhnHSjvAkBZFzjKO6kOZV0AhXveKev8LhKyDiIz75R1FajwRALKGJMMTARusNZmhbo/VbHWllprewOtgcOMMWE3HZoxZgSw01r7R6j74oEjrbUHA8OAq51TzYWbGOBgYJy19iAgFwjbdSIBnNOKjQQ+C3VfRGRvkZB3yrqAUN75mbJOJHwp6/xDWRcQyjoR8YtIyDpQ3gWAsi4AlHci4SsS8k5Z53eRkHUQYXmnrPsvFZ78awvQpsLXrZ3PiY+ca69NBD601k4KdX884ZyyaTYwNMRdqUx/YKRzLbZPgOOMMR+EtkuVs9ZucX7cCUzGMS1euEkFUitUyn6OI9DC2TBgobV2R6g7IhFLWRcAkZZ3yjr/Ud4FhLJO/EF552fKOr9S1vmfsk5qI2Wdn0Va1oHyzl+UdQGjvJPqUtYFQKTlnbLOPyIk6yDy8k5Ztw8Vnvzrd6CLMaaDs0LpLGBqiPsUsYwxBscaXH9Za58JdX9cMcY0McakOD9PBAYBK0PaqUpYa++w1ra21rbH8f/n99ba80Lcrf8wxiQZY+qWfw4MBpaFtlf/Za3dDmw2xnRzPjUQWBHCLnnibDRll1SPss7PIiXvlHX+p7wLGGWd+IPyzo+Udf6lrPM/ZZ3UUso6P4qUrAPlnb8p6wJKeSfVpazzs0jJO2Wdf0VK1kFE5p2ybh8xoe5AuLDWlhhjrgFmANHAW9ba5SHuVqWMMR8DxwCNjTGpwH3W2jdD26v/6A+cDyx1rsMGcKe1dnroulSlFsC7xphoHMVYE6y100Lcp0jWDJjs+BuGGOAja+03oe1Sla4FPnT+4boOuDjE/amS8w+CQcAVoe6LRC5lXUBESt4p6/xPeednyjrxl0jJO2Wd3ynr/E9Z52fKOvGXSMk6iJi8i5SsA+WdvynrAkB5J/6grAuISMk7ZZ1/RVLWQYTknbKucsZaG+o+iIiIiIiIiIiIiIiIiIiIiEgE0lI7IiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IiIiIiIiIiIiIiIiIiIiIiE9UeCIiIiIiIiIiIiIiIiIiIiIiPlHhiYiIiIiIiIiIiIiIiIiIiIj4RIUnIiIiIiIiIiIiIiIiIiIiIuITFZ6IeMEYs8EYY40xF4W6LyIiIoGgrBMRkdpAeSciIjWdsk5ERGo6ZZ1IeFHhSS3nfEP29XFRkPqYYoy53/lICcYxQ8EYU8cYM8wYc7cxZpIxZmOFn/X9Xu6ruTFmrDHmD2NMmjEm37m/b4wxtxtjYqvRz2bGmKeNMauc+00zxsw1xlxmjDFu+vSmMWa7MabIGPO383uNcbFNY2PMbmNMqjGmnq99FpHaTVkXPqqTdcaY9l7+7t6uRj87GWNeM8asN8YUGGN2GWNmGGNOc7NdF2PMBGPMHud2S40xo91s083ZdpGrTBQRcUd5Fz78NbYzxpxujPnSGLPVOYbKdY7DxhtjevuhnxrbiUhEUdaFDz9m3Uhn1pVnyjZjzBfGmGF+6qfGdiISUZR14cMY08gYc7Ex5gNjzArneKzQOaaZYow5xcP91HX+rJYaY3KMMZnGmN+NMWOMMXF+6KfGdRI29AeQ7Kji+WQgyU2bfP93p1IpwH3Oz98BMoJ03GA7DJhe3Z0YY84EXgfK3/QLgCKgrfMxBHgVH36OxphDgBlAI+dTOUBd4Ejn43RjzEhrbdE+2zUAfgI6Op/KBboAY4EDgTOqOOTTzmOdaq3N8ra/IiJOyrrwUZ2sK6Xq31O5BKC+8/PffTmIMeYE4DOgjvOpLKAhMBgYbBwFLZdaa+0+23UA5jvbWhz/7/QEXjHGdLTW3lLFIV8DYoFR1toSX/osIuKkvAsf1RrbGWPicWTRiRWezgHigK7OxyXGmJuttc/6eAyN7UQkEinrwkd1sy4aeBc41/mUxfGzagKMBEYaY1601l5XjWNobCcikUhZFz62s/d19AKgGGjlfJxkjPkaON1am1fZDowx7YAfgPbOp/KAeKCP83GuMWagtTbdlw5qXCfhRjOe1HLW2uaVPYCn3LWx1n4awq7XVOnAd8CTwNk4gs1jxpgzgI9wFJ28DvSw1iZaa+s7nzsKeBZHOHrFGFMfmIYjVFYCh1pr6+L4Y+ca5z6HAM9VsvmNOALsT6CdtTYZOA5HCJ5ujDmmkuMdB1wAfGGtnextf0VEyinrwo5PWWet3ezi91T+O33f2TwfRx56xXmCcQKOE5M/Ad2cGVofeNDZ7GKgshOND+A4MfktjpOlycCZQBlwkzGm474bGGMuAY4GXrTWLvC2vyIiFSnvwk51xnZ38m/RyStAa+fYKxHHycl5OM7nPO080egVje1EJFIp68JOdbLuIf4tOnkeaGKtbYhj7HUzUAJca4zxqfBEYzsRiVTKurASA/wGXAV0cl5rSwY6AG862wzDUXj4H86ZQ77EUXSyDRhkrU3CkU1nAdnAQcAHvnRO4zoJRyo8EQkfc621Da21x1trb7XWfgIUerqxMaYFjoCLAsZYa6+w1q4of91am22tnWutvclam+tD/24GmuO4mHdC+SDKWltkrX2ZfytcLzfGdN1n20HOj3dZazc5t5sNjHc+P3if7yUBx6ws2TgCUkREaoZqZZ0rzuwoP3E50Vqb4cNuHsQxONsOjLDW/g1grc2x1t6Ho6gT4C7nnQEVlWfdDdbaPdZhAvAFjmw+fp/+NsFxgnYzcLcPfRURkfBV3by7wPnxR2vt1dbaLQDW2jJr7R/ACBwnBA1wug/909hORESqy+esM8Y0xnHBC2CKtfYGa+0eAGttrrX2aRx3VAM84ONU/hrbiYhIdR1nre1rrR1nrV1X/qS1doO19jL+LTg5zxjTppLtLwR6OT8/zVr7rXP7MmeR0BXO104wxgz0oX8a10nYUeGJ+MwY08QY85Bz3cpM5xqW65xrgvVwsV1rY8yzxpjlFdZE22qM+cP5/KEV2v4ArK+w+fp91qz7oZL9xxljrjLGzHauNVbkXKPsC+NmfVBjTKJzDbMVzrXQdhpjpvv4pu8Va21pNXdxHdAAWIRjVhN/Kz/5+Ym1dn0lr7+I4+RnNP9e+CvX2Plx7T7Pr3Z+bLLP83fjmNbrbmttqm/dFRGpPmWdf/kh61w5FUcOArzh7cbGmCSgfJ3vcVUUrjzq/FgPOHmf17zNumdx3EV3jbU2x9v+ioj4k/LOv/yQdy2cHyu9Y9pamwn87fwy2Yf9a2wnIrWOss6/qpl1A3EsMwCOgo3KPOH8mMJ/x14uaWwnIrWVss6/nIUYrrxZ4fM+lbx+ofPjbGvtL5W8/gn//iwvqOR1dzSuk7CjwhPxiTHmeBwnuu4CeuOY8rcExxRTlwALjTH/eaM0xhwILAFuALrjGGTk4qjKO9j5/NUVNkkDdlf4ejeO9evKH2n77L8dsBB4GTgGxx/9eUAzHOuDTjfGjKvie2qIY+rFscD+OKbRisUxVdYsY8xoNz+T+yuEa3tXbQOk/Of9gbV7r01aXcaYbkBb55dfV9bGObCa6/xy8D4v73F+7LTP852dH3dVOFYP4Fbgd+AlH7ssIlJtyrpKtw911rlyqfPjamvtjz5sfySO3zFUnXUbgL+cX1Yn6wbhGPBNstZO9aGvIiJ+o7yrdPtQ51353XSVLqNjHFMql9+x5tV0/hrbiUhtpKyrdPtQZl27Cp+vqKyBtTYN2On8ct8sckdjOxGpdZR1lW4f6KwrqPB59D7HrgP0d35ZVRZZ4Bvnl15lncZ1Eq5UeCJeM8b0AqbiqDgfjyOMytc2a4djDeo44E1jzL5Vfk/juBt5IXAEEOtcvzMBx4mzm4Hl5Y2ttacCh1bY/tB91qw7tUK/knC8SfcAfsARYonW2hRnX2/CUd13pTHm+kq+tTdwrKdWCFwJ1LXWNsCx/toUnOuNevRDCjLjWLe0pfPLP4wxvYwxHxljtjmrU1ONMZ8aY/q72o8LPSt8vsxFu/LXuu/z/Cznx4eMc8oxY8xRwKiKrxtjDI7pyQxwubW2zMf+iohUi7IushjHGtvHOr9801VbF7zNun3vFCnPumeNMQ2Nw2nASTjWAv/O2ddEHFNTZgHX+thXERG/UN6FrfITr8cYY142xrQCx3jJGHMwjnW8k4Ff8H49cI3tRKRWUdaFvWgPXuvlok1lNLYTkVpFWRcyx1T4fOk+r+3Pv9fgPcmi5s5CG09pXCfhyVqrhx7/eQD3AxZn0d0+r33nfO0RF9s/72wzZZ/n85zPH+FFX9qX9wVo76LdPc42P+AIx8ranOJsswuIqfD8YRWOcUkl20XjqAwsb3ORq5+Zq356+XvY4Nzf/W7aDalw7DtxBLF1/rwzK7xWBtzjQz+urbCPei7aXV+hXXKF5xtW+F4sjj8myj+fVKHdFc7nngz1vwE99NCj5j+Udf/ZLqyzzs0+HnLuoxho7uM+nnbuI81Nu2ed7Xbv83wnIL1C3uZW+Fk9W6Hdo87nrg7W/+t66KFH7X4o7/6zXdjnHY4TlI8DpRX6kc2/47xtzjxJ9KEfGtvpoYceNe6hrPvPdmGddcD/Khz76CraNK/QZpeX/dDYTg899KhxD2Xdf7YLSdZV2HcKsNW57zmVvH5ihWMf4GI/J1Vo19OL42tcp0dYPjTjiXjFOR3VcTim6HrKRdP3nB+PN8ZUrFzPcH5sgf+VT7H/jLW2uIo2U3BUoTdm72mLz3J+3Ay8ve9G1rFu6VhXB7fW3m+tNc7HBi/67Q8NKnw+FkfgDcIRJPX5t6LUAA8aY079zx5cq1vh8zwX7Sq+9s821jE9Zj/gXRzTZMbhWDvufuBMAGNMc+AxHGF3n/O5QcaYecaYPGNMhjFmojGmi5d9FxHxirKuaiHOuko5f/YXOb/8ylq73cddleeWq5yr+HrFbMRauxY4HJiE4/+BGBx3hFyH4w6O8jtQbgZ+xXk3uzHmbGPMQuNYd3eXMeZtZyaKiASU8q5qoc4767iL7A4cU2LnOJ9OxjGOAsfdh/WBJB92r7GdiNQayrqqhTjrvsdRTAmOJSEqU/H5el7uX2M7Eak1lHVVC1TWGWOigPdx/MwKgGsqaVatcZcHNK6TsBQT6g5IxClfqiUKWOGYZalS5cGVBDTi3zU5p+GYquld57IvU4HfrbXuBgIuOacebuf88k1jTKmL5snOj+1wDA4AyqcX+8Faa6vYbg6O8A7HfzcVi8gMcJq1dmH5E9baFcaYE4HVOO4YuA/H4ClorLVb+ffCYGWew1Eleo61Ns8YMxJHHwuBL3AU15wKHGWMOcRauymgHRaR2kxZF55ZV5WhQCvn52+EsiPW2lXAaZW9VmFqSoBR1toyY8zVONZGTQMmAB1xZOWRzqzLCnyvRaQWU96Fad4ZYxoDn+GYunkW8ACOKZITcUx//TgwGhhijDnKWrslmP3T2E5EIoiyLgyzzlq72xjzAnALMMgY8wGOWSzX4riIdxVwNY4ZLWNxzDoS7D5qbCcikUJZF/ysex4Y4fz8amvtkiAf3y80rpNACLs/PCXstXR+jAKaebhNnQqf3wp0Bo7FUSF+E1BqjFkMfAW87uNJs5YVPm/sQ7+aOj9WeWxrbYExZg+ef9/BlF3h8+8qFp2Us9bmGGNexlEFeoAxppm1docP+6+DowK1MhV/ptlVtPkPY8wwHFWUn1hrv3ZW3L6I44+hE6y1PzrbPYTjjodHgXM93b+IiJeUdeGZdVW5zPlxC/B1NfZTnlt1XLb693WPc87pShwXCx+z1i41xqQAT+AYrB1hrf0bwBjzPnAecBtV3/0nIuIPyrvwzbt3cRSd/AgMqXCiNROYbIz5Cced1x1x3IF2vhf71thORGoTZV34Zt2dQBscd7Sfy3+zYD6wGMc4Kt3LfWtsJyK1ibIuiFlnjHmKf2c4udFa+1YVTfcdd1XFp3FXJfvXuE7CgpbaEW+VV0XuqDBFlbvHhvKNrbUZ1trjgAE4/iD/CUdF4iHAvcBqY8zZ1egXwP4e9usdX34AYapi+P7lot2KCp+3q7LVf22t8HmrKlv9+1qWtTbHRbt/GGPqAK/gmNLtBufThwBtgT/KA8zpKRzryZ3onM5MRCQQlHURwhjTjH/vMHjHOc2mr8qzroExJtFFu/Ks2+qizV6MMS1wDMDWAQ86nx6MY/A3rfzEpFP5tKgne7p/EREfKe/CkDFmf+AE55dPV3Z3n7V2J/9OlX2qcXFbYyU0thOR2kRZF6astSXW2rOB4cCnwEpgIzAXx5I2R/HvxbK/K91J1TS2E5HaRFkXJMaYJ4Axzi9vttY+56K5t+OufbdxR+M6CUv6n0C8td35sbExxpf1pAGw1s6z1t5mrT0Sx1RNJwFLcUwd/JbzQpIv/QLvCirKlU8rVuUbtDEmHscUZOFoBeDJxbaKJySrmp6sMssqfN7TRbvy11a4aLOvB4D2wK323xlYOjo/rq3Y0FqbAezGsRZdEy+OISLiDWVd5LgAxwx+FqjqDgNPeZt1y73Y9wtAfWC0tTbf+VylWYdjWbyKr4uIBIryLjx1r/D5vhlRUXle1OHfuwE9obGdiNQmyrowZ62dbq09y1q7v7W2vbX2KGvti9baYv5dZuFnL3ersZ2I1CbKuiAwxjyJY4k4cIx3nnazyV/8u1ScJ1m03Vqb5kWXNK6TsKTCE/HWT86P0cAwf+zQWltgrZ2KYy0wgATgyApNKq7jWemdXM4KzfJZP070oRsLnB+PdnG32FGE6fJU1toCHOvZAezvomn5SUwLbPDiEH8D5euzDa2sgfOPmgHOL2d6slNjTG8cFZPzgDcqaVLZXQmu7lQQEfEHZV3kuNT5cba1dl019zUPKD9xWFXWtePfnPU060YApwMfWmsr22bfXFPOiUiwKO/CU8WfkasTtBVP/Hp055qTxnYiUpso6yKUMeYg/j2P+Z6rtpXQ2E5EahNlXYA5l9e52fnlrdbaJ91tY63N49/fTVVZZIAhzi89yqIKNK6TsKTCE/GKtXY18IPzy4eNMfVdtTfGNKzweYybqZbyK3xeMbgqrk2W4mL78c6PlzoHJx71y+lT58e2wIWVtI8C7na1zzDwtvPjQGPMwfu+aIxJBq5yfvmrtXaXpzt2Tu9cPsg7yxjTvpJmVwPJOGZe+dDdPp0/09dx/K4v32cK6fXOjwcbY2IqbNPDeYxswOP+i4h4Q1kXGYwxRwLdnF9WNhDyirU2F5jo/HJ0Fb/325wfs4EpHvQxCXgZSANu3Ofl8qw7bJ/nD3d+rG4hjYiIS8q7sLWwwuejK2vgzJcLnF8ucWaYRzS2E5HaRFkXmZxT/I9zfvm5tXalN9trbCcitYmyLrCcRScVl9dxW3RSwbvOj8caY/pW8voZ/DuTiFdFlhrXSdiy1uqhx38ewP04ZsWwlbzWE8ebiMUxXdRJQEKF11sB5wPfAeMrPN8exzRMdwMHATEVXjsAmO3cZw7QcJ9jpjpfe6Hidvu0SQaWONtlANcAjSq8noKj4vM9YHkl23/h3DYfGAXEO59vC3wGFAG5zjYXufqZAe19/Lk3ABpXeGxy7u+JfZ5PrmTbKOBXZ/v1wEAgyvna/sD3ztdKgeO87T+OaSS3OV9fDhzifD4OxwnRQudrr3j4vV7nbP9gJa/FVPidP+Y8RsMK38NHof43ooceekT+Q1kXeVm3z37ecW63p/z78ODY5dv853fufL2D83djccwk1sX5fBKOdW3LnK/d6uHxnnG2v6SS1xoCec7Xr8ZxZ0obHNOYWuCRUP8b0UMPPWrGQ3kXeXkHTK1w/PeBTjjuJIwF+gG/V3j9Am/7j8Z2euihRw17KOsiMuv6AnfimNUkzvlcHI67thc697MJaFrFsd+p6nfufF1jOz300KNGPZR1wc86Z56Vb3+jD9vHVPj+U4GBzuejcBSdZDpfm+7ud15Z/9G4To8wfIS8A3qE58NViDlf71/hDc0CJTjW8cqr8JytJMTsPtvsqfDmZ52fn17J8e6u0KYAx8BjA/DJPu1aAr9UaFsGpFd4Ay9/rK7kGI2AxRXaFDm3Ld/PVc5jBnLAtmGfflb1eKeK7Zs7A6a8XS6OQK/4PY1y9zuvqv/AIc7fc3m7LOc+y7+egQcX/4DWzm1XVdUeOI1/B4EFOApmrPP/GZ9+vnrooYceFR/KusjMOuc+6vHvwPJ5L479jqvfubPNCRX2bXHkaEmFr98CjAfHOti53Q9Vtcdxp1z5fvMrfL4WSAn1vxE99NCjZjyUd5GXdzgu0i3Yp10uULzPc0+4+51X1X80ttNDDz1q0ENZF5FZd/I+3/ce9h53LXXVLzS200MPPWrZA2VdULMOR3FL+balwHY3j5ur2E97HDeKVxzXVcyJhUADd7/zqvqPxnV6hNlDS+2IT6y1PwFdcaxrNgfHH+4pON5o/gI+AM7FsRZYuS3ASOBZYD6OEEzGEWYrcExX2NNa+3klh3wEuB7HybdiHG+C7XAUWlTs11Yca82djeMusW1AHRzVdxuAL519OqqS72kPjrvH7gNW4ngDLQG+AQZZa19x+4MJMWvtdhyDoZtx3AVXjGN9tQ04BlMHW2vHV7kD9/v/A+iB43e4Gscdd7k41nsbBQyz1hZ6sKuXgLrAlVW1t9ZOBIbj+KOkDEdV7RSgn3WsDygiElDKurB2Fo7vGfywzE5F1trpOO7qGI/j55mAY1A7C8dA+xJrrXW1D2NMNI6pKUuAK6pqb619FsdSCX/iuJN9D467PI601mb44/sREXFHeRd+rLW7cUzPfxmOE4U7cIy9SnBM1/8BMMBae2s1jqGxnYjUGsq6sPQH8CSO2Zt34siSPcC3wOXAQdXNCI3tRKQ2Udb5XdQ+nzdz80iubCfOLDsAeBBYhqNQoxhHDt4MHG6tTfe1kxrXSbgxbv62EhERERERERERERERERERERGplGY8ERERERERERERERERERERERGfqPBERERERERERERERERERERERHyiwhMRERERERERERERERERERER8YkKT0RERERERERERERERERERETEJyo8ERERERERERERERERERERERGfxIS6A+EmziTYxKgkr7Zp3qEZdevXYfvmPWRn5Hm+YYxndT82ysN20cZtm8TEONq2bsju9Fx2Z+S4bV/mRWmSjfa87T/b+Fr6VI2SqdYp9agfn8DajDTyS0p831GU9X3bfXcVXea3fVUU7cc+uhIbVRqU41TUNL4+dWPrs6dwJ7mluUE/fmXio6rx/5MHUpdn7bbWNqnufo4/NsHuSfPs/7nFS4pnWGuHVveYEn58yTtv1KmbSKvOzdmRmkZWmof/RkOQi02b1SOlbh02bkujoLDYZVtvMtFxfC/bBzkTE2Nj6dSoIekF+aRmZ/l4cMI+D4ORhaHIwWgTTbuklhSVFbKzYDuW4GR+VfyVgf7KOlDeSeCzzhNRUVG079GGsrIytqzbSXGRh+8XfsxET/IQAx3aNcFay4ate7Au3lLCMg+rMT5smVKPBgmJrE1PI7/E9d8CVR9fWRjsLGyR2IjE6DrsKNhKUVlRUI+9L18yUGM78ZdwyDpvRMdE07FnW9J3Z7N7W4b7DYKch82a16d+cgIbt6ZRUOT633Ygz5kGMwsbJCbSqn49UrOzSC/I920ntTwHg5mBMVExtK3TIiLGgco68adIy7vKGdp1b40xsHn1DkpLvXi/8yAP/ZWFDRsm0aRRXTZvSye3wPXf2YHKwmCeI62bEE+7lBR25OawM8/H60x+yplIy8BgjwGbJzSkTkwSOwu2U1hWENRjV1Qx+3Qe879UeLKPxKgkDk8Y7tU2CVnxPPjerfTs24lnbvqQ7yct8Hhb26Cu2zal9RI92ldR/ViP2t1x4wkMOq47ox/4lKWrt7psW1jXu3fq4mSvmvu8TUmy72+WDRMTmXThuQCc+Pn7vg+qkv1bZJCYXOjX/ZVLSfLx+/NS86TsoBynXKyJ4eFeV9AxuSuvr32GFVl/BvX4VWmXuDtg+76l+8yN/tjPnrQyZn/dzKO2DVqlNvbHMSX8+JJ3XimG58c/SKNm9bns6IcpKvDsQo4nuQieZaMnuZicHM8bL12EtXDhne+Tk+f6vdibXAxWJoLvuXhBvyO4rv8RjP1pNm8u+cO3g/sxDyM5C4OdgwD9Gh3MLftdyu9p83hvw7igH39f/shAf2UdKO8kCFnnoS7JHXh02m3kZuVz25kvsTM1zaPtgj1WPPTg9jw19n98NG0BL370o8u2gR4nBnOMmJKQwJSLzgNrGf75+2QW+ngCy095GIgsrIk5mBJbj2d730KxLeaplfeQF+KbEbzNQI3txF/CJeu8ccftV3HokAO5oO995OW4fs/zdHwI/hkj1q+XyBsvXURBUTEX3/UBeW7GsYEcHwYrC6OM4fmzTuXgZi05ceIHrEnf4/2BIaxzEAKfhcHMwQGN+3BTt4tZkPYz7214JaTFJ67yT1kn/hSJeVeZbg068vSsu1n661ruueBVyrwoPvHX+NBdFsbFxTD+pQtJTIjjvNvedXmutCbkIMBdJwzlpO77c8n0Sfywab1P+wjXHKxJ+ZcUncjTvW8iKaYuT628hz1Fu4J27H2V55/OY/6Xltrxg4LcQu4Z+ThLfl7NmGfPZfCZff26/+gsz94Y4jI9u6j3/Kvfsn13FvdffQJ1EuNcto3P9q7CLtb9JCp+2SYmx4O79qqQlp/PNZO/pEliHV4cNIJo4+O+ciKjbisj17OT0ZGm2JZw3/I32ZK/mUs7Xk+HpK6h7hIAG/PD9v1eJOjevOMjmrRswIkXHhmS43uSizk5hTz46FSaNkzmjlGD3bb3Jhd9ybdge/HnX/h63d/cecTRHNWmvW878WMe5ufE+21fFQUjC7fnen5S3F9+3rOQDzdO5dCGRzK0+SlBP/6+lIEilVu9aD23D3+MpHoJPDHhGpq2bujRdibd/Qkcf44Vf1+4gS++WsRZJxxC7/1auWzr7TjRW8EcI2YUFHDdlGk0TUrmmeOG4fNI0095GIgsrIk5mFGcxWMr36JBbCMubH81xvffnF8oA0U899lz00mql8iwc/u5betJFnrDXR5mZuUz9vEvadUshZsvPt6vx/ZWsLKwzFpunvoNOcVFvDL4RJJiPbux8T/COAdrmrm7F/D+hin0adiPE1v+L6R9Uf6JeGfVH+t48YZ3OPioblx0a3gW0hQVlfDok1/RKCWJmy481mXbQJ4nDeaY8J6Z37Jyzy6eP344berW92kf4SrQY8FgjgNzS/N5YPnrGAxXdLqFhKjQXftU/lVNhSd+UphfxL0nP8HCOasYfObhREV59gbn6QDKnycU8/KLePjxaTRtVJebLzrObftAn1T0VXWKT5bt2MG9s77jyNbtuOKgw3zvRARcbIOaeaIRIL+0gHuXvUpGURqntT4v6McXEdeWzF3J77NXcOY1g0jy8I7sUOTiilXbGP/uXI7r25WTjzvAo/16KhiDKvA9Ey1w29QZ/J22m+ePH07dONcFqVWKgDysqVn4eeoMZu+cz/CWp9M6sX3Qj78vDbxEKrdm8QZuH/4Ydeom0H+Yf7PG00z0xLi3fmDrrkzuvnIodRJcX3gKx2JMX/NwyfbtPDL7Bwa278QlBxzi5155T8UnnlmVvZ7X102ge/0DObbpsKAeuzLKQBHPrFm8gcU//c3Jlx5DTKz7eff9PUZ0Z8nyVN796GeGDejOsAHdXbatKVm4KzeXm76cTseUhjxytPsbMgItEnMw2Bk4acssvt42h0HNR9KmToegHntfyj8R78x4bw7Txn/HGVcdz0EDunm8nb9uTPDkXOnK1dt5/5NfGDagB8cc2sVl23DLQl9ysKCkhGsmfwnAi4NG+HbgMC7ArEkZuK1gF4+tfIOmCc04qdXZQTtuZZR/lYuMKRsiRFFBMfef+hTxzRtTVmaJijKUlbmf2smkZ3s0TVZ0Vr5HU2XFZRa7nS5r+cqtjHtzNuszMt3uDxzh4em0WbE53k+D5cs24AgRX6fPmrhsOQkxMXyx+S+ftv9HTozfptHKz4kP2JSSwbA9t27Qp1jOLM7h7mWv0DjOx6lAA2BjfuOALrkjEkneuuNDHph4M606NuHvxZs82sbfueiJTyf/RkLDBH5atM5tW28yEbzPuGBnYl5xMVdOnEqH1g3ILnK9dqtLEZCHGbmJAZ9iMhRZ+Mqaj1mb8wup+RuCetyqKAdFKrdm8UYuP+QOMoo8PxHmz0z0ZJyYX1DMo09+Rf9ju1Fa6t9p24OVh776YNGfJCbHMnV1NcaHYZ6FNTEHZ+34iRgTzZqcH4J2TFeUgSKe+eypqdz9wbV07N6Kv/90P04Mdh6+/+kvxNaL4/el7mdPD+Q502Bm4S+bNnPXjFksy9nh+05qeQ4GOwPfWPcZ63J+ZnOej8tC+JHyT8Q74275gC1bMlnyy2q/79ufWdikdX02bfNsqVhPeZNtwczBTRmZXD/1K3JiQn9uVBno2rLM1Tz213iyihcF5XjiHc144mfFhcXkbNxGXEIsD30wmpEXHxXqLlXp8y/+4JfFjj9Mo3xdbqYKwVpyB6o388mHi/8ko7CA2KgoWiRVoypPd3r/IxR3e+8qTOOvbEMUUZzY8kzqxoR+OjRVO4o4rFu6mQv3v9HjopNy/ryrzZNKfmvhnTfmsCs9B2MgPtb1+3o4LjEAvmfilqws5q1wnFQ9sGlz3/8uUB4Cwc/CElvCV9vWANA6sR2N4poE9fiVUQ6KVC5tewZlaem0368lL06/mRbtGgX1+J5k4rK/tvDaK99TWOz+hFk45mF1xofj5y5gV34e0cZQL87HHArzLKyJOfj19jnkl+YRa2JpHO/ZmtiBpAwUcW/BzCVc0Pc+j4pOyvlz2R13eVhWZnnjtR/YnZFLlDHExriemaWmLDUwcdlyVm1wFA+0Sq7n0z5qew4GMwPLKOPbnZsB6JDUlXZ1OgXt2JVR/ol4rqS4lElPTqG0pIz6jZJJru/fmaL9obS0jCefmM66VPc3/AZyXBjMHJy3YSOL12wDoGWyj+/ntXjmk2BakL6Mv3NiiTExdKvbI9TdkQpUeBIgJbv2kJtdwOgHT+PUy12vgwahWVrA0a6Us044hOfvOM3tRSZvwyNSik9icqJ4YdAIPhx5BvXjE3zejz9F8sU2CE3xCYClG0c3GcxVnW8jMbpOSPpQkQZcIg5lpWXExEbTu39Xr7YL5onFcvFZpTx1yyncPmqQ344Nwc3E6tg/vgmTTjmHOw4/2vedhPmJxmAJRRZuL2jMqE43cUWnW5SDIuEuJ5umrRvy+IRr3RafhGqs2L1Tc8bdeyb1klyPkQI9tXKwi09icqJ4fejJjB92MtE1tBCzJp1wLJean8J57a/kui53kxLbMNTdEREPZK3fCuDxsqye8ucSdInZZTxz26nccMExftunL4KdhRf2PIhZZ13M/o18LGj3Yw4GQk0qPgHYmt+As9pewhWdbg55AabGgCLeic7J5pkpN3DrixcQFeXZ+3Ywl9wBqFtguO+qYZx4TE+X7WpKESbAKV27M/ucS+ndtLlvOwjj4pNACsW50CHNT2Z059vYr26voB9bKhcRhSfGmKHGmFXGmDXGmNsref0mY8wKY8wSY8x3xph2FV670Biz2vm4MFh9Liku5ZGzn2POl4sYdc/J9Bvqfh3vUJ1QzNmeQ5+e7Th/5KFu2wb6jrbqqE6QvD1/Ia3q1uP1oScRF+V+fdlK+XlQpeIT723O38ZjK9+geUIrrug0hlgTF/Q+7EsDLvFUJGadN/536QAe+nA0rTs19fu+/Xli0Vr4a+kWhh7ZneFHu66WDkZBpi+qk4er9+zhw8V/Mqp3H07vVo1qcV1wC4kSW8Kzf39A04RmXNzhOqLw8W8aP1IOyr5qet55at2STdx2wqPEJ8bx2KfXUqeu6+IOf48VPZJRTI/OLRhz8UC3TcNtXW+oXh5+vfxv+rZsw82HHel7B3TRLaD7r8x7G74jITqRKzvdTEJUaLNe+Ve7Kes8d88bl3L365d43D7YM2OWllnW/b2DU4/vzXF9Xd9IUZOycObSNWQWFjB+6Mk0TAjt+2mkXXQLhTIsD694C2MMozvdSnJMaG4ILKcMrB2Udf5RXFTCZ09P49Bju3P+zScE/fieZGFRUQmNGyRz/fnH0qKJj7NhVSKci0/mrNjAztxcXhkykkaJob+xy59q2jjw3Q1z2Z6fyiUdr6N5QqugHlsqF/aFJ8aYaOBlYBjQHTjbGNN9n2aLgD7W2gOAz4EnnNs2BO4D+gKHAfcZYxoEq++lJaU8eu7zrF+5lUvvGklMrP9O/vvzhOLM2SuY+fNKLjutH907ua/gC8c72qpr4Zat3DZ9Bn1btuHRYwb7vqMwP7lYriYXnyzO+IvnVr9Lh6SuXNzxWl10k4gQyVnnqS9f/5bCvCIuum2EV9sF+8QiwAcT5rNg2UbGXDiQ9q1c3zEb6OKTUMwE9vD3PzAvdSMPHz2Ig5u19Hk/4a4mLjUAjnVOX1nzMfvX68UZbWr1+RsJQ7Uh77yxbskm7j31aZq2asBpHsyS6U+eZOKadTt556OfGNxvPwYe3s2vxw/3PJyy4i8+Wvwnow/uy8B2HX07uB9F6k0Jwc7BjXlbefyvN2ie2IpLO14f8rGgxoG1k7LOO8t/XE7v/l3p3Ku1x9sEe2bM8e/NZdnqrdwxarDbC27hdre3r3bl5nLV5Kk0rlOHVwaPJCbKh0sItfxmhKDPelKwk4dWvEZKXEMu7xj6m/GUgTWbss6/pr81m6/f/oGzrh1M/2EHerSNv2Y98YS18PiTX2Gt5Z4rh7lcOSGcb1z3RmZBAVdPnkrDhEReHDTCt5kww3jWk5qUgQVlhdy/fDzFZUVc3mkMSdHJbrfpkNSFYc1PDULvaqewLzzBET5rrLXrrLVFwCfASRUbWGtnW2vznF/OB8pHK0OAWdbaNGttOjALGBqkfgOO5QXeuP1DSopLadIyxW17f6/R5ulFtuefm8Gu9Bweuu5E6tf175tOpCy5M23lKp6Z+xOndevB6IMO83k/4T6wKleTi09+2r2Q19dNoEvy/jRPDI+LphpwiRsRnXWeyNydzWfPfkX/YQey30Ht3G9QQbBPLJaVWR5+bBr5hcU8dO0I4uNcv68HelAV7EwstZbrJ3/FtpxsXht6ku93uEVAHtbU4pPvd85nUupMjmwykN4p1fibxk+UgVJBjc87b/312xp+/HIhLdq5/3cSihkyP/7sV5at3sotFw+kUUqSy7bhmoe+euj7H1i6azvPDDyB1nXr+7aTCMjCQAt2Dv6ZuZKX13zMfvV6cUrrc4N67MooA2slZZ0Xpr81m9ysfE6/0v3sWhUF84JbaWkZYx+ZirWWsdeOICbaf6fTw/lu76Xbd3DHN7M4vFUb7ut/nE/7CPccrEkX3gBWZa/nmVXv0C6pEwOaHB/UY0uto6zzs5dveo+VizYw5tlzadvFf0tm+etGvZ27snnh1W85aP/WnDnsYJdtw60I09ccXLFzF/fM/I5+rdpyS98BPu1DxSfBsbsonYdWjCcltgHntb/SZdsoomgU14ShLU7h/Hau24pvIqHwpBWwucLXqc7nqnIp8LWP2wbEgllLGT3ocbZt3OPX/frzhGJObiH3PfQF9esm0Hs/9z+iYCwvEIrik1fm/8qr83/j2w1rfd4HEPYDq9pgxva5XPXHg2zN3+y+cZDopKO4EPFZ54lJL31D2s4sLrv7ZI/XLfWGP3MxLT2Xh5+cRp3EeJo39t80khAZmZhZUMCVE7/gtcW/k1ZQjRO2EZCHNbX45IONU/l44xssyVgQ9GNXRhkoTrUi77z1xPkv8sR17/t1n3672FZmefSJr4iPi+GMIQe5bR+Os2P6moVFpaVcN/krNmdlUjeuGncNh3kW1sQcnL1zPq+v/ZS5u2YF9bhVUQbWOso6L+RlF/DVm99z5PDetGjXyO/799cFt+07s3jiuW9oWL8OzRq5fk8Lx7u9fc3CL/9ayYs//8KvW6txbi3MZ4euSRfeAH5N+5N7lj3P7J1fu2/s1Dl5P4a3OD2AvZIaSFnnZ8VFJYz937PMn7WM9F2e3XAQ7Bv1Zny3nB9/X805w/v49Sa9cC4+mbx8BW8vWMjGzAyftgdUfBIkq3M28NSqt/liy8cu25VRxsrspXyzfTJ9GvbnjDYXBaeDtUh4/+XnJWPMeUAf4Ggvt7scuBwgwbi+i8tXJbv2kNy2OZ16tmHp/DWu+5OejW3g2T/I6Kx8Suu5f3OIyyymqH6syzar1mznrItfY5cp8ujY8dllFNb1vHYpNgeK3c9yVO1twBEkJcnW+w2Bp+bOoyTZEYzNkpLZkRuCtX/2kZ8TT2Jyod/3m5GbSEqSH9eBr8L23Lo0T/LvbD6eSC/OIr04hXPaHkpJWQk/75kd9D7sa2N+Y9ol7g5pHwotrCvx/0V/CQ5fs865bcDzzpWC3ELevvdTTr5mGAlJ8eRlF3i8rafZ6GkuemLBog2cf9l4cj34UYVzJvpqzZ401vyUBsnQpE4Su/JyfdtRTgwkl/ilT5Gch8HOQotlQuoiWieWkRxTj8ToOuwq3B6041cm2BmovIts4Ty287eS4lJIS6dl787k5xaSuafq8Yc3Y0VPeDJOTN2azjU3f8hfaWke7dObTAxWtvk6PtycmcnJ73z4z/jQZ2GehcEaFwbT19vnANA6EdrW6cimvHUh7U+gMlBZF9lqU9a5MuXlmYwYNZDTrxzIi3dM8Hg7f2aiJ3k45+e/mb9gHTmJ7vMkkFkY7HOlz//0yz85GB8dTWFpqfcH91MORup4MNhjwRVZa4D69KwXRbukjvzp4maEKKJoENeYwc1PomFcE97fOC5o/fSUsi6yRfJ5zGDbvTWdx859nqiGDUhMiic+MY6M3a7fOzzJQn9eQ3z6mW+gURyFRf4Z25QLVhb64uHZP4TNeFBjQdd+S1sCOMaAruSUZDNv17fEmFiObzaCwtICpm79JAg9dK2m5F0kzHiyBWhT4evWzuf2Yow5HrgLGGmtLfRmW2vt69baPtbaPnEmcLNLXHH/qdz/9ijqNXAfkt5UK/rrbjaAzKx84jJL6de7A8cc2sVt+3Cs4i9XnZlPYnKiuPaQw5l+xgW0rRf6aZUhsu/0htDc7Q1gMHSv15v/tb2ILsn7LjUZGrrjTSoR8KyD4OWdKzPfn8v1R91HXnYBsXHRRHkxTXGwK/kBiktKiYmO4vIz+vt1PW8I3pIB1clDgE4xDfn2rIu57MA+vu8kzO/2hpp5xzdAan4Kl3caw+jOt1I3xr+z9/hCGVjr1Zixnb/VbZjMa9/dwRlXuZ8WPRRL7qxet5Mya2mcksR+Hfw39TME5w43qF4exuVG88CRA7myd3gsyRoINelut4oGND6eMd0e4ID61fg7RsQ7yjov7dmWzq1DH+XV+yd5vW0wl9wBKCoqISY6ilGn96NBvTp+22+4Z2FMThTDO3Xju7MvpVlSEO+GqESkzgwdihw8seX/uKj9tTSIrXo2oTLKWJG5mK+3TaJPw36c2eaSIPZQIlitOY8ZCmVp6dz20oU8+fm1NG7u47WpffgrCzOz8slcn4kx0LNLC5dtw+3aYXWvF57QsSufjDyTuKhoP/aq5gvVONCd7JIs5u6axY+7ZjCw2XCGND851F2qMSKh8OR3oIsxpoMxJg44C5hasYEx5iDgNRwBtrPCSzOAwcaYBsaYBsBg53Mh8dkTU0ioE8fpV3q2LqY/L7CB5xfZAM4feRj3jh5Gpzb+vTgQzOUFoHphMv3Pv4kyhrdPOJV6cT7+YaPik72EImQslkf+ep+dBTu4tON1NI5rGvQ+iHigxmSdJ4oLi7HpGdz12qXc/tIFRMf4988Rf15oA2gencDpg3vz4DXDiXZTKBPo4pNQXGzbmJHBz1s2ccfhR9G/VVuf96PiE4dQZOG4NZOpH5vCFZ1uJi4q9CdrVHxSq9WqvPNGdloOcyb/xvDz+vn1RgV/ZmJcZikPXjucJ28+mUYprvsY6JOMwc7DMmtpmJDI7UccxZAO7m/QCLRIzcFQZOCkLUvYmLeOizpcTaekbkE/fkXKv1pDWeeD1YvWU7htF0n1Ejl8cE+/799fS+4AtK9Tl3NH9OGeK4diXMRKuI4NfbU2dQ8p8Qm8NuQk4qN9uOgW5uPBmjgWfH71F7y5/nnSi/e4bJdbmsO83d/x7Y4v6df4WE5pdW6QeigRTFkXYJ8+NpmGTevz5MTrad7W9VJ0obhR79xjD+bV+86iV5eWLtvVlCV3AEpzyzi8VRvu7Of15D0OWnInaFLzU1y+bnD8f5BRnMbX2ybx6545nNDiNPo39uzavbgW9oUn1toS4Boc4fMXMMFau9wY86AxZqSz2ZNAMvCZMWaxMWaqc9s0YCyOIPwdeND5XEhsWrmV2RN+4cSLBpDS2LN/aKE4oQjwwENfkJNfyGM3nUS9pASXbYNxh3ewB1PguNA2etJU2tRL4dWhJxEb5eM/FxWf7CUUJxvzSgt4cMXrAFze6WYSooLzvbqik45SUU3KOk9Za1k0azEDRhzE7S9fREysZyeuQpGLO3dl89QLM+jZpSWXn97PbfuadrEN4PapM1iTkcZLg0+kdV3/3G1RXZF6pxsEPwtX52zgyZVv0bpOey7tcD1RhP7uDOVg7VQb884bnz71JQl14jnpkqP8ul9/3un93AszSUqM56HrRvi1GDMSxoi3T5vJwu1beW7gCfRs7OOsL2F+0Q1q3knHwrIi7lv2OmlFu7m80xhaJLT2eNtAjBuVfzWfsq56zrtpGHe/dgm9+3f1eJtgX3DblJrGy+Nnc0TvDpw57BCXbcNxbOjruHDNnjRumf4NvZu14MEB7mdoq1SY52Cwzo0GS15pPt9s92ypuZySLObsmsUPO7/hmKZDOaHFaQHunUQyZV3gLf/lb24d9ih16ibwwNuXExVV/eU3/HmudNo3f7Jjdzb3XjWMxHjXy/PUlOKT79au483fF3BRr4M5oaPnf6fsRcUnQeOq+MTiWHrQYEiOqUdcVBwltpj/tbmYvg39ez6mNgr7whMAa+10a21Xa20na+3DzufutdaWh9Xx1tpm1trezsfICtu+Za3t7Hy8HarvodyHj35BbHwsp48e6Pd9+zM40tJzue+hKTRrVJcHrjmBKFcl/IR38Ul1LrQt2LKFO7+ZSb9WbX0fVIGKT/YRiuKT7QW7eWzlmzSOb8p+9XoF/fiVqSknHY3DKGPMr8aYHGNMrjFmgTHmSmNMpTljjBlhjPnBGJPp3OZXY8yFwe57OKlJWeepyS/NYNwtH3DkCQdyx7jQFZ944od5q/jymz+54KS+HNqznd/2C5GRibnFxVw1cSpRxjB+6EkkxrgeVFYpzJcZgJqbhQvSl/Hq2k/oXv9AhrU4JajHrimUd/5RG/POU5tWbmXe9D858aKjqJPs/u/9UMyQuWHTHp54/mt679eaa852f0Im0MUnvvA1CwtLShg9cSp78vN484RTaO7rUgNhftEtGIKdgdkludyzbBxFZYVc0WkMMcb97yDGxDKo+Ylc0elmv/cnnMeByjr/UNb57p07P2Tz2p3cMe4imrVp6PF2wV5y54vpi/nx99VcddYAurZ3PbNuTcrCWavX8OLP8zlz/16c36O3bwcP8xysaRfewPM7vzOL0/l62yTm7/mRIc1P5qgmg4PQu+BT1vmHsi7wVi9az4vXvU3brs3pN+xAl22Dfa40L7+IR5/6ipZN6nPdeT7OAOInwSw+eWrOPBbt2Mrjxw6hXb0Un/ah4pPgqSz/ok00UUQxoPEgzm8/mjv2f4yDGhxOZnEGm/LWsado5393FKFClXcRUXhSk2xZs53vPppHy/aen2jw9wlFTy1fuZXnx33L4Qd24JjD3E8nXFOLT6as+Iun58zj2w1rfN4HoOKTfYRioLUsczWj/7ifxRm/Bf3YVQnnk45e+AB4HWgPfAy8AdQBxgHv7NvYGHMN8CXQ07nteKAl8I4x5qmg9FjCxpRXZvLyTe/Rb8gBXP/4WSHpg6ezgb34+nesS93NHaMGhXzJHV+3qY6NGRncOHU6K/bsosxa33cU5icaoebd6Vbu2x0/88Lq95m98+tQdwWIyAxU3knAffzwRBIS49i/TweP2odiJrDv56zk8y8WcNYJh3B0n84e7ddT4b4E3Z68PEZNnIIBOjdwPeW1S2GehTVxuYFdhWnct3wcEza/Q4kt8WALS1ZxJt3rHciVnW4JeP/CiLJOQio/p4AHTn8aYwz3vnEZ8Ylxft2/P5fceerpr0nPzOOuy4dUt1t7CfcsfOGnn5m1YQ2N69Tx7cAQ9jck1LQLb+DZnd9RRNMgrhGJ0XUosSWc1vp8+jU6Nkg9DCplnUSMeVN+Z8obP7Dp721u2/pzbOhJFi5Znsqnk37j5IEH0q+36/FrOBZh+qK4rIzrp3xFaVkZgztUYyys4pOgKc8/g6F1YntObnUON3a7j9PbXMAhDY4grWgXP+z8hvFrn2HcmidYk7Pyn/Y1QEjyztjqXDiogepHN7KHJwwP6DFiYqMpq1vP6+1sA8/+QZbW8+yNoai+Z3crH3RAW37duMWjtgCFdb2rZyr28kYxb9tXVJLs+//vJcmOcGyQkEh6QTUqQ5M9OcnlmcTkQr/ta18pSf67E8SV5kmhKaw6rmkr6sXUZ0H6zyE5/r7aJe522+aW7jP/sNb2qe6xuh8QZz+Y1tyjtoe02+zymMaYU4BJwHrgMGvtbufzccBEYARwmrV2kvP59sBKIBc4xFq7wfl8AxzTK3YC+llrf/HpmxOPBSPvvDH0wqNZu24Pa5d7njehyMV2bRpRVj+GtZvd/5uF8M3E6uShY/syooypXgFKBORhMLIwVDnYvk4OnZP3Z2X20pAcv6KKGeivrAPlnYRf1nkqpVNrstJzvdrGn5noSR7GxERx3kVH8tFXC8jJc/8eXNPyMD4mhtyEIt8OWpGfslA56J3WiRl0q9uDdTmrKbZV/x4TohLp2+goRrY6k7U5q3hlzeN+68NLh3yksZ34RaRmnTuHHN+LsZPG8PWHP/PyXZ95vJ0neejP8WGP/VqSGV3CutQ9btuGYxb6moNRxlCUVOrTtnup5TkY7AxsnZjxn+fKZwE7svHxdEjuwoEph2Iw7C7cQWZxBl9s+ZiNeWt9Op6yTvyppuadp6IaNvCoXbDPlcbGRHP/fSfz4bTfWbzS/Tldb7IwnHOwSVIS20w138PD/LxoTcjAGBONxXJmm0Non9SZgxscjiGKzOJ0dhZsY/bOr9lWkEpa0d7n+ROiEjm19Xn8nvYTq3NWuDyGv7IOak7eacaTECgpLqUsLZ2mrRrQoInn1V2huJsNYNGSTQC0a9mQDq2qcVeXn1Sn4rE6M5/E5EQxpEMX5p47it5NPfvHX6kwv7utXE2e+QTguKYncG67K+iQ5ON6fH4WgXd9lytfr+Hp8vACsNYWAfc4v7ymQvtLgHjgpfLwcrZPBx5xfnllwHorYeubd39k9dxlABxz8iHExrt/rwxFLm7cvIfNy3YA0Ka5+0FfOK7pDdXLQ4BWyfWYfsYFDGhdjWWHIiAPa+Id3+UGNz+J0Z1v5YD6rtelD4YIyUDlnQRNxtpUAOo1SPJ4m2Df3VZSUsY7b8whJ6+QuNho6iT4b11vXwQ7DwtLSojJieKs/XtxW98Bvh3cj5SD3iks68jFHa6lWUJLl+0KyvL5dc8cvtwygU7J3biq8+1B6mHIKOskbPzx7VKeuPRVPnp+hlfb+XPJHU/ycPnKraQud0zJ3rSh6/ezcMxCX3OwzFpicqLo07wVbww7hfhoH8d2YXzHN9Tcu77BcSd3uzodOaX1edzY7X5OaX0uvVMOY0fBVr7d8SWvrX2a19c+/U/RSQ258xuUdRKh2nZpxvljhrltF+xzpcUlpdx1z0SPik4gsDOfBDMHd+XmEpMTRY/GTTm2bUef9hHus39Fuh71OnNuu5E82utmTmt9AYc06MeOgm18t2MaL695jDfWPcfyrMX/FJ2U51xidB3ObncZfRsdxejOt9K2jo+/39ALWd6p8CRE6tRNYNy3t3PujUO92i5UxScJ2WU8ftNJPDHmJOomuf5jviYuL1Bu0Zqt7MnPY/ywU2jtw6w1/4iAi21Qc5caAHj0rw9IK9rFqI430DDOswteBkOHJPfLTvkqQi687au8CmtdJa+VPzfAWUkJcJzz4zeVtP96nzZSC7Vvlcwtz5/HfW9eRpybi1ihdMrAA/joiQvZv2Mzv+43Upahy9pTgAVeHDSCtvXq+7yfSMjDmnrR7d0Nc9mYt44LO1zjVba5u0jnqwjIQOWdBNWoe0/mxa9vISY22uNt/LlEqzfjxJfu/h/3XOn+JGigp1cOxRixe72mjD64L2ft38u3HSgHgdAsu3PFggdIzd/gtm1BWT6/ps3hiy2f0Cm5a00vPlHWSVj54bP5pO/MIirK0LZrNW4Aq4Sn5009df6Jh/HhExfSvLHrc4XhmIXVGRc2sokc374TDx91vM/78JdAnh8NpGBmYIyJZntBI45uMoQL21/NTd0e4MjGA0mOrsvKrKW8suZxxq15gi+3TmBn4TYKyhz/ThKj63Bh+6vpXu/AoPU1gJR1EpF69GzBOTcMpXd/9zfR+vsaoicSssu46qwBnDaot9/26Ytg5+A9/Y7lhUHDaVcvxbcdhHEBZiQXXzaMq8+DPa9nZMvjqB+bzOKMlYxd8QqvrnmSqVs/ZUfB1n8yrpzFkhxTl4vaX0PvlMNYkfUnOwq2cl2Xu2iR0CZgfQ2gkOWdCk9CJC+7gO8++onBZx5O09YNvdrWnycUPVVWZnnima9p1rge9199AlHG9ZtxOBefVCdI0vLzGfX5ZOKjo3nzhFOpG1eNtWYj4CQj1MwTjQC5pfk8uGI80SaG0Z1vJSna/VxsDeIacXKrc7h1v4drUqV/dZVXS1a2kGN5OWhMhc+7OT/+vW9ja+02HFN5tTbGVGOxYIlk65Zu5pnRb3DQgG7c/9Yo4t0Un4SqIHPOzL/YnZHLg9eOoI6bdcdrYibmF5dw1cSpALw+9GTqxIRHkZAuunmusKyI+5eNJ6NoD1d0GkOz+BZut0mOqcfIlmcxptsDAelTmBefKO8kqP6Y/gdNWzXg2FP8MmPrXvyZiWVlljk/rOSYw7pw7ohDq9u1vQSrkKQ648OHv/+B2RvXMXbA8fRr1da3nUTIuLCmyS3N3+vOb1fyS/OYu2sWP+/+gW51e3B5pzGB7VzoKOsk7JSlpXPZPSfzzOQbaNWxqUfbBHsWMIAfZzmmYX/wmuFER/nvnGm4Z+F3a9fx/E8/c/p+Pbmo10G+HTzMc7CmjAV71u/C+e1O4rEDbubU1udxUIO+bMvfzMztU3lpzaO8vf5FVmUvI6M4Dfj3zu860cmc1+4KDmrQlys63Uyn5P0C3tcAU9ZJRJr5wTx2b8/grOsG+3W//spCay0dWjfi+vOPoUdn1+eXalIO3jrtG0rLynh58InER3t+08heVHzid2lFmTzy16tMSp3FA8tf4qlVb7I44y+WZjmWCazs2l792AZc2uEG9qvXi3m7vuXjjW8wYfM77CrcwfVd76ZxnGd/h4aRkOWdCk9C6OMnpmLLLGf7OSzK+fsi27K/tvDCq9/Rr3dHLju9n9v2NfFCG8C6tHSunvIlHes34OVBJxLtpggnWFR84r0t+Tt4+K/XaRjXhH6N3RfrZRdnsjB9Pg3jGnPzfmOJNdUoPKpCGF50a2yMWVDhcfk+r3/l/HiTMeafKjpjTCxQ8cpk+Zok5VMjZFZxvMx92kktNOuDeTx9xXgO7N+FW1+8wG37UBSfZOcU8NDjX9KiST1uvcT93V01MRM3Z2Zyw5fT6dKgES8OGuF7HmpqyX8EOwuzSnK4d9k4Sm0pF3a4xm37wtJ81uSsoHlCK27sel9NK8JU3klYWTBzCWuWbuZ/Vw0kys0FrIpCcXfbhCkL+P7XVYw+60gO7u76TqBA52Gws7DUWm6cMp21GWm8OmQknVK8u6nkH7roFrKl59wVnyREJdIqsS0ntjyTgxv0xVJGj3q9ObLxwOB00L+UdRKRJj31BSXFpdz35mXUqZvg0TbBngVs+45MnnlpJr26tuTS047w27EheFnoq5d+ns/M9au5u9+xHN7SxzuClYMBv/P7/h7XMaLlsSTH1GFh+goeWP4Sr659iq+2fcauwu2V3vldN6Y+l3a8jp71D2Zp5h9sylvH1Z1vD/dlB5R1UiMVFxbz+TNfcWC/LnTvU9l15L35++Z1d1loLTz6+DR2peXw0HUjqF/X9ftmuC2546utWdncOn0GPZs0Y+yAmjn7V6QWn/yRvpwPN01la8FO8ksL/nk+NT8Fi92rbVxUPIObjaRjclc25q7llz0/kFWSwfrcv5m29TOyijO4sdt91ItNCUhffRS2eafCkxDasy2daW98x6D/9aXv8T282jZUd3hP/Xox02Ys4eJTDufQnu082sYbkXChDeCXTZu5d9Z3zN+6mVJr3W9QFT9fbFPxifdWZK3hlj+fYNaOqW7bFttift79PZNTPyQltgG37f+wRzOleCvQxSdFNoYNxY08egC7rbV9Kjxe32d3nwAzgE7ACmPMa8aY54HFwABgk7NdYBczlhrn249+4s17JtBv6AF07N7Kb/v1Zy4uXbGFtz/4iSH99+fEY9xPsR/oNb1DYd6Gjdz/7fc0qZNEUmzNngWspi4/t6NwD/cvH8d7G15x27bYFvPT7tlMTv2QZgktuGW/scSY8JjtpjLKO4l0Hz86mdadmnHGVd6dwArFkjtPPPk1m7elM/baETRp4Prv45pWfJJTVMTln0+hsLTU91lPIOyzsKaOB6uSEtuQTsn7cVnHGxjd6VYGNhtOfFQCc3bN4u31LzJv93eh7iKgrJPaYefmPYw953latGvMQ+9fSXKK/yYW8Of48Lsf/+LrWUu58KTD/V6I6a1gLjVggVunzmBDZjqndu3u0z4A5WAApRVl8siKcUxKncn9y1/i6b/fYknmKpZlOf4/rOyGggaxjRjV6SY6Je/Hjztn8NHGN/h887tsyd/ItV3upEVC66D1X1kn4jD9rdlk7M7m/DEneNQ+2Dcl5OQUct/DU2hQrw4P+HnVhGCMCX3Nwe/XruPFn+fzv/17cUJH90shVaqWZ2Cox4FFZYX8nvYT32ybTLukThza8Mh/ZoZenrWIaVs/o7ismAvajSba+DizjSf9qCF5p8KTEHv3wYmsWbqZ1p2aeb1tqIpPnh83i1c+nsvilalu24b7RbbqFJ98tnQZr89dABA2S+5A5E+vHIqQ2ZS3zeNplottMb+n/cTk1A+JNXHcuv/DNIht5Pc+heHMJ5Wy1pYCJwK3A7uAC52P1UA/oPyNaqfzo7vKSHeVlVKLTHl5BlcNfpx1K7a4bRuKZegAPvxsPj/+vpqsHM+yNhzX9Ibq5eHHfy7hzPc/IauosHrzX4T5IAtq7kW39bmpLEjP9ahtUVkhv6XNY3LqR9SPbcit+z1MQlTknoT1lPJOQmHeFwuYPeUPhp59BPFulnXblye56M9xYn5+EfeOnczWnRnEx7l/Pw/HC27gex5uzcpmyBvv8P7yxb4duFyYzwJWU3OwfCxoMNSNqcdRTQY5C05uoUvd7mSWZPDtji95atW9TEr9gMUZv/2zbaekblXsNfIo6yScLZ23iofOe5HOPdtw7/hLPdomFLOAPf/qtyxfu406bpaMhfAcG1anCPOcjyZw2w8zfNr+3x2F95gwki+8LcxYwYebvmR7wS4KSgv3eq2yO7+PbzaCdnU6sjlvPb+lzSWvNIeNeev4auvn7Crczg1d743EZQeUdRLRCvOLePeBz1m7YgvRMf69tOuvJXf+XrODF179lt77taZr+9C+RwQzB1/46Wdu+f4bZqxf7dP2gDIwiOPAyq4Fbshbw9fbJ/HV1s84pulQ+jQ8krgox89xSeYCPt38Fr+mzaXUlgatn9URyrxT4UmIFeQWctMx9zPxte992j4UF9qKikv59INfKC4ppW5SPEluToKG8/ICUL2LbQD7N2rCj+dcxpAOXXzfSYQUnwSruj9cp1kGx8nIMkpZlPErn25+i1Jbys37jaVJvPfFY+5EUPFJsbX2cWttL2ttgrU2xVp7MrAB6IKj+nK9s/kq58f/lP8aY1oASUCqtTYvCF2XMFdSXMran5YDEBPrvpo4FAWZZWWWe++fzI8L1gC4reb3VqQUnxSXlZFcEMfbw0/jnO4H+LyfcB9kQc296Aae52CJLWZh+nw+3/wudaLrcMt+Y6kTgBnAwo3yTkLh2cvGccOJT1OYXxSQ/fvzYtvGzWlcfcMHpO7I8Ns+ywV7jOiLrMJCYnKiOLRFK+7rf6zvOwrjNb6hZuZgYnQCBaUd+F+bixnd+TZOa30Bbep0YFX2Mr7Y8jHPrXqAaVs/Y3vBlr3uCj8opS/Xdb2b67vcE9T+BpKyTsLZL9MWctcpT/LWI+5nrC0X7FnA8guKueaGD5i3cJ1H+6xJxSdpeflE50TRMrkuNx7qfon2KoX5mLAmXXiDyseARWWFzN/zI1O3fkrbOh3p1/jYf2Y4WZm9lK+2fk5WSQbntrsiIMuQB5qyTiLZ9Ldm88bYKZSWeJYfwV5yB2DajCVccMUbrFy/w23bQOegL3zJQQtM/mMFpdbSpE4SPRr7WHQT5jci1BTt67RiV2Hlv6OZO6ayIO0nBjQ5ngZx/95w/lfWEn5PmxesLvpFqPJOhSdhoKS4lLK0dLr36cDzX42hQRP//4Hp71lPAOrkWsY/cA73jh6Gu+ts4V58Uh2btmSyMSuD5waewIFNm/u+IxWf7CUcL7oZzD93AZTaUnYWbic1byPJMXW5u/tTAZlmMlKKT6pwFhAHfFzhufIqu6GVtB+2TxsRAC6+/USenHidR21DNRtYXGYpI47uyfgHzyYh3vX7eThnYrWKT0pLKS0rY+yA4xncvrPP+wn3E41QMy+6lXNXfFKeg8W2iFXZy/krewmN45txV/cnqB/bwOW2NZjyTgKmML+I9DWpRMdEcda1g6hTN8Hjbf2Zid6ME+sWGO67ahgD+7qeZjgYeeiL6t6YcHijNlx8wCFcfXBf33ei4pOg5GAUhn6NDmJ0p7N59IAx9Gt8LPVjG7Ag7WdeX/sMb6x7ju93TqfY/vv/f3kOHtzgcM5vP5r80lw6JHfmui53B7y/Iaask7Dw549/seK7xQCcdMnRtOnsn5uAAjE+/N/Qgzhr2MHV6dZ/REIWDm/Vjev79OOK3of6sUfhpTYUn2zO38B3O6YxKfUD+jceyGGNBvxz5/fyrMVM2PQOc3bNpNgGpjg6RJR1EhHK0tLpfWRX+g7q6VH7UJwr3b4zC4BBR+xH62YpLtuG25I74HsOxuRE8fzxw3l3xOm0rlvVxBFuhPFYsCbkX5fkdtzU7WJ61O/yn/wrv8lgdc5fJEbXoV+jatxQEt4CmncqPAkjxXsyaNu5GQ+8cwWJSZ6/KYTqIltJSRlffLmIo/p05qKTD3fbvqZeaCssKeGKz79gd34ebww7hdZ16/m8LxWf7C1cLrqVB075ScYOSV0Z2GwE13W5iwNT+pBRnMbW/M0UlPrvjtGKwr34xBjzn//pjTG9gSeBdOCxCi+9DRQC1xhj2ldo3wC40/nlq4Hqq0SmTUvWsd9B7T0eUHnK37mYuSWLbh2acd/oEyK6INPXTCy1lhumfMWSXdt5YdBw+jRv5VsH/CySszDccrBcs4SW9GnYnys738JhDQdQUJpPfFQ8jeKaBLGXwae8k1Bq17Iu5944jFufPx/jxexawb7TG6C0tIxWTVO44/IhtGvZ0GXbQOdhKMaH4+b/xpTlK7il7wCGdwr9MizKwaolxdThmi7n0b/xwaQXZfL1tjnc8ufTfLzpDZZnLdpr6YGKnx+U0pfz2l1JWtEuJqZ+wGeb36V1nXY1YuYTZZ1EioSSAs6+bjBXP3yG27ahOm96YLdWXH32UfTo3MJlu5qWhW//sZAvV6/ktsOP4pi2HXw7eATcjBBowRwLdkhqze7Cyou4ftw1g/l7fqR/44F7zfa8OmcFizJ+DVYX/UpZJzXB+WNO4NpH/+fxkqyhWH6uUWksYy46jkduONHtcqzhWHziq/u+/o4YY3hn+KnUiwttBqn45L9KbRkGw+mth9A4rsFe50DLx3x5JY7/aWKi3C+dGM5ClXcqPAkjq/5Yx8PnvUTH7i2589WLvVqnLVSDqIlT/2Dm7OVcdlo/jujtfjBREy+0gWM6ycs+m0x8dDRvnXAqdeOqMc2gik/2Eg4X3coDp0tyd4a3OIPru97NyJZnkhxTjz/Sf+bd9S/zwuqHSC/eA0CM8f+UaGFefDLLGPODMeYlY8yjxpgpwO9APHCKtXZreUPn1F23AA2BBcaYl40xzwJLgE7A09baX4L/LUg4++7jn9myfhfnjxnm0cU2by6y+XNA9esf6xn35g8cc1gXRp3e3237mpiJ+cUlXP7ZFLZkZ/HmCafQpUEj9xtVJkKmlqwJF92qUlkOtklsz1FNBnFdl7s5r90VtKvTkfW5q/l081s89tcdrMv9OyR9DSLlnYTMmsUbGHfLB/Qd1JPzbx7mfgMv+XOcWFJSxgMPTaGwqIRHbjiRxHjXJ2tq2gU3gDtnzOL3bak8fdxQevs6K2YEXHSL9BzMLsnl1j+fYPy6CTyw/CXeWj+RnYV72JhX9fJxPeodxEUdriG7JJOvt03m97R5/LpnDtO2TqBVnbY1ofhEWScRIS8rnw8emcyB/brQ+0jXM2xBaC64Pf30N+xMy2HstcOp6+YGQ2+z0FvBvuh251czWblnFy8cP5wO9X2cFTHMczDSM7Bcl+R23NT1Yg5I6VblDQhrc1YSHxXP4Y2O2ev5CKask4g3/tb3adSsPqdefozH2wR7RszsnAIefnIando04bZLj/fo2J4KxpjQ1/Hg+vR0rp7yJW3rpTBuyEhio3y4DF/LMzCQ+bcudzM/7vqN9kmtGNJ8AHVjkvbKvzrRyfSofxAAOcVZAetHkIQk71R4EmZ+m/EnL1z3Dn2O2Z/rnzgrIMfwd/HJUy/OYM2mXTxw9Qlup83yRSRcaANYm5bGVVO+ZOmuHRSVlvq8H0AX3PYRyotuidF1aFenIxe0v4orOo1hcPORlNoSZm7/glfWPMZ7G8axLvdv8ksdS5vVjanHVZ1v45AG1VjPNvJ8DtQFzgNuAg4AXge6W2t/3LextfZFYCSwHLgAuBzYDlxkrb05WJ2WyFFWWsYHD02kU4/WHDGkl0fbhGINU4DPpixg2jd/cvEphzO4335u2wf6BGN1+JqJ6fkFXDJhMpuyMnwbXJUL80FWuZpywrEyqfkpxEXF0zyhFae1Pp8rO9/Caa0vIDmmLr/tmcvb61/khb8fYmH6fHYX7QQg1sRxWuvzaVPHx7sbw5vyTkJq2vjv+PqdHzj7uiEcecKBHm8XipsUdu3JYezjU2nXsiG3XzbIbftwveDmaxYWlZZy9cQv2ZGby+n7VWPGtgjIwkjPwdT8HXyzfS65pfmU4fj/0GIrXXYgxsTSIrEVuwt3khCViKWMWBNHiS1h/p45TN3yCW2TOjK6060B628QKOskYnz91mx2pKZx0W0jgn5sT7IwJ7eQBx79giYNkrnriqF+nRUzWOdKq3NDwuiJUykuK2PMYe5vyqhSmOdgpGcgQIktpQzLqa0H0yy+UaU3IJTP8BzrvPO74ixgEUpZJxFvxa9rmDf9T06/ciApjT1/nwj2jJi//rGedz76iWEDenDK8a7HsOF4Q4KvOfjr5lTunDGT/q3bce0hR/i0j9qegYHMv89TZ/DbniWMbHUcp7YeTKO4FFLzU2gU14RDGhzBgSl9SCvazarsZQHrQ5CEJO8i4+p2LfPNuz/SqGUD2h/YgZjYaEqKPStiMOnZ2Ab+/ccYl1lMUX3Xd6gVFpZwz4OTufqqgeQVuF/XMT67jMK6ga95is2B4qpvVKpSTI6hJNm3P6Dnb9rM/E2bKUkuIzEmlvwSz9dCD6T8nHgSkwsDsu+M3ERSkgKzxExF23Pr0jzJvxeSXWkQW4/mCU04p90I2ie1oE50EtnFmXy9bRJrc1exIXfNP22jiKKMMhKj63B2u1F0St6PTsn7ER8Vz897Zgetz6FirX0Sx/Rc3mzzJfBlYHokNdEPE37hnDtO4YyrBvLzN0s82sbTXIzOyqe0nvs/lj3JRIBnx82iRZsGNGtUjaXXquBLtvmah9WxJSuLU975iJJkx6AxLiqaojIfijJzYiC5xC99ivQsDHYO1o1JonF8A85qO4LOyS2pH9uAorJCvtvxFWtzVrE8a9E/bctzMIooTmp1FgOaDKJf4+N4afUjrM9dHbQ+B5ryTsLByze+R7ue7bj64TP4ffZfFOa7H3+B/zPREwv/3MRb78/jzNMOpXnjemzf7b+7hYKZh76OD9Py8znzw0/YYap5m3kEZGFNzEFwFGC2Tsz45+sSW8zsnV/z8+7ZXNrxes5qexnJMRP4ZfcPFJUV8lvaPEpsCW3rdKRuTD2ySyLvDjllnUSS4qISPnhoImNeHcURQ3rxy4ylLtuHYny48u/tvPLmD1wz6jh6dW3FklVbXLb35pxpsLLQ1xzckpXFJRMmsbY4zett9xLmORis86KBsj43le93/MKZbYcztMVRTNnyLan5/JN/SdHJdK/fG4DMovTQddSPlHVSU7x5+wcc/sdjnHfTUF668zOPt/MkD/05Lnzvk5/Zr2dLbjj/GH5ZvI7tu6v+mz5Y1w694WsOTln+FwAzt61x09KFWp6BgRwDPrf6XWKjYhje4hgGNO7DprytNIlvSJP4FAB+2zMz4s9phirvVHgSpj58dArGGEyDFKKioygr9azaLxQnFAG278jknvsmUVQ/mihjKLOu34i9DRBfTxKGovgEoElpHT4680w+X7WM1xb/7ttO/BgqEPkX3IIl2kQxuNmRHNGoN52S25IQHc/anE0sy5zKoozfSC/a/U9bg8FiKXPe6XZuu8vpUa83i9LnExMVy5ltLyEhOpHvd04P4XckUjOUlVkev+gV0vK9q34PxcnFkpIybrn9U/I9zLlgZGKo8jAmJ4objuvHYS1bc/6Xn1NY6kOuhfkgq1xNuegWRRTHNTucIxr1pktye5JiEknN38Gve6ayMP0XthWk/tO2Yg4CnNL6XAY0GcTanFUkRCdwfde7GbfmyZpwh4BI2CguKmHsmc/QsHMbj4tOAsHTYswPP5vP1F9XsDsj123bcM5DX+3OzSOaKJo1T+bSAw/h4Z9/oNTNWLlSEZCFNWlMWJ5vAFvyG9Aq8d8LbaW2lLzSXF5a/SjXdLmTgU2Hsyp7GTsKtlJUVsjvafNYmD6forLA/L0hInv79qOf6HvioeRkevb+E4rx4cSpf/Dnss2s2LPHoz56I1i55uu4cPlOx6yICSkxnNP9AN5estC3uTLCPAcj+cIbwBdbv6NVYjNGtDiWWBPDtG2zSc2Hg1Li6V6vNwel9CW9aDd/Zy8PWB9ExHtb1+7gkyenkpETmJufPclCT3LQWnjk0WkccER7l0Un5QJZhBns86PlxSfx9aPp1aQ5C7a7LkCtlJ+vE0aaQGbgk6ve5JRWgzikQU8OSNkPA/yZsZK1OQv4cdeMgByzNgiv0jHZi7WWhs3q8eL0mzl8sOfT84ZiKuVy9QqieO6O0zhneB+3bQM9dVZ1VWfZnYz8Albu2cUdRxzNad16+N4JPy+5o6UG3Is20Zzc6nh61O/Ciqw1vLthCnctfZb3Nv5MetHuvdYxLT8ZGWNiOKvtpfSqfwi/7pnDhM3vMHHz+/y6Zw4ntTqbE1qcFvB+i9QGqxetJ32n93eOhiIXS8sscZmlHNitFc/dfhoJ8a7fz4ORiaFYhg7g7y276dO8FS8cP5wod/NLVyXMp5csVxOyMC46lrPbjqB3yv6szd3EJ5u+4o4lT/Haull7FZ3A3lMsj2x5Jkc1GczyzEV8uvktPt74Bmtz/uaqzrfR07k2qoj4R9r2TNbMcxR09T2+B1HRng3rQ5GH1kLWRkd2nz74IFLqun6fDNc8rG4WHtm4HZcccAh39zvG951EQBZG8nTLFZXnW79GB9M6sTmp+Sl7jQPLC1O+3fElKXENOa7pCf+8VmpLVXQiEkRlpWWM/d8zLJ1fjbuJq+BpHnpizTpHAcahPdvSqU1jl23DcakBqF4WnthqP+7tfxy3HX6Uz/vwJy058F+vrP2IubsXMLj5kTx2wM081PMGLu90JyNbnUm0ieaXPT+yNndVQPsgIt57/+HJfPX+T15vF+wld7JzCvhp1koA2rdq6Pv5wUpEQg7e0ncAH448gz7NW/m2Az+NBSMx/yCwGTh5yyweXPESYxY/yo2LH+GJVW/w4aZfA3a82kCFJ2Eue+M2igqLuf3lC9nv4PZ+37+/i08KCovJzi3gqrMHcEj3Nm7bh+uJxXK+hokFbvtyBnM3b+DxY4YwuH1n3zuh4pO9BHqgVVRWzPWLH+aJlW/w8F+vMnXrdxTbEgyG1PyU/6xjajD0a3wcfRr2I6MojYXp88krzSW9eA+zdnzJ3F2zGNL8ZE5pdW5A+y1SW9SNKuGJz6/lmJMPCXVXPFKvNJo+Pdty3+gT/LqmN0RO8cn0VX8z9rvZDOnYhbEDBvq8n0i44BYsgczCgtJCblr8CM/9/S4PrXiFz1K/Ia+0YK8Lbvvq1+hYBjYbwZ6iXczZNYsdBVvZnL+BaVs/Y2XWUkZ1vImDGxwesD6L1FZdOzXk/rcv57K7T/J4m1DdpNAhqR7XnHMUD1zjvggxXG9QqE4WTly2nLcW/MHFBxzCeT16+94JZWHQik+aJzThtNaDObvtcAA259f/57XyMWFq3gZySrJpltAiKH0Skaol1UvkjNEDiYmNdtvWm4ttnuShp1mYnAd3Xj6Ex8ecRL3kBJdtvc1CbwV7XDh5+QreX7iYKw86jAt7+liU7ufzo4EQ6cUnL6x+j/c2TGF9biqdk9vROK4Bq7NX8MWWT5ixfUpAjy0i1ZCRwZCzDmf/Q9p7tZkneRiIceG7j5zPqDP6uWwXrkWYvho35zdSs7MYP+xk2tdP8W0ntbz4JJCKyorZmLeVTXnbKCjd+yaCZvEa63lLhSdhrjCviHtPfpLd2zJ54J3L6da7nUfb+bNisZyn4fHEk1+zcWsaY68bQfPG9dy2D+cLbeD7oKqotJSrJ37J0l3beXHwCPq2aO17J1R8spdAD7QKSgv5Ne3PvZ775+Rifsp/nt+ct4HJqR+SHFuXY5sOo1tdxwxFuwq3893O6Xy/czpd6u5P60TP/v0GWqGNZV1RU48eIuEma08OyfXqcMHNJ1An2fP3slBdaPv1j/W88sZsjjmsC6NO7++2fThnYnUuuL2/aDGvzv+Nc3v05tpDqlGAEAEX3II12ApkFmYW5zB39wJK7b//P1rsfzKwXE5JFj/t/o5GcU3olXIIrRLbArA+92++2vY5K7L+ZETL/wV9sKa8k5pu5e9rmfzKDE657BhOGXWMx9uFIhO3bE3nuXGzOKxXOy473fVJRqh5F9wAHvthDrM2rOH+I4/j6Dbtfd5PuGdhTRgPAqQXZbI6ZwM963dlULN//4aL4t+L2q3rtCc5pi71YhsQa+IC3qfKKOtEHLp1bcwld47khHPdZwz4/7ypJ1lYVFzKfQ9PoXFKMg9ffyLRbmYs8yYLI2FcOPb72cxcv5r7jjyOIR26+HbwMM/AYAh0Bk7bNpuHV4zj5j8f4/rFD/HgineZu3tWQI/pjrJOxLXY+FjOu2kY1z1+FvUaJPl9//6cAWzL1nRmfreci04+nCMP7uSybTgWn/iagZkFBYz6bDJl1vL2CafRIMHHMVMtLj4J1g0I5VLzUzi80dHc0f2xf673BVpNyTsVnkSAzN3Z3DH8UXIy83hswjUccIRns2f4+4QieDaQys8v4p4HJhMTHcUTY04iMd792t/hfKENfA+UvOJiLpswhW/WrWZdRprvHYCIKj4JhmAHTUX7Xnhbn/s3P+z6hnFrnqBjcleGtTiFJvHNAEgv2s2327/k/Q2vkZq/MQS9FalZrLW8fMPbNG3VgDHPnofxYmrGUBWffP7FH0z75k8uPuVwjj+im0fbeCOYVfrVueD21Nx5fPLnEjZkplevExFwsrEmFJ9UpbLikyWZfzBh8zt8vvk9jmw8kIHNRlAv1tFuU946pmz5iImb32dH4bbgdlakFhh/xyfM+2oxl997CpfcOdKrXAy26TOXMm3GEi4+5XAGHOL6JCPUvAtuZdYyZsrXrNqzi6sO7uvbwQMgEk86QuAzsLCsiE83f01aUQYntzqe/o0PJjU/hTJKAagf24Du9XoDsCh9PsW2KKD9ERHXFsxcwsI5q7jgluE0aOLZ+0Moxocr/97O0y/OoE+Ptlx/3jFu24drFvqizFpumvI1i3Zs5d7+xxIf7X52mkqF+XiwJmRgiS0lNX8HW/N3UlRW/M8YsHlCK5on+LhMhIgETGF+EU9f8Tot2zXm8c+upUFT9zeDl/NnFnp6nvT5V7/lr3XbuXf0UFo1re+ybTjekODreHBTRiZXTvqClsl1eeLYIT7tw58iMQODfR70q22r2J6/hUs7Xq/884IKTyLEjo27ueGY+/nlmyVsWOndSfuERPd33vizahEgdWs6Dzw2laTEeJo0TPbrvqsj2FNogaOaccyUr9mVn0dMVBTt6qX4vrMIKT6pyRfcABKi4yu98LYmZyXvbniFDkldOazhv+vW5pbmsK1gcxB7KFKzLZ23ivF3fkK/oQdw5rWDvNo2VMUnz46bxaK/UunXu4PbtoEeVEFoijEB7p75LV+ucawLnRTrvjC1SmF+shFqdhbuKKh8Xfq5u2cxZctHHNLgCPar26tC+60sz1oE4HLJHhHxXmlJKQ+f9SxTX53FaVccS7febT3aLlR5+Py4WaxYu527Lh9CHQ/Gqd6IhOKTvOJiLvtsCpdMn+TbgctpuQEg8BmYXpTJI3+9RlxULJd0OI1LO5xBQvQBHNZwAMNbnM5RTQaxp2gXq7P/Cmg/RMQzL137JnHxsYy652S/79uf501nfL+cTyb9xhlDDqJPD89y21PhvvxcQUkJV3z2BedMnUBhaanvHQjz8WBNyMB9bc1vwCUdrmd051upH9sgqMcWEfcWfreMu095imatG/LUxOto2srzf6f+nAXM0xnA7h87BWstj9wwkvhY/72nh3sOLtq6jeu//IrHfpnj+8FreQYGM//ySwu4f/l4isoKubLTzSTHeF7UVZup8CSCZO7K5rHzXiArPdfjbfr37cArn19D245N3Lb190nF3xdu4IJRb7Bpm2d3NgdrLe9QTKvs2D6Ke/sfy6RTz6FTSkPfd6Tik70Ee6B1YstjOa5p5ctEGAwbc9eQWZzO4Y2Oom5MPV1gEwmQyS/P4LtPfuKoEw8iNt6798VAzAjmTklJGXfc8zljX/3Go/bhPhNYdcTkRHF8+07MOXcU+zWsvIDBI2E+0IKamYVDmw+gT8NelRZgGgzLMxdTXFbE8c1OJCEq8T85WL50nYj4T1mZ5eUx73PdUQ+wcpHnM+yZ9GwGntiblIaup2P25zixqLiU+8ZO5s7nvyQv3/0MEeGch76OD3fl5lKYXkpCTAx3HnE0ybE+FuAoB4HAZ+DOwj3cufQZtuXvYliLo3j8gFs4t93l9G10FDsKtvLNtkmszF4a0D6IiGe2rNnOhGemcewpfejdv6tH23hzsc2fd3u/9vaP3PPiNBYs3+S2bTguNQC+52BGQQFbtmYBcEXvQ2mR5OP7eJjnYE3IwIrKsDy56j3qRCcxutMtJEQFZ6wrIp77c85f3D78MZLr16HbQe282takZxOfEMuBh3Wsso0/x4Xbd2bx0BPTmL9kAyVuChFrWg5+u2YtG1MzAOjT3MdZNGp5BgYz/3YXpfPQitepG1ufUR1v1PU+D6jwJAKVpXlWyHH+mGHc/Nx5NG+Vwl1Pn+3RNv4uPikuKSUhu4ybLjyO8088zG37ml588u4viyizlg9OPIPWdV1PI+aSik/2EqygqRuTROfkdpzV5gT2r9vpPxfdLJbskizySnJIikkmJipOF9hEAuj5a95mzMnPUVxYEtJ+eJqJ+flFxGaU0qJJPe66Yghxsa6nFq6JF9vKrdywi6LSUt4efhrNk8JjZrRIz8JgSImtx6ENe3FBu5NoFt+o0hzcWbiNtKI9JEQnYp3/iUhwrF603uOxYv1Gydzw5NmMGXsqtz32P7ft/TlO3Lkrm2W/bABgvw7N3LavqXnYO6kFF/c6mNeHnVxjlxuAmnHhbVdhGo/+9RpPrBzP1K3fM3fXAt5Y9xkfbnyN39LmBfTYIuKdT576kpkTfmXXVs+X9/Tnnd7gWRaWlVnmfLMCgNbNU2jWyPX7WE276AbQNqo+Vx98OO+MOI16cT5mUJjnYE3IwIrW56by2Mo3aJ7Yiss63kC08fHvFxEJmJW/r+WiHmOYO20xAHEJns023KpjUx574xIeff0ieh7Svsp2/hwX/vrHet587QdKyyzR0a4vVYdrDlbHGe168vkpZ3NK1+6+7aCWZ2Aw829NziaeXfUuP+6aofOcHlDhSYRydUKxWZuG3P/WKM66bjBZaTl88dYcGjVO5rbHzvBo3/4uPrHWUjcpnqvOHsDQI/d3274mF59szMjgwk8nkhgTw4cnnkHTOq7vLnRJxSd7CUbQZJfk8tPuhZRZy8hWx9G2Tou9LroZDF3r9iAlrhFpRbvJKc4MeJ9EarPC/CJyN28nPiGWi24bQWKS5+9joVpiAGC/xo0YcXRPbr9ssNu2NfVi2/acHC77fDJJsXG8M/w06saF/m5viOwsDEYOZhRn8VvaUurGJnNSq+NpEFef1PyUf6r9DYb96vaiUXxjymwJZTbwy0aJyH+5Kz45fHBPnvz8Ogb97zB2pqZz4GEdOPOyo93u19+ZeFDLZrw59lwuPMn/NyhEggVbtnDb1zPo16otzx0/nCjjY66G+QlHqBk5mFuaz29pS3h3w2ReWP0eX2+fw0970gJ6TBHxXlFBMU9f/DJb1u/yartQjQ8TcyzP3346T4w5mQQ3M3kGOguDPS7cmpXNVVOm0qF+A8bX4CLMmnQjAsCfGSt5afWHdKvXk+OaDg91d0SkErmZeZSlpdPjsI70OLTqGUzKHTawBw9/cCXderaisKCY+58/l4ZNqv7b2p8zRAN0adeET5++mG4dmrpsF445WJ1zo9P+WskvWzbx+DFDOKxFa992UsszMJjFJ/PT/mTq1pUANIhtFLTjRiIVnkSwyk4o9ji0I49+fDWHDuzOwjmruGHks7z+wGQmj/+Bo4f24oQzDvVrHzwZSFkLTz45nQXLNnLX5UM4tKf7ab6CVXziq+oEyt+7d3PJZ5NplFiH14aeVL2O6ILbXoIRNL+lLeGz1G84tOEBnNlmON3rdf7nolvbOh04svFAEqPrsCp7ORY09ZZIEHRsl8Lpowdy09PneLVdqE4u/jR/DW+8N5dhA7pz7gj3uVxTi09W7drN1VOm0imlIa8OOYnYKB//LFUW/iMYOThj+1zm7PqN45v146SWA2kW34jN+Y5Z3JoltKRPw37EmFiWZi6ixBYrB0VCpLKxYmx8DKMfPI27X7+U5m0aMfHV77nr3HFM/+BnLrxmIO06uz7R52/L/9rKrF9WcuWZAxh4eDe37b3Jw0jJwql/rWTsd7MZ1rErDw043uf9hPsJR6g5OQiO5QbKld+I0Dl5f2KMf/8mERHfNWxWj9tfuZAW7Ty/MBCK8WFpaRnPvTSTTm0bc8+VQ922r2lZOH/TZm6Z/g19W7bh2YE1twizJl14A/hh1288+tdrzN45PajHFRHvrPz+TxbNXeWyzRmjB3LbSxfQuEUKHz7zDQ+NepPM9FzGvnxBtY/v6XnSjA2ZREdF8cj1I6mXlOCybTjmoK8ZWFxWxtUTv2RzVgavDz2JDvUb+LQfZWBwMzA55mDu6fE0fRr2D+pxI4kKT2qYmNholv66FoDvJ/5Oxm7HoOmHLxYy96vFXH3niR6dUPSmatGTACkpKePeByazfsseHr3hRLq0a+J2m3C+0AbVO8G4ZPt2Lp84hfvmfu97B8rpgtteghE0X237gbfWf86hDXtxS7dLubXbZZzX/lYuaH81B6Ycyua89czf8yMltlhTb4kEwfJf/ubNuz/hyOG9OeMq7y7amPRsDj6is9t2/i4+ef/TX5j1y0quOmsA/Q9yf/dBOGdidfLwl02buWPGTBbv3EZxWTXuXFAW/iMYOfj6ugnM3jmfES2P5Y79r+DstiM4ocXlnN32Mg5teCTbClJZmD5fS+2IhJleh3emQ/eWGAMfPPs1bz36JVs37OLHqQvZ+Pd27nziTLf78HcePvnEdBavTOWeK4dyQNeWbtuH44lGqF4WvrtwEa/88iuDO3ap3vJzYX7CEWpODu6rxHbmmi53cEH7q1RwKRImbEYWhx7TndFjT/dqO38Xn3ji1z/W8+pbP3Jc325cfMrhftsvBP9cqS++WrmKh76fzaD2nTigifsl+IJBF97c+y1tCRvykqkTnUTvFPez14lI8BUXlbicDXPkxUdx/s0nEJ8Qy8t3fcZHz89g4ZxVfPbOPFq1a+RyBQV/jgszs/K57+EpNGmYzL1XDcNdDWI4jgl9HQ9mFRZy2WdTKLGWN4adTEyY3JQXaYK77M5G1uf+zTltR9ExqWvQjhtJVHgS4fYNjj9/Xs1Ld3zK79//xeX3ncKxJx8CwNYNu/jy7bls+ns7D71yoUf79veUWbl5Rdx+z+fszsilaUPP3gjC+UIbVO8E42+pqaxYvxOAEzt3IzHGs/X2KhUhwVKTik+mb/uRx1e+zobcLRyYsh99GvSkbmwKv6XNZWLqB2zOWx/wPojIvya+8A0/fPEHF902nEOO2c/j7c4YPZCHxl3AtXePdNvW7xfbnpzOqg07OO9E/85GVi5SLrhNWf4Xz373M4Dva3uDik8qCEYOjlv7MR9s/IJoE83prYcwpPkA2id1ZkXWn0xJ/Yj1uX8HvA8i4tq+Y8VFc1bx1A0fkrpmJ32O3Z8ehzkKH5f9upbJ43+gRZuGXDbG/d3W/szD4pJS7rl/Ejv2ZPH4mJNJqevf98hIycJn5v3EiLffY3tuCBYXr0Ik52CwbS/YzXsbpnBQg76c3saz8y0iElh7tqXz7oOfc+ix3TnyhAO92tbT4hNPeDo2nDD5d775dhmXn9Gfw3q5nik6XGeJrk4OvvPHIoa9/S6Ld273vQMRcG60phWfAAxtfgoXd7iWA1P6BP3YIuKZqopPls5fw4fPfoOJMsTXiadhs3oAzJswnwlvzeXoob044+IBVe7Xn+PClX9v58XXvqP/QR258KS+Hu3XU+FefLI5M5PRk77goZ9/pCQMbsqLxOLLYCqxpYxd8Q5pRbsY1emmUHcnLKnwJEIZY+h9THcSk/879VRxUSkPX/kWhflFHH9GXxo1d0x/vvz3dUx6/Xs2rtxKg0ae3Unl74tsu/fkcPGVb/HTonXO78OjzbwSScUnAN3iGvPcwOG8NvQk4qJ8XM8UIuION6hZxSd/pC/nqVVvcuPiR7l/+UuMWfwYz/49WRfbRELkmcvGsWHlNq4aezpR0e7/xDnvpmFcdPsICnKLGHZ6H66+60S/9cWTXCwsLOHOeydy0+OTPNpnoNcyLReKC24AnWIa8t3Zl3D2/gf4vhMVn/wjGDk4Zcu3jF3xCvcue55X1nzEnUuf4alVn7Aye2nAjy0irnU6sB1J9evs9Zy1lp2paTxy1Tvsf3AHBp3Rl3oNkgCYP3Mp33z0C6ee34/2XdzfbezPcWJWdgG33/M54z/7iYxs9/sN55sTqpOFu3PzALil75Gc1GV/33YSITkYaKG48DZ16/dMTp3FUU0GMaT5yUE/voj819TXvmXtslSueOBUEpP8/37m73OmT780g/Gf/8yfK7e4bRvoLAxFDm5IzwBgWMeunNq1u287iYBzozWt+OTVdTPYmLuWC9tfrTu/RcJYWVo6Zp+Lcev/2sqnL81i4qvfM+qekzjqxIOJjY8hNyufWe/O4fuv/uSia4+nifP6YmX8mYVfTF/MzO+X071TC7/OegLhX4S5aOs25q7YAEDPxtWY/asWF58EM/9ySvK4f/nrmuuyCio8iVD79+3M9S9ezIBTD/tPxWJUlKG4sISvP/qFA/t3oXnbf9cz/eGLhdxz/qtkrNnm8bH8PZAqLS0jLrOUE47qwYt3nkF8rOs3Q18utEVS8cnatDTunDGLo9q058VBI3yfTsvPVHzimdzSfHYW7mFp5iq2FeyiqKz4n3W+DYbE6DqudxAChWUxbMxv7NFDJJIU5hVx/+lPc9e54ygrdZ0dl919EmdfP5jtm/bwxPXv8/7TXzP8jEO55IbBLrfz92xgaem5lO4sIDE+lktOOZxoNwUz4TqwKledPEzNzGT57p2MPep4jmnbwfdORMhFt5pSfLKzcA/Ls9bw3c5fWJW9nszinH9yMCEqkSiqUVRbTco7qa26HdKRm18bxcHH9fjPCUZjDBtXbWPae/M4fFBP6jV0FJ5kZ+Tx9Uc/88R177Nh9Q6PjuPPceKWbRlMm7gQgHYtGxIb4/q9o6YWnyTmxXBws5Y8dexQjm7T3red6I5vIDTFJ+9tnML3O+YzouUZdAjSxTdlnUjVykrLeP7qN2jYtB5nXjPIq239veSOJ1lYVFzKB2/Po7C4hKTEOOpVcrNhRTWx+CQmJ4qz9u/FE2GSg5E6FgxmBhaVFXPf8vGkFe3m8k5jaJbgfulEbynrRKonKjqKI08+lJi8yt/Y33r0S5b9upYhZx1OfeeNCbu3ZTDhzTnccN5r7Nqe6XL//jxX+uQL33DbM1OwHqzaHOgb9EKRgwcmNeeL087lxkP7+bwPf1HxiZtjFezitiXP+nWfNSXvwuMKt3gtLzufqOgo+p94CHUb7j17SVmZ4105Kz0XY6BX307/vFZSXPrP595MHenv4hOA0vRCDtq/DfddPYwoNyWMNb34ZOKy5Tz43WyGdOzCk8cO9b1SLkIutkHNKj6pTGp+Cme0uYjru9xNnehqrNUuIl7ZsXE3Wxevcdlm1L0nc8qoY9i6YTePXf0uv85axveTFjD1rTmcftGRnHP5MS63D0Qm9uvUhlFn9OfGC4512zacL7aB73lYai3XT5rGyj27eHnwiRzQpLnvnYiQPKypF94Athc05rqud3NB+9EY3QMgElSlpaXUb1KPo07tS2xcDLbCWbvyz7eu30XdBnU4sP+/F8c3rNzGj18sDMg40VNNieeNB8/hjlGuC0EhvIsxfc3C4rIyRn8+lW7+D+QAAQAASURBVFVpuxk35CQObtbCtw7oohsQmgx8Ze2HvLXuBc2CKRImVi1YxxOXvcbE1773etvyPOy8v+sL6f4eHyZkl/Hy3f/j0RtGhvzGhFBcdLt+8les2rOLV4aM9H1MGAE5GGjBzMDsklzuXfYqpbaE/7W5OGjHFRHP9OzXlaueOo+hFx1d6U3sAD9/s5S2XZrRe0C3f17b/Mc6Vq/Y6rd+eFqEGZtRStOGdbnlkoF+vSEhEm5GWL5jJxOXLef6Pv04rVsP3w4e5hlYk4pPtuR7dtNObaPCkwi1YXkqcyb+Sq/+3TjqlEP/CQxjDNExjl9ri3aOqqfsjLwq9xPK4pPZ81bx0vjvOfawrlx//jFu20dC8Ul1vLdwEU/PnccpXbtzeMs2vu8oQi62Qc0vPvl2xwqaJrTgui53khxTLyR9EKmtqlq/tH6jZHIy89i2cQ9lJY5ciY6JYmdqGl+8PYfpn//OeVcdx+kXHely//7OxLm/rOajz3/ltEG9OX3wQW7b19Tik9ziYkZ9NoU9+Xm8f+LpdG7QyP1GVYmQPKypF95KbAnf71zEIQ2P4Jx2o1R8IhJEaxZv5I9vl9LjiC4cOuTAvTKxfKxYPtNJUYHnRZLV4WkeZmTm8cnnvzFsQHdGne7+Lq9wveAGvmdhTlERl06YzI7cHN4efhr7NfTxbqYwP+EINTMDS20ZX25bBUCLhDZ0q+vjCWMR8ZvZn/5CdkYedZLj6XFoR6+2Pe6Ybrzw8ZWMvn14gHr3X2Vlls8m/s7B3dtw04X+vzHBW8G+6JZTVMSln00mLT+ft4afSrt6Kb51IMxzsKZl4M7CPdy37BXeXf9y0I4pIp5J255BXnYBRww/mGbtGu81Piy/ib2o0DFeq7/PTe6hmAEMoEezxpx6fG9uvnig27Y1rfjk3lnfMS91I48ePZgjWvl4nbCWZ2CorgeKgwpPItib90xg95Y0Tr5qCEee1IeytHSstZSWlNG1d1sGDO9NdkYey39f53I/3hSfeMrTAJk49Q8+nfQ7/xt6MOeO6OO2fbgXn1Snoh9g3PzfOOm9D/hl6+Zq7SdSLrZBzS4+WZK5iodWvEqThOZc1+Uu6sWkBL0PIrVZZcUnmXty+OSFWTxwyXji68Rx+8sXctCRjmr+7Zv2MPnFWfz4zVK3d7WB/wdVr7/zIz/+vpobLjiGI3q7X2om3ItPfLUrN5fzP/6cGetWsznL9XSebkXAcgNQ8046lpuy5Vs+3vQVhzc6mjPaXBj044vUZm/dO4H4xDhOuOQYOvZq808mlpaUUb9RMgf27wI4sq8yobxB4f1Pf+GrmUu45NQjGH60+4v2NbH4ZE9eHhd9OpHMwgLapzTwvQNhfsIRam4GpuancFrr87i80xg6BmnZHRGpWllaOhfdfiKPfnI1R490X+gPMPz8/lz32P+wZZYTz+rLqDFDq2zr7yycNXsFH33+K6ce35tTjz/Qo208FQnjwt25eVw8YSJRwLCO1XgPDfMcrGkZuCFvCyuyIYoohjQ/OWjHFRHXUldvZ/pbs+l+eBeOP7s/8Ylx/1mStYPzPGh+buF/tg9F8clP89fw3ic/M/LYXpx9wiFu24dr8YkvSsrKuHbSNNZnpjN+6Cm0TPbxfbyWZ6CKT0JHhScR7qHzX6JuwyRufv1yrnzyXP53yZGcP2YYz0y5kaatG7Bo7ip2bE5zux9/h4c3xr01m1m/rCQu1rM3wkBX8pcLVfHJ8h07icmJ4ohWbbjkgIN935GKT/4jVMUnDy5/hYZxjbmy8y018o5vY8xFxhjr5lFaoX17N20/CeX3IzVLVHbWf56z1rJ5zQ7uOOslkuolMvwCx6ALYOuGXYy/53Meu22CR/v356DKWnjk0Wms2biLmy441u20yhDexSfVycMtWVncOW0WhaUl1I2Lo1NKQ5/3Fe4DrXI17aRjuQmbpzMpdSYDmgxicLORQT++vyjrJNKk78hk3C0fcNCxPRjz2uX0GdSLHod25OCj9+PhD0fT49COLPllDWuXp1a5j1AWnzz90kx+XbKB2y8dRPdO1Vh6zU9CUXyyJSuLoW+8yzfrVgO4XZ62ShGQgzU1Ax9d+T7pRXu4svMttElsH/Tje0tZJzXd23d8yMqFG7j95Ys4+Oj9XLY9Y/RARo89ncKCYl68YwKfjP+RU87v53JZVn9n4fh35/DTwrXceOFxHNzd9R3P4TourM6YcEN6BsPffp9XF//m8z6AsM/BmpiBcdE9OaZp1YVaoaa8k9po0ovfsPD7ZZx67VBOuXoI8O8yrEPPPoLh5/cnc0+u25vY3fFnFr71wTy+m7+K6847huP6ui9CDMfiE19zMLuwkIs/mcgTv85la041Jg6o5RlYm4tPQpl1kXEbqFRp08qtPHHpq5x584mcdOWgvV6b9u48xt070eN9mfRsbAP3/xCjs/Ipref+DSEus5ii+rFu21kLDz88lcJ6jvXaoqOjKC11HRLx2WUU1vW8bio2B4qT3bfz13YxOYaSZOu+oQunde3B6fv1JL+4hI//WuLbTnJiILmkWv2oKD8nnsTk/1a9+sP/2TvL6KiuLoDul4m7IMHd3d2d4u7QFigtFFrcrUBpC7SF2teWtrS4u7u7e9AkECHuNvO+H5OgISOZjCR3r5UVyLxz5xHg7Zx7zz03ItYBdyfDFza9TWCsC95Ohu/ykxG3oh4w+9bP5HdIRCZz/y7MlKvAnPe81ghoDuxJ57VrwNZ0vn7TIHclyPE4uTnScVgLNv64G5WLK5IkvUyqrBRWBDwNZd+as/T4tDnVGpXm7H71P73w4Cgk0MqJuqCNFxMSk5k6axOyh41GF6ZhDCeayofWMVYs6t6OGvkK0GfbOh6Ep78z35hYugtN4cH/nm4jNiWe+9Enjfq+BuYqwnUCC+Pg6lPkLuBJl5Ft+GrzuDdee3DDn19nbCQuOiHDMbTNEcGweaJSqWL23K1071ub+0+CNY5pzi7MDElKJdYxVrSsXILRNerRf8cGwhP08IQB88Ks8mB2dGBkcgzTbvzMN5W/4LNSk/nx/lcEJjwz2vvrwVWE6wTZmNjIOKa0/5puE7pw7dT9917Xd0xrBoxtR1hwFD9MWMOlo3fxLuyFu6czAz5rTkx0AtvXnE031pAuVKlk5i3YwegxrfELSP8o2dcxVxdmJid8ERuLNVaULuLFx1VqMunoPlJUemxINHMPZjcHPojxZdjFWUZ5Lz25ivCdIAfyVb9lLNo/jcGzutOoW23CgyKwsrOlWiN1J+jt/xzn6b2AdGOzIi/UhCzDwoU7yfWNMwM71eboeR9UsuHWVoyV3+nrweDYWFafvQbOUMYzF7HJyfhH69EZOoc70BTzoGbCVUzkOlF4kg24fPgWj2/5U75OKUpULkxiQhI+919w9eT7k6jMkhXFJ7aRSkpUy8/czzsweck2HvqFZBiT3YtPpu86iIe9A/ObtCI2OYntD+7qN5AoPnkHU8jmbvQj7kZDQQeo6dmAxzH3CU16YdR7yCpkWb6KWmTvIEnSmdRf/p7Oy1dlWZ6dNXclEEDZWiXoM6EjTm4OLJ+xHivPV63qValFHYG+atcULJ7nnXhDF2SCdl4MCY2BUMBNQd/2Ndh17BZRsRkvDJrrJCNk3offHT7B6r69WNOpF322reNhhOZObu8gXPgGpvDg5mf7ASjkIFHetQq3oq4a9f0zi3CdwFJZt3gn147foc3gJuQp5EVyUgq3r/qx/ueDWo9hquKTuPgk/vvrJCluCtxdHLBWWBESEfve67OzC6PCEinu7sE/H3Sn//b1xCQn6T6ImU84QvZ0YGhSBNNv/sTCSl/QKm9H/nv6m9HeW1eE6wQ5geTEZNbN2wDwRn6YhpunE9Y21rx4Fk5ifDIhAeoFnkDfUDb/tB9be2tGTGpPYkIy+7ZcSvc9DO3ChQt3kuSmwEqSsLFRkJj0/me5ubowsx6s6JyX7mXUx++NO5zeGokWmLkHs5sDE5RZky8bAuE7QU5FmaLkq35L6fFFe+q2q0rxlpVIiEsiJCCSFd/u5PDmixnG65IXaoM2HkxKVjJ91iYSXay0KjrJag+aIh+0i1Xwv36dAei5dS0v4t6fE7+XHO7AnFh8YkrXiaN2sgnhQZGc2n6Rf+dtZt2inVzefk6vcUzZThkgyi8aOxsFSyZ2I7en5ie4MVpJZiYuMy0lU1QqRm/Zxfnn/ixu3o7mRYrrPZY4duddTNVmKywpH90KDGBM6RnktstrknswFpIkVQLqAs+AXSa+HUEO5PFNXx5ee0rttlWp2boyqrBXu8QU1uofgdxyuaT+XpGp98qKo+hKubozondDFk3sir2d4WuFLeXYnSfhEQxcuxGA1Z16Uczt3QlirbAgFxoDU3mwvNsHjCg5gaa5zbf1si4I1wnMHZVK5va5B3z/2XKmdPyWhUN+0anoJA1d8kRDYxel5IfJ3Vk8sRuO9hlPTJrrUQOQORee9/fn8207Ke+Vmz/adcFOoafTzLzVMmTPIwcCE0KYdP171vj+adT3NRTCdYLsyuv5YRqRYbGs+n4vc4cux9ndkSk/D6Z6Y/VO8GePgtnw1wkunLhPy07VXuaU6WHoOVPbSCULvujIvNEdNB69Zqy5Ul3JjAc337rN9ydP0b1MBSbWaaT/TZi5B7OjAy0J4TtBTiAsMIK/Z65nfJv5zOrxPTO7L2ZCj6Uai07S0DYvNKQHo6ITSHweh62Ngi8GNsXD1THD67Pag8bOB5WyzLgde8jt6MS/HXrgZmev3w0YeF7U0Ihjd4yDMVwnCk+yMeklUNpgyuKTFyHRTJyxEScHW76f2A1nR80/xGfn4pPElBRGbNzG7dAXtCxaQu9xAItacMvOxSdxynhm3FyGtWTDmNIzyGuf3+j3YESGp35eLsuyMp3X80uS9IkkSVNTP1c25s0Jsj9hgZH8MW0tXvk86DGmHRXrl37pRmWKCu/CXrTqWRtZJfPgpl+6Y5jSiU/9Qpn7zQ7Kl/BmwZhOWCsy/rFNVx+C5Sy4PQwLY9DajVhbWfFds0wULFiIC7OzBw8EnuZK+Hm6FxpIg1zNjf7+WYBwncAisLJSP4PjohOyPE80tA9lGf78+zjFC+Vi3uiOKKxMv+BmChceefSYiXv2USd/IX5q3QGFhoXH92Lmi26QPRfeghJDeRLnjKPCiYFFPjXqexsA4TpBtkWKinrnayqlike3nzGlz094ebvRrl997B1tAfC9+JBfF+5i6vC/UaboceRLOmjrw8sXHtOwegk+6d1Q47X65Ia6YAoP/nzmHKuvXuOz6nUYVLGa3uOYuweNlQsK0kX4TpAjSElWEhYYyfl917hx8h7B/rp1FjZVXljc2Y3OzSuzaEIXjRv0spsHrwUEMmLzNoq7e/B3+244Wms+ZSJdDORAS92MJ4pPACO4ziIKTyRJaitJ0j1Jkh5IkjQ5ndcbS5J0WZKkFEmSerz12reSJN2SJOmOJElLJUnf2RnLxDYxjsm/DKZm03I6xWXFjjZtBfLoyQumz9tC4fyeLPyyEzZa7ELPzsUnMUlJDF6zkWnHDug9xqvBLGPBDbL3otvTuOdMu7EUCYkxpaaTz76Q0e8hq5EkyQEYACiB923tawX8BsxP/XxNkqQjkiQVNs5dmhfCdVnD3QsP+W7Y/6jUoAxjln1E7/EdqN+oBB8MbMDCtSPxLuzFpWN3uXH24XvHMGXxyYkzPixetp96VYsxY0RbNP3NZnVilYYpfOgTGsrAtRv54lAmi7EtxIXZ1YMqVCy4s5KbkVfoU/hjantmYseiiRGu0w/hO9OgUr3Z1lcVFo6zmwNVG5TWaRxTTTKev/SY739W+3D8hy00Xp9di0923LnL7IOHeBQRjjIz55ub+aIbZM/iEwCZUjyJ9TH6++qLcJ1+CNdZBo4u9nQf3RZFjNptr3+rrawkfH2C2LvmDPXbVaZC7VddiINu+pKcnN48/ZsYuivm1l1X2bbrCoM61aZNA83zvLq40FI8OPvgYfY98qFZ4WJ6jwGYvQfFrm/jI3ynO8J1lossv5sb6oop8sJHT14w55sdlCmWlzkjPzBoBzBL8OAZXz++2LGbKnm8+bR6bf3eHMy6+EQUX2YtxnKd2ReeSJKkAH4G2gHlgb6SJJV/6zJfYAiw+q3Y+kADoDJQEagFNMniWzYrZJVMweJ5mLhsIHkLeeoUa2h5gA5V/Nd8Wfj9HiJj4jUKRF8srfhEEWNFQRdXtnbrTzmv3HqPZSkLbpB9F90A/OIDmHLjR1JkJaVcdCsMsxB6Ae7AXlmW324lEQd8BdQAPFI/mgBHgKbAIUmSnIx2p2aAcF3Wcm7PVeb2WYpKpWLIrB5MWzmKz+b1IHcBDy4cvsNXw5aTlJCxn0xZfLJr/3X+988xmtUpTemieTReb66tldPIjA/vh4QQGBCDBIyt1YACzq76DWQhLsyuHkyRlcy99Q93o27Qs9BgnBR6HJBrHgjX6YjwnXkxYk53Zi4fSr4iXjrFmar4ZOe+66xcd4YuLarQrWUVjddn1+KT1Vev892BEwB4OWTcZjpDzHzRDbJn8YlPzBPW+GnXytxMEK7TEeE6y6FcnVL0ndiJAdO6ogoLf2MhLq1g8/njFwAUK/tmt1pTufDH/x3i0i1fpgxrTfkS3hqvz26LbipZZuy2PXyyd5t+b5xFWOLimyg+eQfhOx0Qrst+eBf2YvIvg/HIo/08W1asH2ri9LkHLP3tEI1rluTLwc00Xp/dPHjA5wFDNmxi2cWz+r2xgRH+sziM4jrzPtRJTW3ggSzLjwAkSVoLdAZup10gy/KT1NfeforIgD1gC0iADRCU9bdsPiTGJ/FV7x9YdnIu0//3EeO6/ahxce11pPBoZA/N/xEVUfEoXbV7INhGJpPkprkV1MGjtzl49DZJbgpsbRQkadhNYBetItFFt1oqmxhI1mOtQ9846xiJFGf9d6bZxCnI7ejE6k696L99PbdDX+g3UIw1OKfofR9vEx9jh4NzosHGe52IWAfcnQy7SyQ9AmNd8HYy7tn1z+ODGXV5AZ62AQBYS9akyIb7e3kfySoF/vHu2l6eS5Kk12dGf5dl+Xct4tJadv3v7RdkWQ4GZr715eOSJLUGTgJ1gKHAj9reZDZAuC6LObf3Ks8eBlKsYiHqflCdoKchvAiNY99a7X9Q19aJoL0XtXXi6g3nOH7qPo9i320FnR66OlEfr+nrQsi8D4sqPBhSqRpdSpej99Z1BMTq8fy2EBdmVw8myynMvvUX1T2ciFVmTfWTEXwnXKc7wndmxN9TVlGnZQXGLunPpJ7L3umKkhGGzhO19eGf/50gVlJy+Nx9re7TnH2YWRcWwJWtvfqz5vZ1llw4pd8gBnShJXvQFLmgoRCuM0uE6yyExzf9eHzTj9ptq3Dt+B0uH7qJlacHAAprK5QpKlw91Q94Gw0t/TPCkC5UKlXMmbeNebO76X0/GWEJHkxMSYEocPOyZ3m7rvx8+RxHfB/pfgMWkA9mtQMtxX9iHtMsEa7LZsiRUTRoW4WYiDh+mrpB6zhD5oXa5oRbd13BO68bH7SpxL/bz/MizHBzSpbgwTO+6noBNy97+pWvzG9XzqPzKGaeB+ZU/2WX3M7sO54ABYDXK2/8U7+mEVmWz6CuxglI/dgny/Idg9+hmRPwOJhvP/qVkpUKMXJ+zyx7n6zofAKQW7blr6/607ON5vM79TliwJJ2evtFRjJgzQbikpNZ1akXFXJp3vn+Xixktzdk3x3fAHHKePzj3SnkWIwZFRZT1Kmk0e9BAyGyLNd87UNjsiZJUgWgPurn9W5t30iW5RRetfhqrNfdWi7CdUbA3yeQE1su8N3Q//HvV5vY88seU98SoL0T/Z+HYxuppFW9snRtofl4RXPe6Q2Z9+GQ9Ztxt3NgTede5HXSswLGQlyYXT2YqEriTKi6pWt9r2aUdalk1Pd/C518J1ynN8J3ZsSLZ2H8Mu5fKtYuQddhmneLZTXa+FCWYfWK00REx2OtsKJoAc1dPc3Zh5lxYVBMDId9HjG6Zj2GVqmp9zii84maHLLzTbjOOAjXWQhhgRH8NXM9eQvnpvvnbSlTs/jL4waUKSo887rSpHN1VEqZ+9d834k3VVfMyKh4Ph+7itsPA9UxNhkfUZ5dPShFg61CwW9tOtGggJ4nnRg4H8wKxM5vnRHzmMZBuC6bEfA4mN3LD9O2bz0KFNOt274uPtSEtnOk//v7KIOm/KdV0Ym5d4bWl66FyzGpbmOm1W+q3wBmngcK/2nEbHM7Syg80RtJkkoC5YCCqMXXXJKkdw6SlyRpuCRJFyVJupgkZ03XBlNzft81Vi7YQu3m5XHz0m1xJisSKdBeItExCfgHRfDFwGa0qKP5DHJjFZ+YcrFtwJoNxCYns7pTL8qLY3cMiqmEcz9aJkWVzGclJ1PU0eyKT3QlrXJyuSzLmg8+fpO0Nj45qkVlZtDWdanXZnvfaUNmjo41tRMBWtQtzfgPW2aJEy3Jh9cDA/low2a8HBxZ1bGn/scNWIgLs7MHAxO8aJi7BcNKfGn0984EwnVGRuR2WcOhNac5ufsaH07pSP22mosaX8eUR7PaRioZO7g5/5vVh8L5PLQePysxxaLbjAMH2fngHtPrN6VHmQp6j2Puk44gik9MhHCdkRGuMz43T9/nx1F/Ua15RcYs+5BOn7SkarUCNOtSg69Xj6RIaW9uX3zE3ctP04035ZGstpFKPupWj19n9sbBLuMd4uacF+rrwZikJD5at5lHkeH82a4rtfJpteadzkDCgcJ/wnfGRMxjmi+rvtlGYnwyk38ejKOz4Z9nhvSgLEP4owgABneuTaVS+TO8Pqs9aIpccOWVa/xz6TJDq9RkZPU6+g1i5g4U/jMoRnOdJRSePAMKvfb7gqlf04auwFlZlmNkWY4B9gD13r5IluXf06qCbKWsWzg3Nf/N38LnDWcSGRqDJEk4aXk0Dph2oU2lkpm3YAfX7z9j1sj21KqouYo9JxSf9F+9ntPPfPGP1u7YhfdiIQtukL0X3UKTIphyYxkxyZF8VmoShRyLGf0eDIEkSfbAQEAJLNdjiLqpn/Xok2rRZLnrIOf4ThNvnN2duqMtl7cbVgrtfizKKidqy7wFO7h2L82JRTReb86TjJA5H14NCGDYxq14OThS2sNL/5uwEBdmVw+myEqm3/iNsKQQo76vvgjXZQqR25kh3w5axva/j3P9jI/OsaYsPlm35iwpShVLJnXDwzXj4kNj7XIz9oSjSpaZuH0vx/2esLBpG+rru+MbzH7SEUTxiTERrssUwnUWxrFN5/j2499wcLLn00UDmL91PON/HEDBknm4ee4hcz76g9gMPGbK4pNHtwMpXTQvX3/ZCWsN+aw554X6ejAiIYEhazfxLCaKv9t3p0oeb/1uQDgwx/pP+E5vxDxmNiQiOIr5g36iaNn8DBz/gU6xhs4LtfWge5KC9o0r8M24zhTI45bhtdmx+GT+4aNsvXWbCXUa0b98Ff0GMXMHCv9lHmO7zhIKTy4ApSRJKiZJki3QB9iuZawv0ESSJGtJkmyAJkCObtsV7BeKKiycfl+0Ydnu8RQulVfrWFMutCUlpTBt1iZ8n4fx9ZedKV1U8xEz2b345FlUFJ9v2klUUiJ2CoU4dsfAmEI4YUmRTL3xE3EpMYwqOYU8dvmMfg8GoCfgAeyRZdkvvQskSaouSdI7/pEkqQWQtt19ZdbdolkiXGdCPOzglwOTGTC2rdYxppxcTEpKYfqsTTz2D2Xh2E6UL6F5cs2cJxkhcz68+OwZTX/7kzPP1Y8cvUeyEBdmVw9GpcQw7uoSo75nJhCu0x/hOzMkMT6J38b8RdTj59jYWTNwfHvsHGy1js+K4hNtCAiMZMqsTXi5ObFoQhfs7TJ+jmfX4pMkpZJRm3bw9/XLXA0K0O/N0zDzSUcQxSdGRLhOf4TrLJCjG87yVf+lfP/Zcs7tvsrWX/bzx9ytTOn7C3ExmnfZG/KYAV04fe4Bi5bupU7losz8rB1WGrp7mnNeqK8HQ+PiGLRmI5cDnxEaH6ffm4NwIDnWf8J3+iFcl025uP86M7ovZsW3O3WONUXxSVx8EpNnbESSJBZP7Iars32G12e34hMZmLx3PwefPGRolZrYKTI+fu+9mLkDhf8yjVFdZ/aFJ6nnB40C9qEW0HpZlm9JkjRXkqROAJIk1ZIkyR/1N+9/kiTdSg3fCDwEbgDXgGuyLO8w+h/CDLmw/Tx2DrYs2fol1ZuU1TrOlAttMTGJTJi2gSt3/AiLiNUqJrsXn6jjrZhctzEbu/TV/0xTsJgFN8i+i24AIUnhTLmxjIvhpwlNCjb6+xuAtJZdGZ0ptwTwkyRpgyRJ36d+HAIOAnbADFmWT2f1jZoTwnWmJTQgnFPbLtB7VCvLcWJsIpOmbyAsMk6rTmD6YEk+jE1OxjrGip5lKrKyY0/srfV0moW4MLt6ME6ZYNT3ywTCdXoifGf+VKzgTe9Rrfh24+d45Nb+GWCqHW53fQKZ880OyhTLy+ShrTVeb+7ne+vrwrjkZL45cJy4lGScbWzNZlOCJXswB0w+akK4Tk+E6yyXRzf82P/fCWb3/oH/TVrN1uXHUCm194apXLjn4E1+XX6UVvXK8sWgZhqvz47FJ8GxsXy0bgv+0VFIQD4nPZ/hwoE50X/Cd3ogXJe9uXzoJnH+Qdg72tJ+QAOdYk2xKeFZQATT5mzGO5crC7/sjI11xsUX2a34JEWlYsyWXfTauoZEpa4nqLyGmTtQ+C9TGNV10uvt5gXgpvCS69rr1kbKUsld0Iu5W8ZTpLQ3/5u9hR0rTmgdK3to/59QqeWRPkluGZ9H+ua1ChRWErk9nQkM0SyzRBfda6ySnXUO0SsmjRRn/f8vejo6sKJ3d4q7e/LJvm0c9X2s/404p+gfmw4Ozll3/qK7k+GPsEgPbyfT7Fwp6BCBs7ULTgpnghID+KnG6kuyLNfM7LgeZfPITZf31OrarQ1/0fo9JUkqB9wG/IGi7zsrTpKkj1G3VawI5AJsgCDgDPCTLMvaP4wEepOTfKcNdg62/HhyLp65XRnV9ltCAiO1jjWlE50cbQm30T6pyO4+7FC2DEs6tOe43xOG79lKkkrPhMtCXJgdPaiLdzSRFb4TrrMshOv0o067qkz9dySRYbHMHPw/fO8HahVnSh+2bVmR20EhPPB9oflidPehJbnwu85taVm0BAN3bOBqsHZ/d+liQBdasgezyoGG8p1wnUC4zvBYWUmoVOrnsJWnh87x2vrQ0C4cNrgxPi9C2X/qrlbXZ7ULTeXB8S0b0r1MBfpsW8fDiDD9BhEONIj/zNl1IHxnaQjfGZ9OI1oxcvFA/l20izU/7tcp1pAu1NaDzRqVZfr4D/jym81cvOWr8Xpz9WBmHCi7yMxq2JzN927pnwuauQPNzX9iHvNdzL7jiSDreOEfythmc7hw5DbDZnYhf9HcWseacpe3+lolXw5uzh9z+lHIW3MSmN07n4TFxTNwzUbuh4fwe9sutCpaQv8bsZDd3pB9d3yn4R/vzqCiIxlderpFHLsjy/IdWZYlWZYLvU9gqdctl2W5gyzLRWVZdpZl2U6W5cKyLPcWyZrAVCTGJzG/74/Y2tsw6efBWGk4H/t1TOnE2LgkbCOVlC6Sh19m9MLNJePnYnb34c6795i27wBNCxdjaasPUGhoNf1eLMSF2d2D5ohwnSAncG7PVca1moe1jYIlW76gUt2SWsWZ0od7D97E94Z6Yq1MMcMfy2pJLlx86CSh8XH826EnVfJoPo7vvZj5jjcQnU+yCuE6QU4nregEQBUWDoCkQ15hqs4nf6w4ztHd6oYCXu5OGq831x3fkDkPbr6s/h6s6tiTwq5u+g0iHJgj/Cd8JxBkzI7/HeTAqpMMGv8B7frVM9l9aOvBIyfu0n/YH1oVnYB+c6S6YOzOJwCeKQ40LlSUvz/oTkkPL/0GMXMHCv/philcJwpPcjjxMQnM6baIcS3n8fyJdrvDshJdik+2bbqElZUVP03rSYE8mhOJ7L7YFpGQwKA1m7gVEsSCJq1xsNa+g8w7WMiCG2T/RbdfHmwBJD4vNdUk7y8Q5CT87gfw4+d/EeQXho2Nbmdimrog01WpoHwJb5ZM7IqjfcbP/+zuww03bjL30BHaFi/N4ubt0HskC3FhRKyDWHgTCAQG58HVp4xpPJtHt58RoUWHyTRM7cO2Dcvxz/yBtK6v+ei87Fp8EhgTw8A1GwhPiOe/Dj2olDuv/jdhQCx14Q2EAwWCnE6bPnVZtHlMlmxOyAoXVi1bgE0/fEyLOqW1jtEWYxaf6MujsHAGrduIrULB6k69KeDsqt9AZr7wBmLxTSAQZC2yLPP9Z8s5f+gWIxf0on7bylrHmsqDgcFR2EYqaVSjBO0bldcqRlss4ei58PgEPly3mWSlkv869Mi2DhT+M29E4YkAlUrm3qVHqMLCadalBrVbVNAqLismFUF7iTz1C2XclLXY2lqzbFovvHNpfohm98W26MREhqzZRP8dG4hP0T4pTRcLWXCD7F184h8fxIyby4hO0f7YD4FAoD9H1p3h20HLSEzQ/Rmqixe1RVsnXr/lz+yFOyhdNC9fZ8F5pmBZPvz38hUWHT/Bg/AwMnWopHDhG4jESyDIWbzwD2V8i7n4PQgC0Hqi0ZTFJyf33+HybT9mjGhLw+rFNV6fXYtPAqJjGLBmAxGJCXzbrG2OKMLMaoQDBYKcS6RfMOVrFqPTh411ijPVotvDq8+58yiIWSPbU6ti4Qyvzerd3mCaHd8+IaF8uH4zrra2LG/fFSsz6IRpqQ4U/hMIcjbKFCXz+nzP/atPGT6zCza22m/UM2URZtcWVZgyrLXBPWgJxSd+kZF8uGEzTjY2/NuxB572enrCzB0o/Ge+iMITwUusFFZ0G96MsUv64ZFHu0o4UxefPHoawvip63BytOXbcZ3RJo/I7ottscnJPPJVn2E6ukY9epatqP+NWMhEI2Tv4hPfuABGX1lk9PcVCHIyBYrn4Zv1o8iVz12nOEMnVaC9E0+fe8B3S/dSu1IRZn3WTuPkWnb34W/nLvDb8fMA5HXKxAHjwoVvIBIvgSDnoQoLp3qNgsz442O6f9JMqxhTFWMmJSuZNnMT958GM290R2qUL6QxxpwnGyEzxSfRDFizgU/2bs0RRZjCgQKBIKs4veMSFw7fZuC4dnh563l0iwYMueiWmJjCtFmbePo8jIVjO1OueMadr8zZg5nJB28FB/Phhs3MPXUElZwJE5r5whuIxTeBQJC1JMYlMaPLd0zu8zPJSe89qSNdTFV88tX8bTx9HsaCLzpRrEDGR85kRw/eexHCsE1bcbOzp7i7p35vDmbvQOE/80QUngheolKqWDj4Z+wdbflyUV+t40xdfOLzKJjxU9fz3V+H0DaPyO6LbQB2sQpqeufnu2Zt6V++iv4DWchEI2Tv4hNDkqxSEBjrotWHQJBTUUVEUrJSISb/PBiFtW4/Lpmy+GTvwZv8svwIDvY2GrueQM7wYVGFO/t7D2FsrQb6DyJcaJEI3wkEhuPigRuc2nONwRM6UKxcfoOObWgfxsUnMWnaBvwDw1k4tjOuzvYaY8x5sjEzPI+K5vlz9c8lE+s0orxXbv0GshAP5sTiE+E6gcA4/Dz6L6ytFQyb0UWnuKwoxNSGmJhEJk7bQERUPIsndiO3Z8aF+Obswczkg9cCAjl/zx+AtsVL4W6n+WeCNGp453/VMczMF94gey++CdcJBKYnOiyG51cfANB3TGu8C2dczKEPhiw+iY1LYvLMjSQkJrN4Yjc83RwzvD47evDSs+c0+9+fXAx8pnNsUTf3V78xcwdmJ/9lF9+JwhPBG/jde87vk9dQq1l5Og1ppHWcqYtP7j0I5N5FPwDaN66gUSSQ/RfblLLMiI3bOfjkIfObtOLDStX1v5EsmGi01EQrDXN/uAsEgszx7EEgP4xcToVaxRk84QOd401ZfLJu8wWmzdhEYnIK1lqcQ57dfegfGcXeuz6MrlmPUdXr6n8TFrLoBtkr6RIIBObDD5/8j6iIWCYuG4StvY3G602ZI0ZFJzB+6nrm/rqHqJgErcbNjpONaeRROdG5VDlWduxJWc9cWsd1LV0ea6vUnyUsxIM5sfhEIBBkPQGPg1m7aAeNOlSlUMmMO4i8jal2e4eGxTB+6jo27r9KSLhmAWVnD+aTXfi+eXv+69gDV1vN/qmVrwAbuvRlR4+BeKQdUWDmC28g8kCBQJD1eDlIdPm4KfNWfoqbl3bdhU11FGvwi2imzN6Eu4sDbRuW13i9OXtQX+KTU7COsWJghar80roTCi2OjCji6s6Wbv3Z1n0Azja26i+auQOF/8wLUXgieIedfxzi/KFbfDStk9ZH7oDpi08A8ikcGDekOcum9cTDVfPDJrsvtiUplXy+aQd7Ht1nVsPmfFK1lv43YuCJRrDcRCsNIRyBIHtzbOM5dv5xiJ6ftaTLx02y7H2ywokqlYyn0po/5vajZ5tqGq83xtneYDofzjhwkK23bjO+TkPG1RadTwyBcKBAkPOICo1h8fDfKVomHx9O7qhVTFbliNoQFh7LucP3AahbuShF8mtuMWzOk42Z8WBYXDwD124gUalkVadeWhWf9CpbkcXN27Gzx0DyOaU+8y3Eg6L4RCAQZAXrl+ziy87f4/cgSOdYUxWfPAuIYOXfJ5FlyJfbFVenjDt+ZFcPvoiNZdS2nZTxzM2KDt1fLaSlQwl3T75p2gaAkh6e7Os1mJIeqTv7zXzhDUQeKBAIspYXz8KY2W0xXt5ufL12JHkKeGgVZ6qjWO8/COKjkX+zetdFrcY0Vw9mtgDTJllB+xKlWdi0DRmN5GnvwJIW7XCzs6dS7rzs7T2Egi6pxwyauQOF/8wHUXgiSJdFH//CojErCQ+O0inO1MUnoWExTJ61ify53Vg6tSduLqL4JFml4sstu9ly/zbBcbH63wSo5WJBk41iwlEgEGSW3yat5vTe69RvVxkrLbqHvI6pnRgfl0TgiyjGDm5OhyYVNV5vjOQqM3Ggvw9VsszEPftYf/0Gn9eoR8+ymr8f78VCPAgi6RIIBIbn0sEb/PvVJi4cua11jKl2uKXhHAeThrZi2bSeFMzrrvF6c51shMzlhb4RkQxYs4EkpZLVnXpleOxO08LFmF6/GbHJSeRydGJf78FUz5tP/aKFeFDkggKBwNAkJyZz9/A1AJp3q0mF2sV1ijdV8QmAY6zMT9N6sWhiV+ztMn6OZ1cPHnv8mNHbd1IxV17G1Kyf7jW5HBxZ0qI9Rd08+PXKOYbt2UpIfBw7ug+gXoFC6ovMfOENRB4oEAiyljvnHzCrxxJy53Pnx53jKF+zmFZxpuoO/TwwAttIJcUKePFJL82b0czVg5lx4L+Xr/DjqdP0LFuRjyrXSPcaO4WCxc3bUS1vfrbcv82n+7YTl5zE7p4DqZzbW32RmTtQ+M88EIUngnSJfBHN8ZVHAShRoQA2tgqtY0290Hbtph9T5myikLc7S6f00FjND9m/+CRFpWLCtr3suHwX4FWlvr5YyGQjiAlHgUCQOZITk5nbYzEzO3+LSqnCxd1R61aSYFonKlUy8+Zv5+y1x0we1ooWdUprjMnuxSdT9x1g2r4DbLt/R/8bAOHB17CEs0UFAoFhWbVwG5eP3dUpxpTFJ0nJSibP3IiNtYKlU3vincvwzyxLWXR7GhFB/zXriUxMJI9T+j/PVMiVhwVNWuFoY8Ock4cZvmcLD8LD2Ni1Hx1LllFfZOANCZa68AYiFxQIciJSVBQ9Pm3B16tH0rRL+os37401UfFJSoqKX34/TPkS3iwY00njkazmuugGmfPgwQcP+XDDZhZfOPnOa7ZWCpa16kCl3HnZePcm3507yQn/pyw8e5xrwYGs6tiLzqXKqS8WDhT+EwhyOFeP3uaLpnOICo/F3vH9XaTeJiuKT7SleaUSDOlSl6Hd0y8+fB1z9WBmHLjs9Fmm7TvAmtvX0319SYv2NClcjMNPHzHu8B72Pfbhm7PHeRQRzqaufWlaOLXAyMwdKPxnekThiSBDPO1h8dYv+Hxhb53iTF18cvmaL9O+2kKR/B7UqVxUq5jsXnySRlm7XOzqOZDZDZtn2FZLI2LR7Q2EcASC7Issy8RFJ6AKC2fc9wP4cec4ipXLr3W8KZ2YnKJk5pwtXL/3nNkj21NXCydm5+ITgHXXb6CKknG3s2d6/abYKbQvrn0D4cE3EB4UCHIWqrBweo9qxdRfh2gdY8rik8dPQxg/dR1OjrYsm9aL3B4ZF5Gae26Y2c4n7Zev4Kjv43dey+PoxNKWH+Dt5MJPl86y8d4tLgcF8PXZYxx6+pAfW3bg02q1XwWY+aQjCAcKBALDo0xRMqHlV9y98oRJywbRe1QrneJNVXxy4owPi5ftp17VYswY0RZJg0rMddENMufBs35+pES8+2drX6I0HvZqZ9wJffHy18f9nrDw7DH2Pfbh+xbt6VDitSJMA2GpDhT+EwhyNn73A/ikxmQuH78HoHUnMFN58L91Z9i1/zofd6+XJceSZ2aeUxcyOyeaFKF85+vtipemTOpxrKefPcXB2gaAQ08fseDMMU4/8+Wv9t1oW7yUOsDMHSj8Z1pE4YkgQ0Keh7Nu0U5a9axDt+HNdIo1dfHJhctP6D/sTw6c0X43nrlPMELmi08ehIax8vJVhlSqzoImrbHSlGlmhFh0ewMhHIEg+/PvrHUoFFYs2foF9dtW1jouq5yoDYmJKUybuZEjF3x48jxUq5jsXnwC0MirCEOr1OTvD7rjZGOj3yDCg28gPCgQ5CySI6Np1KEabfrU1TrGlMUnPo+CmTB9PR4uDnzQpILG6809N8zscazWMe9OBzUrUpyYZPX3MyY5iUKp53lfCHjGd+dOsu7ODSbUaUT74q91UTPzSUcQDhQIBIYnOjyWKe0WcGjtKYZM6sCYb/tkyfsY2oW79l/nf/8co3WDcvRup7lbS3YtPlHHv+nBfY8fMHTPFtbducGMBs2YXr8pRd3cAbgaHMiS86f44cJpTj3zfRUkHCj8JxDkcFRKFaqwcIoVcuXbDZ/TpJPmgg4wXfHJ4mX7OHreh7GDm9O5eSWN1+uTE+qCuTjwuN9jhu/dypb7t5larykzGjR9mQueD/Bn0fmTrL19naeREa+CzNyBwn+mQxSeCDSyeuE2Tu66ykdTO1GzaTmdYk1dfPIiJBrbSCVVyxbgh8ndcXTQ3PbL3CcYIfNi+frocX46fZa+5SuzuHk7FKL4xGAI4QgE2ZsHV5/wecMZPLkbwIw/PqbfmDZax5pysS02Lol587YTGBKNlSRRukgejTH6TDJakg/33Pdh7M7d1M5XkNWder/c0aYzwoNvIDwoEOQcNi3dy9WT9xkxpxv5i+bWOs6UPrx7P5Chn6/gn63ntLre3HNDQ084brl3m493b+av65eYWq8JU+o1oYS7JwD3w0L4+fJZvji4i92P7r85kJlPOoJwoEAgMDzJSSl8+/H/WPX1Vl48j9Ap1pQuXL3hHN/+uJdth9Jvtf82OaX4JD4lGf/oKKYc2883Z4/TtXR5upYu//J1n/BQfr58lvCE+Dc38QkHCv8JBAIe3/Dl/jVfPp3bAzdPJ4OObUgPKlUyX83fxqkrj2hep4zG7l+gmwct1YGxyck8ighn3OE9/Hz5LH3KVaZTqbIvX7/xIog5p45wJ/TFm6comLkDhf9Mgyg8EWhElmW+HfITT+48Z/LPg8lT0FOneFMXnwB4SrbUKF+IJRO74WCneVezuU8wQubF8sOp0yw+cZKupcvTrYzmHX8ZIhbd3iAw1kVIRyDIxoQFRjK+5VccXH2S1n3q4uym/XPFlBOM6muVDOxUmz/m9qVBNc0tMLO7D7ffuctnW7dT1jMX6zr3Jq9TxkcvpGElSUyo0/Bl60lirM0+2UpDLLwJBAJDIcsy3334M8lJSiYsHYDCWvvpBVP68HlgBLaRSgp6u7NwbGecHTN+5pq7Cw054ZikUhISH8e800eZduwAbYuXole5VzsB/aOj2P5A3VH0nc6ZFuBB4UCBQJAV/DtvMytnrgGgdNXC5M7voVWcKV24a/91lMEJONjZ0LC64fNCS/BgLidHAGxj3z169ferFzjq+5jPa9Sjcm7vl19XyjIAqtTPLxEOFP4TCHI4KpXMkuH/w9HFnk/mdNcqxlQeTElRMXvOFiYu2srbj/P3kd2KTzwd1E5Iz4HfXzjNcb8nfF6jLmVTj98BSFSmAPDOt8zMHWiMHFDwJqLwRKAViXFJzOq+mE2/HybkebjO8aYuPjl59gFffbeTCiXzsWhCV+ztND8Ms7qNVhqmLD759ex5hmzYxMa7NzM1DiCKT9JBJF0CQfYlOTGZ74b9zuhGM4mJVHvLSqHdj1WmLj7ZsfkSD3xD+PrLTjSqUULj9ea+4AaZ8+Hhh4/4aONmkpXKdycQ38Pshs35tFod9vQaREkPr1cvmHmylYZYeBMIBIYi5Hk4Sz//i5IVC1GuRjGdYk3tw8KOLtSvWowlk7rhaJ/x5gRzd6G+HszjpN6NmN6E47q7Nzj05CHDqtSkVr4C77yerjMtwIPCgQKBIMuIjGTSskH8sP1LSlYqqFWIqV04pGtdvhnbhRZ1Smu8NjsVn5Tw9GRcw4a42NmhkmVs3uoAJgOXA58DUMYrVzojgJudPfmdXXC3s8dOoRAORPhPIMjpPL39jDXfbqdZlxrUaandRmdTeTApWYkckoizox2/zOhN45olNcZkl+KToh7ujG3UAGdb23QdCHAhwB9bhTWV8ninMwLYWilwsrHB2io11swdKIpPjIsoPBFozQv/UFbNWodKJeOR20XrBbY0TF18cvTkPRYs3kWVsgX4blwX7GwMX3yirxz0PZ4AMl98cvLJUxQxVhR0ceOPdl3w1PeoARDFJ+kgki6BIHsTERyFKiyccd/35/Ove2kdZ8oJxpiYRCZMWce9x0EsGNORJrUMm1ylYUnFJ+f8/OnyzypexMWikCQKu7q999pPqtZiQIWq+EZFoJRldvUYSIsir+0SNHCyZamTjiAcKBDkFI5vPs9HVSZw89xDnWNN6cOLV54w++ttlCvuzaIJXbGzzfj5be4u1NWDpXPlYkKTRng42KOS5XeO3VHJMleDAwAo5qbevf/2O5T1zEWDAoWpksebgqlngJv7pCMIBwoEgqxBpVQxu8cSkpOVfLdxNLVbGH7RTVu0deHKf05y/f4zZo1sT62KhTVen12KTwq7u9GpfFlmNG+Kk40NMq86gKUtoqlS93S/fSRrKQ8vBlesxo4eAzjc9yN29RzEb206U9ozl3Agwn8CQU5n3aId3L3yBDcv7Z8FpswJrSNSsLG2Yt7oDtStUlTr+9AGc3VgMQ8PulYoz4wWzbC3tn7DgYrUrpZKldqBeRzfPDbJ28mZFkWKs7x9V7Z3H8DKDj2Z1aAZTjY2Bl8bNDSi+MR4iMITgc44S8n8tGcCw6Z31jnW1MUnh47dYeGS3bwIjyFFqdQqxiFGplyRvBTz9kRhpfkBnplFs8yIJbMFKCVsPWhYoAgbu/aloIur/gOJ4pN3MLekS6mSiIh10OpDIBBoR/DDANr2rUfzbjWzZHyDF5/EJjJhynpuPwxk+idtcXWy1xhj7gtukLnik7REa2Ldxvz9QfeXydbrdCxZhrG1GhAcG8PI/TsYfWAn5wP8+aNdV4ZWee3v3kJcmN0X3oTvBALjEfQ0BIAaTcvi6KzbM8uUC24nzz5g3nc7qVymAN+O64KtzbudP14nzYW53Z3I5abd2eXmOuFYwNWFdqVLM715M1zt7FLj31x0U0jqz4426o4waT1OGhQozIImrdjdazD/dezJlm79Wdu5N73TjuURC2+AcRwoXCcQmBdP7zxjTONZ+PoEMXP5UDoMbqhVnLYuNPRcaWJiCtNmbuLp8zAWju1MueJ5NcbYRatwtLPB2d5Wq/swRw+e8fVjz737NC1enCE1q+Nmb58ab0WKSoW1lRXNCqs7uQXGvvq7aVyoKPMat2R6/aZ4OThy+OljboUEUcLDk81d+1EvfyHhQAzvP+E6gcBySElW8kWjWexfd1anOFMVn8TFJzFp2gYe+YWw8MtO1KyQcRFmdijAPOvnx777D2harBiDq1fD2dY2Nd7q5ZFy9QoUAsAvOvJlXOXc3sxs0JxlrTrSoGARklQqPB0c6F6mAjt6DCS/s4vBHJhVG/HM3RPZxXei8ESgM1GhMRzdcIYuQ5vSunddneNNXXyy/8htFi7chVIl4+HqiI11xpOLNSoUZsGQtvz6RXem9GtBgVzv3wWdhimKTyBzC26nnvoyaP1GPO0d2NS13xvnt2VE3fyFWN6u65s7ACxkwQ1ybvGJQCAwLP/N38L1Mz58/nUvCpXUPFkHui+0Gbr4JC4+iYnTNvDFwk1ExSZoFZPdi08AVp+7xugDO18mW2lUyePN/MatQYKvzx7nVkgwt0KCWXj2OBvv3mRqvSZ827TNqwALcWF2WXgTCASmJ4+rNXP++YRPZnfTOdZUC24AR07c5Zsf9mBjrcBaQ24IUMbVndmDWvPLmG6M7Fwfd2fNz1FznHA89dSXHXfv0qRYMT6uVQMvR8fUePWim4utLe1LqI9eCEt49X3vXa4S85u0omeZitwLfcGCM8f45uxxHoSH8nWT1oyoWlt9oVh4A4QDBYKcSHhQJONbzuX8oVtUqa/5CBtdMbQLY2ITmTB1PeFRcUwd3oZ06u/fwN3FgfFdGrP40470b1kdB7uMj6sD8/NgQkoKE/fs42pAAJ/VrcPo+vWokCcP9tbWVHLMy6JmbanhXYCH4WGc8vcF1Itw3zRtQ618Bdn58B6f7N3GZ/u3M3zvNiYd3YdfdCQzGzZXd88UDhT+EwhyMLIsowoLp0mnalSqq/mI7zRMVXyS5kH/wAi+HdeFymXePWb0dSy9+CQ+OYXJe/dxN+QFn9Wrw2f16lDc0wMrSaKEtSfzG7ekUaGiPIuOennsXIVcefiueVvaFi/FxQB/xh7aTfv1K+i0aSXTjx/ETmHN9y3aq09TMHMHmnvRRnZAFJ4I9OKPqWu5dOwOoxb0pFaz8jrHm7r4BMA5Dn6d2ZsFX3TMsPjkkX8IP60+xp2HgdQtV5h/JvWmUSXN55dn5vgcUy24XXkeQN/V61HJMuu79KFCrjwZXp/bwZGZDZrRrEhxDvT58M2zvy1kwQ1E8YlAIMg8KqWKrwcsIyEuiam/fYi9o3a7v0xdfBIfn4TPJX8AOjWrRNuG5TTGZPfik2dRUdx7EvLG1yTUlf23QoKwsVJQxNX9pSNvhQSz9NIZfr1yjs6ly1En32vnuVuIC4218CY8KBBkbwKfvGDdoh207l2X1r3r6BxvyuKTfYduMXbiGuLik7CztUaRwbGysfFJHD55j4SkFLo0rMjyCb0oX0Rz0am5TTgmKZVM2bufk0+eMKJObSY0bki9woXwdHSgkWcRlrbsQGnPXFwNCmDfIx8AOpcqx+wGzSns6s4vV84z+uAu/rx2kf9dvcDcU4c56f+UwZWqUTFX6vfDzCcdQRSfCASCrCExLom53RexcMBSABTWmqfgTTlXGhYey7gp65jyw3beqr9/BxsbBY/8Qijg6cbH7Wrz2xfdKZTHXeN7mJsHVbLM8M1b2XHnLn2rVmbLoP4cHf4x6/v1oVOpcoQlxDP64E5C4+Mo75WbhU3akNfJmf9uXmXe6aOc9H/6cqwzz/zYcv82xd08KCSOnhMIBAJsbK0ZOP4DvlzUDydX7Z81pio+iYyKZ9yUdVy7509ouGb5pM2NatsBzNwcmKRUMnj9Jk48fsKHNaqzY/BADg39iI0D+tK3fBXiU5L58tBu/KOjKOLqzqJmbSnl4cXOB3eZdvwgW33uIKMu5Nz+4C57Ht2nnFceiqYe0WruDhT+y1pE4YlAL1RKFQv6/Yjv/UBm/zNM63NL9SUrJheTkpVs3HyRhtVL8PUXHd/bVjk0IpaTlx8xeck2flhxhIfPQ/lmeAeaVyup1ftYWvGJT2govVau5dDTRzyJDM/w2gVNW1PWKzeHnjzkQVgo6zr3YVDFaq8usJAFN8BoLaqy+6SjJEktJEnaIklSoCRJiZIkPZckaZ8kSe3Tuba+JEm7JUkKkyQpXpKk65IkfSFJkuatpgKBGRIWGMHCIb/gkdtF664nYPriEwC7KCUt6pZhxoh2tG+kuaDUPkZFyfxeFMrjjpWmLXGpWFLxiTr+1Y/JMrDmznWG7tnCDxdO80Wt+kys0+hldzD/6Cj+uHqRbptXcy7A/82BLMSFoghTe4TrBIL3s3LBVq6evM+Xi/oxZFIHrLQ4qvR1TFl8olLJOESrWDyhK9M/afPe616ExbDz2E2GTVvF0s0niY1P4n9je1C7bCGN72HsCUdtXPjFzt2suHyFD8qWYUWvHhwbPpQVvXrQpHAxnkZG8PmBnSQqldQrUIjZDZtjZ23Nd+dO8Mvlc/iEhwLqAs1HEeGsv3ODvE7OlPTwfPUGZj7pCKL4JD2E6wSCzKNSySQnJuNpDz/tnUijDlU1xpiy+CQgMJLge+rn+vCeDSjk7ZHudS/CYtiw7wo9v1zOP5vP4mBnw4pJfShngUWYAJP37mfi7r1sunmLsLh4HoaGsebadYZu2MKd0BfkdnBkUt3GFHJ1Y5vPHb4+e4zQ+LiX8WnvEp2YiI1CQZU83q8GFw40e4TvBIKsITkphcXD/keufO7M/nsYtvaau2OlkRVHsWpDeEQck6Zu4Fmw+niZPJ4Z//zuLdkxtmcTlnzWiQFadAAzRweO2r6T2QcPc+jBQyQJQuPi2HHnLsM2buVi4DNcbe2YXr8pZbxyc/jpI2aeOPTG8Ttp7+ITHoqzra1FOTAn+c/YrhOFJwK9iYmI48tms1m77ABXT93XOT6rFtpA+8nF7XuusmjZPooXyoWby/sfNGmLaicuPeSf9WeIT0yiX4tqeLk6avU+llZ8EhAdzYRte4lNfv/3cVq9prQoUoJjvo8Ztncrs04eYueDu8xu2JwFTVq9utBCFtzSEJOO+iNJ0rfAQaAmsB1YDOwCcgNN37q2M3AcaAxsAX4CbIHvgbVGu2mBwMBcOXKLweXG4nPdT6c4UxefyDJMn7WJS7d9mfZJW9o3zrigtHLpAswb3JbfvuzOxD7N8PbQ7rlmycUnKSoVscnJLL10hlH7d1C/QGEGVqz68vWIxARuhQQDrxKvl1iIC0XxiWaE6wSCjFGmKJnW4Wt2/XmY3qNaUamudsX6r2PK4hOlSubsmYccveCT8XunFtTs33+TX7adIi4xmU861COPu7PG9zBHFy44coyR23bw18VLXA8I5JyvH7+cOcfwjVt5FhNFQRdXvqhZHzc7e/66fonfrpwnUZnyzjgutmo/FXJ963haM590BJEHvo5wnUBgWMKCIkmMT2Lk/J6459L8HDB1l+g82OHl7kRgSNT739dKQiXLrNt7md9WnyAxOYXxvZqYnQe1zQd33r3HjP0H6fzvSnqtXsusA4e4GRSETYwVrYqVpF6BwlwOfM43Z4+TkPKu/wD1ETvAndA3u2cKB5ovwncCQdZy68x9vv34N8rXLMaUXwZjlUFXybcxdE6oy+Y820glw3s24J8FAyhawPO911lbK3jy6AX5vFz5qF1tfvuyO4U1dAAzRweuu36D8bv30nHFSnqsXMPkPfs47++PdYwVHUqWoUnhYtwOCWbOycNEJL55VLuUum6a5kDfqIg3BzdzB+YE/5nCdaLwRJApEuOSWDF1FQnPg3F0tmPm8qFmscsbtJfJjr3XGPLJX7wIe/8TXPVan8krd/05es6HikW9KZwn/er/9LC04hN1fPqPiO5lKvBxlRqExMWy+PwpAO6FhbDo/El+uXyObqXLs6vHQBysU6s8s2DBzdKP3rGUSUdtkSRpGDABWAGUkGV5uCzLU2VZHibLcnVg2mvXugJ/AEqgqSzLH8uyPAGoCpwBekiS1MfofwiBwEDExySgCgunx4jmNO9WU+s4UxefJCamMHXGJi7cfMq04W3o0KTie699FhTB31vO8tg3hEaVi/HPpD7ULVdYq/cxxwW39Mjnop40tY19s6DbSpLY99iHg08f0rd8FeoXePPPLaHukPIOovjkDSzRg8J1AoF2pCQrWTrmH0Y3mc210+oCDjsddriBaYtP1m+5wJmD9zK8Rql6LT88/4SDl+5Trkhe8nm5avUe5ujC44+f8M3R4/Rft4GB6zfyw6nTPA4PxybGikYFi1Itb35O+j3hl8vn3vFc2u9Le3oBcDu1EPMNzHzSEcTRcyBcJxBkBSqlikUf/4qjkz0j5/fUKsaUxScRkXEsXrSH5BTle6953YOnrjzi6DkfyhfJS8Hcbu+NeR1zXHhLUalIVqlIUipfbiSQgcb5ipGkVPLPjcsExr55E2m5X2nPXHxYuTqRiQkEx6Vzo8KBZofwnUBgHI5vPs/PX/5L3VaVqNVcc4fl1zFl8cmB3TdQKlUsndIDx/fksmkdwHqNWc4fu87hYGvDP1p0ADNHByYrlUQnJhKTlESK6tUR6429i5KkVPL71QtvdDoB9fyoSpbxdnKmV9lKhCfEv+NJwOwdmJ39ZyrXicITgcHI425L2WpF+WHHWOq3rax1nDkUnyQkJmMb+f6ECl51PXGws8HD1RFrhYI8Hpor+V8nOxSf2FhZ4Whtw95HPuRydKJ3uUrUK6BuK+0bFcnvVy+w4MxxIhITsH69tXaMtcUsuoEoPtEFSZLsgPmALzBcluWkt6+RZfn1/4w9UFdUrpVl+eJr1yQA01N/+2nW3bFAkPVYKayo0bQcX3zbl7LVi2odJ4VHm+w8U4CkpBSmz9rM+RtPsLd7/zM7JCKWoxd8GPftFpauOIpvcDhLPutMkyrFtXofc1xwe50KefIwqUljPB0dUMnyGy5UyTJKWX65qFbE1R14s8uJvbU1PctWZEKdhnxStRbti5dWv2AhHhTFJ+8iXCcQ6M69i49QhYVTtnpR/j49i5pNy2XJ+2RFfqi+NuP88PWT5mxSwNZaQcVi3u8PeAtzdOHrBSWvL7o1zV+MFJWKdXdvEp6Q/ve7YcEiDK6kXnTzjYpM9xpzn3SE7D3xqAnhOoEg6/C9+5wVX22kYfsqNOlUTXMAWVd8oi2aPPg6oeGx2CgU1C6rLsrX5jRWc1x4SyPNhwVcXWlRojgBUVFcDnr+xjVWkoQMeDk4Mrthc2wV1my+d+tlF8x3MLADxeKb/gjfCQTGZeefhxnZYCbnDtzUOdZUxSfPnoczbuo6flt/iriE98ekdQDbvO0Sy7acJClZqVUHMHNzoJzOr4t6uNO6dCl8IyK4GPjsjevTik4crG34qnFLvBwcOfL0kdEcaGiyo/9M6TpReCIwGI+u+zKq/nR87wcy44+PGazDmd7mUHyivlb53uQoretJjQqFKZLfk+CwaJ4/Sj3TOjXGxVHzQ89Si0/SFt2SVSr+u3WVkfu3M+XYfnqXq8SE2o2olFtdyRmVlMiqW1f5ZO82opOSXhbsvMRCFt1AFJ/oQCvUUtoMqCRJ+kCSpEmSJI2RJKleOtc3T/28N53XjgNxQP1UOQoEFolKqWJenx94ERDOzD+HkqeA9h2yIGsmGXUpPpk8bQMb91/N8Lq05/vRCz78tf40cQlJDGpVk1xuTlq9jzkuuKWR18WZliVLMrN5MzwdHVLj1R60tlJ/tlWoO6HIqSmZjPp70rd8ZVZ37MU3TdvwabU6TKrbmO9btOe3Np3Ug1uIB0XxyTsI1wkEehJyz5ew4EjmrBhOjxHNNQekYuqjBgDsot6fH6Y1xSxXPC8lCuUiMSmFu76pR65pqRxzdmHahGNuJyfqFCrEi5hYrj0MSLc4pbi7B5PrNgbgl8vn8AkPff/AZj7pCNlz4lFLhOsEgixk0497uHv5Ca1719U6xpR5IbzrwfT8VjCvO+VKqOcEnzxQe1BOtwXku5jbwlt6yMCNwCCCA2Nfek+RuuBmp7BmUp1G1M1fiIuBz1hy4VTGg1lALphDHCh8JxAYmQdXn7zclND5oyY6xZqq+OSJbygHd1zP8JrXO4CdP/2Qg5d9KFdYuw5g5u7AtHnfi/7P3nBgWtEJwIQ6DWlRpAT3w0L46vTRjAc08zwwG/rPZK4ThScCgxLyPJzxLeay+68j9BnVig+ndNQ61pyKT2xtFLg62ZPbw5liBbyoWrYAnZtXYs6oD5jxaVsK5HXj9NVHBLyIxC5ahSxDqYK52DJnCL2bVdX4HpZYfKIe49UjQwbW3bnB4J0bKeOVi8+q13n5mlKWiUlWF9Cp0ss2LSDRSkMUn2hFrdTPCcAVYCewEPgBOC1J0jFJknK/dn2Z1M/33x5IluUU4DFgDWjXOkEgMFOiw2KY2W0xtnbWzPprGPaOtjrFm3KSUZY173B74xi6O/4cOXef8kXzUjCXdu2VQf8kK6sTrROPn7Dp1i2aFC/GiDq1ye/qkhpvBVHg7eRMjzLqo4hikl4VjI+v3ZDp9ZtR3N2DLfdv03XzKrpsWsXPl89Rv0ARfm/bGUdrG4vxoCg+eQPhOoFAT4L9Qvmy6WxO7LrKx9M6M3HpQGy1PHrH1MUnsgw2EWof2lgrcLRXd7/Ml9uVGuULMbhzbSYPbU3lMgW4dNuXUN+Il3GOdjZM7deC4vm8MnwPcy4+AUhRKVGqVPhFRhAYE/MyJ1S8ttN7Sr0mlM+Vh32Pffj35lXNg5r5pCNky4lHbRCuEwiyEJVKZnaPxcwc/D+d4kydF6Z5EEBCws7GGkcHWwp5e9CkVklG9m1M/arFuPMoEL/ACOyiX7Xob1G9lMb3MOeFt9C4OO4Ev6B83jzkcXJCkepAZWouPKthM3qUrciTyHAWnjlGbLIW31cLyAVzgAOF7wQCE9GmT11GzOmmUxEmmPbYHV06gEUEx2BrrX0HMHN2YFhcPP6RkZT08sLBxvqlA9PmgyfXbcyQStV5ERfLvNNHiUxM0DyomeeB2cx/JnOdYX/SEQiA5KQUfvz8b26f8+HaZX+dYqXwaGQP7RcfFFHxKF21exjYRiaT5Jb+BGfZ0t60aFyOfHndcHa2x9XFHgcnOxwdbHFysEVh9argIig0in+3nefo+ftExyYC4C3ZM71/S9ydHZjQqylF83rwzdojGd6PTQwk63ZST6biQC2XFGcttx28RkE3V/wjo7CSJKxjrEhxVieRCkniXIA/R54+pn2J0tQvUJjTz3zfibdTKLC3tiFZqSRBmaKWU4w1OKfo9wdJh/gYOxycEw023utExDrg7mT4tqWvExjrgreTbsVXZkSe1M8TgNtAI+AqUAxYBLQGNgBNU69LW5V+Tw/ul193N+xtCgTGx+/ec+YP/Ik567+kSv1SnDt4S6d4Xb2oDRn58N1rlSS5Kd77elqVu421Aldne2wUCgrlcefqw+fvjXkbY/tQGxcmq1TMOnAIa8mKITWqU9DNjd1373ErKJiK3nkZXL0aeZ2cOfL0ETsf3gPg02q1GVGtNtFJicw+eZhjfk9eHkfwIDyUPE5OdC5Vjip5vTnzzM9iPGgMB4JFeFC4TiDIBIlxSSzo8wOPxndk8Kzu3L38hO3/nNAqVhcXGio/LFEsN00blsE7jxsO9ja4uDhg62iNg50tzk522Nla4+ygnuhSqVTsOX6bXcdvEvAiCjsgydWK0d0a0q1RJT6oV44Jv+3g9K2n770XfZxmrLwwIj6Bs35+1ClUkMre3lwPDHyZE9opFHzdpBUtipTgWnAA3184TaJSS7cZ0IOW7kAzQrhOIMhiwoPU/y1cixegSClvbl14pFVcVrgwIw8WLexFo3qlyZ3LBTtba5wcbVE4WmNva4Ozo9qDuTyccbBTx1+548f2Izd44PsCALtoFe3bVWZa/5b4PHvBsMUbiYl//3PamB7UxYEJKSkce/SYEXVq0bdqFTbdvIl/ZBTliuVmRLXatCtemuDYGGadOMSVoADtb8ICcsFs7kDhO4HARCz79Hdy5XNn9De9iYmM4/TejDuKvI62LjSEB9/GLko9P5q2B0+S3u3uVdDbnfIl1Eev+jwLAbTrAGauDoxJSuLMUz/aly1N1wrl2XLrNvExKeT1dmZkjTr0KVf55RzomXTWBN8/sHnngdnIfyZznSg8EWQZB1aeBMA2by6adqnBwQ3ntYozdvGJQmFFp3ZVadeyEgAhYTH4+ocSHhFHWFw8gSFRRMUmEh2bwAPfFwSFRhMaEftqTBsFk4a2okJRbzYev0ZispK+zatROI87ny/bmn7Hj1QsofikWv58DK9dizkHDxMYE5M6hnqiMa3C/2GEupVyCXfPNwpPPO0dqJQ7L8Or1qKEuyeRSYk8iQjnu/MneRAeahGJVhrGKj4BsnzhTaW00qUiNJckSRdf+/3vsiz//tY1aZVZKUAnWZafpP7+hiRJXYF7QBNJkurJsnxG3/sWCCyVSwdvMKTiOMK0KPxOD0MnVpC55Op10hxXvXxBiub3JCwyFr8H6snGtCTM3saahOSMn/XmWHwCMG3/AfwiIxhaqxYtS5Z447WrzwMYc3AXAB1KlGFsrQbqmGMHXhajgLo4Jy4lmT0P79OvfBVq5yvImWd+SIBsIR40ZvGJITGw74TrBAIDsHbRDq6fuMO9B2EA2NhZk5yo+TlozOIThcKKbh1r0L6VOj+MiU0gKDgKhcKKqNgEHj8LJS4+ibiEJO4/DcYvIJzbDwNJUb7a4T2iZR16NK7CZR9/bKwVLB3Vla/XHGbT8fdPrJpr8YkM7LvvQ8uSJRhWuyYbb9ziRlAgdQsUon/NKtTKVxCf8FBmHD/E/bAQ3W7CzCcdwfwnHoXrBALL5Itv+1C5XklGtFxIWFCUVjHGKj5RKKzo2bkm7VtXBtRHsYZFxGJnZ018YjJRsYnERMQSEBLFg6cvePwslLPXHhMWGfdyjGa1SzGlb3OCI2LI6+HChlkDGb5kI37BEe+9F3NdeFt6+gzeLs58Vrc2fapUIj45mbzOztgoFDyNjGDk/u3cDn2h+01YQC5oLg4U85gCQfYhJVnJ3J6L+XbfdCYtG8T0gb9x4+wDg7+PoYtP0jqApW3Ok5CwtVGgsLYil7sTxQvmom2j8tStXITbDwMIfBz2RnyrGqU5cOmdRhIvMUcHpqhUzDl0mOJeHkxt2pS+VaoQm5REYXc3cjk5EZEQz8gDO9Sb63TFzPNAU/kvu+R2ovBEkOU0bVOecUv64+zmyNY/j2oVY8ziE6VSxbL/HSK/tzuVKxRk595rbN5xmegY9QphRju9AT7t3YhG1Uuw+/gtfvn7GEkuVryIiOHj9nXYMHsQo5ZuISD0/UmkuRefeDg40LBoEaY1b8qCI8cIiI5OHcMKlYuMSpZxsVU/DCOTXq2qFnRxY1SNOrQvXgZnW1uuBD0nUamkQq487Og+gDEHd7H/yQOLSLTSyKG7vkNkWa6p4ZqI1M9XXhMYALIsx0mStA/4GKgNnOFVdeT7zuNI+3rEe14XCCyOkOfhANTp0QBHF3uObbusU7wpi0/SkiuVpw12ttbY2qh3vLk62VM4nwdVyxakYfUSuDnbs/fUHfVu72gViS5WFMrtxvefdWb5nvPsOX83w/cxRfEJoNGHv527wKmnvtQvXJjSuXNhbWXFeT9/Dvg8ICE2hRIFPRlapSYKKyu+OXv8jaKT18nnrP77S0xRt+h8+a4W4sG0dpPmMPGYRWjyXUTqZ+E6gSCT3D6nnljMU74oi7Z8wa8zNmrVEcxYxSdKpYpf/zpCiaK5KVk8D+s2X2DrrivEpHa71JQfdmxaiY+71ePKHT++23SM5BQlwzvUZUrf5uTzdOGnrafeG2uuxSe7793H3tqamS2b07JkCVJUKuysrUlRKjn33I8xB3cRHBercZx0MfCkI5AlE4+QLRwoXCcQmAnLJ6/i13PzGP11b2Z/9IfWccYoPlEqVfy+4jjly+anQH4PVqw9zY4917SeJ61WtiBTh7chMCSKZdtPk5icwqguDVg9rT+fL92SYXdMc1x4A5i67wBXngfQqGgRCrm74RsRwQGfhxwKeoR/dKR6U4Hut2ARuaC5FJ/ogJjHFAjMnMS4JKZ3/pYlh2bSsmdtnQpPTNkBzN7OGkdHO6wdrLG3s8bJwQ57O3UHMHtbdfzlO35sP6zuAGYHJLpY0b1xZab0bc7Q9rUZtngDUXHpP6fN0YFJSiV916xnQuNG1CyYnyIe7gRFx7D++k12+N3lnq4bD15HFJ9kFrPN7UThiSDLOfDfCep0rMXQ6Z15cvc5V0++v7LvdYxZfBKfkMwXU9Yyc2JHhvRrQO5cLmzZeZmHj19gG6kkxd063c4ljWuWpFfb6sQnJHHuxhMSk1MgDDYev05IZCwjuzTg38l9GL5kI48Dwt6JT8Oci0+OPnrMmmvX6VW5EiMTElhx6Qo+oeoOJ1bREpWL52VQxWoARCWqH+7ONrZ81agFTQoX42pQAGtuX2fDvZsA1M1fiMl1GzO9QVP8oyPVuwIsINFKI4cWn2gibZU14j2vh6d+TvsPeg+oCZQGLr1+oSRJ1qjbfaUA2vWcFQgsiK5Dm1KxdgmC/MK4e/lJlryHoYpPypT0plnjsupj6JzscHVxwN7JBkd7W5wc7LCxUWCVelhpRHQc/2w7x8Ez94iIVj8jvZQ2TOnXgqLennz1YVsK5XHn951nM7wfc/XhjcAgbgQGpftabY+ClM+Vh72PfFhz+92d7Gk/PzQtrD4C80lU+DvXvDzj1IwTrjQsIPHKKoTrBAIDE/c8mMjQGCYuG8SXnb/H936gxhhjFZ/ExCQyYux/fDe3Jx8NaIijoy279l3nWUAEtpFKlB7WKFXvuqNq2QJMHtqK6NgEth6+ju/dYBJdrPh951lCo+IY0qYW+b1cmbp8z3vvxVyLTzbfus2dFy9oXaokeZ2dSVGpOOjzkBtBgbxQxOm/6AYWkw/mAAcK1wkERuLZg0D+nrWBEd/0p0X3WhzadEHrWClcPVeUlccNREbFM2zMCpYu7MvgPvWRgH2HbxESGvPSgypZfqcrZtECnswb0wGA1bsucvzoXRJdrEhRqvi4fW3+HN+LKX/utrhd3wAbbtxk2+07JCmVb77gDF4OjuRxdNK/8wmYdS6YDf0nfCcQmJjosBjGt/qKWLTrxvw6puoAlpiUTHhEHHa21sQlJRMdm0BIeCLPgyO5//QFT56FcvbaE8KjXnUAa1e+JJP6NCMoPJpcbk5smD2YTxZv4ElQOnODmK8Dvzt+Alc7O6wVVsQmJZOYkuosZ3CxtSUhJYVklSrjQdJDFJ9kJSZznSg8EWQ5siyz6MOf+eH4HKb+OoQxHRYT8DRUq1hjH7sz99sdPPULZUi/Bnh6OLF6wzlu3nmGdURKuhX9YZGx/PDfEfp9UJNhPerj5GDLobP3iQlNZO+Fe0TExFOvQlFsFBnvBgDzXWxTyTILjhwjKUXJ8Dq1KO7pyfHHj7kZFEzVfPnoWakiVpLEqlvXOOr7GIDZDZvTpHAxrgcHMv34gTcqH88+9+OfG5dZ3Lwd5XLleZWUWchkI4jik3Q4hHqeubwkSVayLL/9U0bF1M+PUz8fBvoDbYE1b13bGHAEjsuynDV/gQKBCZnf70eWnpjDzD+H8kWnJQT7v78o8W2MfcxA5w9eHUMXHhGL37Nw4hOSeBgXQnBYDNGxCcTGJXL/6QsCXkQS8CLqjfixg5tTu2xhdp27Q4pSyfAP6lI4jzvT/9qb4f2Yqw/TeHthrVmJ4iDL7H54j6ik9B9bvcpWpH2J0gTFxnAp8P07/Mw94UrDwhMvfRGuEwgMTExEHLO7L+anU3OZtXwoYzouISYiTmOcMX04YeYGxo5sTd/udXBzdWTD1gs88Q1FEZ6CMp380NPNiWMXfWhUoyQNqhUnKCSaGz7PeUI4K/ZfJDI2gWbVSlCnXGHO3Xn/OdjmWnxyJ/gFd4LfXVSzRn0Uq721NXkcnfCNet+x0BlgIflgNnegcJ1AYES2/bKfht3rMmJON66cvKf1kTtpGLor5tseTElR8dn4Vcyd2pmhgxrj7GzP1p1XCHoRla4HrSSJamULEhefhKuTPY4Otni5OxEaEculs4+IiU9k2Ad1mNy3GYFhUdx4/P6CU3NdeHun6AR1R+iF7dpQK18BBu3cyLVgzYW06WLmuWA285/wnUBgBkSGqNc9cpcvyucLevHT1PWEBGqXR5iiA9i/a8/o1AGsevlCTB3WmufBESzbfpqEpGRGd23Iqmn9+XzZFi77PEs3zlwdmLbpHF7NjTrEWbOqey+CYmMYuX8HSap3PakR4b+swmSus9J0gUBgCOJjEpjdfTEAk38ZolNsWiW/tiiitH8I2EYmv/O1FWtO89V3O6hToxhjRrSkfu0SSBLYRr770LzpE8CGfVf4aPoqomMT+axvY6qXLwSAXbSKs3d8+d+OM9z3167i3SZG61s3SBy8OmpAE4tOnGTcrj14uzgztlFD/urRjdEN6uHh4MDuu/eYceIgAB9XrkG3MhUIiYtl2vED3A59gfKtLRBhCfFIkkQN7/xvvkmMYWvh4mPsdDkTTSfS2i1nNYGx2hdemQpZlp8CO4DCwJjXX5MkqTXQBnVlZdpq80YgBOgjSVLN1661B+al/vbXrL1rgcA0RIfFMLPbYmxsFfy4YyxVG5TWKV4XJ2bGh0qlip9+P8zNO89QyTJbd11h2rzNjJ60htlztvLDv0dYvukMa/dc5vJtvzeKTgA+7laXNg3KcfDsPX766wi/rjjG77vO0rxaKf6a0BsHu4x3NJizD183mrOtLSW9PAmLj+fSg/QLSmrnK8joGvUAmH/6KC/iYsnwnQzowqxyIBjPg+aCcJ1AkDWEBoQzt8+P5M7nwZSfh2Cl0G6Kwlg+BFjy837++Pc47VtV4pMPm1K+TL7Ua9/NDw+fu8/UH3bw1a97aF6nNJ/3b0KpIrmxi1YREhnL6kOXmfn3vgyLTtLQx2nG8GAab19tHWPFvEYt2dytPxVy5dHvJrIgH8wKsqsDhesEAuOiUsks/vhXHtz0J1qLwsvMoK0L0/PgzAXb2LrrCn261WZQ3/oUKeSZeu2bHlTJMruP3+ajGavYeewmn/VpzNDu9cjjqZ7XunftGUs2HmfxhmM8fK55Q6IxPairA99m7r7DRCQksKpjL+rmL6T/QGaeC2YX/wnfCQTmhZstVKpXkh93jqNC7eJax2VFTvi2ByOj4hk6egU+D4MY0rc+ndpVIbeXc+q1ShRWElI6CilWwIuvPu+ADKzZdYmTx+5x6uYTfth0gttPg/h9bE/a1irz3vswdwemzY0mq1Rsvn6LVsVK8k+H7rjY2up3E8J/BseUrhOFJwKjEfA4mCkdvmXpxLU6x0rh0UadXDx8/C6fjluJl5cTndtXxd3NEQC7KOU7iZUkQXhUHGO/3UxEVByf9WmER2oFpV20irjEd8fPCJsY/cWS1XLZcecu/dduYOKevSw7fYY/zl9g1LYdTN13AOsYKyrmykvPsupCubmnjnArJPiNeEWqhXM5qL+fPmHpJJoGnmwEy59wtITiE2Ak4AcskSTpoCRJ30mStBHYDSiBobIsRwLIshwFDAMUwFFJkv6UJOlb4CpQD7Xk1pngzyAQGAW/e8/5otlcosJiqVinhM7xxvJhXHwSn09czckzPnzYvyFDBzaiWJFcqdcqcYhOv4Viw+rFGdy5LrHxiRy/4ENUbALRsYls2X6ZxRuOUcTbg7XTB5Db3SnD+zHn4pM0klUqYpOSiE9O4UVsLNYxVkjw8giiEu6eTKnXmPwurvx38yoHnqjPrdW4l8DME640LDHxyiTCdQJBFnDn/AOWfvEPkgT2Dtq3WjZmfrh6wznmLdpJnRrF6NqhOq4u9qnXpr+ja//pu4z/biuli+ahR2v1saRpuaE2i21pmHPxSXou++3keRJSUljbuTe18xXU7yZirC3Cg9nYgcJ1AoERef4omEmt55GcmIKTqwMN2lXRKd4Yi24AP/52kOUrT9C+VSU6tq2Ks5Nd6rVvejAxOYXo2ES+WX6Qf7aepWOzSjSo/moR8fn9EPacv0tcYnK6C3VvY+4Lb2k8j4qm3+r1PI+JYsUH3WlZVPc8/yVm7sBs5D/hO4HATHhw9QlfNJtDXGwiC9eOovNHTbSONYYHlUoVI8ev4syFhwwd1JjunWqQN7ereszwlHeOnbOSJKqUKUBMXAL2ttY4O9mRy90Ju2gVZ24/ZenmE5y69ZjxvZpSuXi+996HsR2orwdXXrnG2J27qeVdgHWd+5DbMeP53vci/JcVmMR1ovBEYFR8rjzG58RNADoNaUTu/B46xRtzcvH+gyAGjVjOmk3n1We32VnTokn51OtfJVayrJZJVEwC5288pZC3BzUqFH75ul20Crv3LM5lhLEX3LSVS0B0NFtv3WHZ6bN8d/wkx588IS5Z/f0r65iLYu4ebLh7kyO+bx71JQFKWcbGyooR1WoD8DzmPX+fBp5shKydcDSGdMy9+ESWZX+gBvATUAp1FWVT1FWVDWRZ3vTW9VuBJsBxoDvwOZAMjAX6yPLbP7IJBNkLv3vPGdVgOqt/UBcVl6hYEGc37Z8luvpQl+TqbSfO+nobqzacpXP7anzYvwFlS3sDoFTJ6S64xcYnsXzTaZQqmQGdatO6flns7ayJik1g797rzF95kCsPnuGtRUtMcy8+SUxJ4eijJ+R3daFL+XJYSRKKGCtUskx+Zxd+bPkBVfLk4+CTB/zv6nkS02nN/F4MnHCJhbfMI1wnEGQd+/89zpR2C4iL0a0trjHzw0PH7vDJl/9x9NQ9oqITUFhJVK5QENtI5TuLZ1aSxLV7/ty4/4wOTSpSray6CEOf3NCci0/e5kl4BH1WriUoNoZ/O3SnWWHtdyy+g5lPPEL2dKBwnUBgGlRh4XQb3ozpv3/E0BldtO4ABsYrPlm57iyzF27j7v0AYmLVvi5UwPPlju+3WbfnMs+DIxneswH587i9GjtK7UFtnw6WsvAWFBNDv1XruRP6gnG1G77cfKcXZu7A7OA/4TuBwLx4evsZnzeYwfnDtxgxpxvdP2mudayxPDjr6+1s3XWZ3t1qM7ifhg5gJ27x0fRVbDtyg097N2Joj/rk9XLBLlrFjceBfL/hON+tP6rxpARLyQW337nL8M1bKeLmzuJmbfW/AeE/g2Iq1xm+tYBAoAXutioGjm9Pl6FNmdTrJ148D9c61phnesfEJHL1hh8AzRuVZeQwtfAOHr2NbaTy5TluqtT/b+GR6taYxQvmAu69MZZdtIpEF91qvfQ9m80Y53vDq7Pc0mhZqiQ2VgpOPXtKbPKbck67blzthpT08OJi4DP2PfbJ+A0s5JxvsOiz3gyGLMsvUMvocy2vPwW0z9KbEgjMmMS4JIgLw9pGwcw/h6JSqpg/4i8e3PDXKl4XH0LmnPjnvyd49jyciWPakcvTmb9Xn+LC5Sep1yrfONf0yh1/rtzx5+DZe3w7rguj+jchIjqe8zeekqJUcer4fS7d9ycyNkGrezF3F26/fYfO5csysl5dinp4cPnZc2oXKkibsqUo4ubOKf+nzDpxmIBY3Y4OBCzGgznJgcJ1AkHWIcsy7l7OTP55MP9+t4s7l55oFWfM/NDnYRA+D4MA6NqxOl07VGfy7E34PQt7w4UqWSYpWcn9Jy+oXr4wXh5v7vrSNTfU95xvyPqzvt8mMCaGvqvWsbxXNxY2bU2T1X+SkKKny8z8vG/Ing4UrhMITMPKmWtxtJHo/llripfLz9ef/aP1ETxZ4cL0PHjs1P2Xv+7bvTa1qhdjwoz1EJ6C8jUPAkTFJnDTJ4DmdUrj5mzP8+DIl68Zy4PGyAdfJyIhgcFrNmLnYY1Slt+ZN9UJM3dgdvCf8J1AYF7ERcUzt/tiOo1oybH9d3SKNZYHf/ztEKFhsXw8sBFxcUn8s/oUMbGJ78yNJiUrSUpWsujvQ4RFxvFxt3o88H3Bxv1XsYtW8YRwngaHI8vqExUyWs63FAeeePKUAWs38ELK5PGBwn8GxRSuEx1PBCYhLDCSKR2+wcXdkW/WjzLrzidpPHoSQlBwFH2716Z2jWKp1ytfttP3cHWkWMFcqGSZh77pVypaQucT0L6y8XX9WFtZ4WBtTWxSEmfv+r08buB1upepwKCK1YhJSuTrM8eAd88GfwcL6XwCllfxKBAIzIOUZCXz+v2IlcKKxZu/oF2/elrHGvMouj0HbzJm8hoKF/Kia4fqL4+hU1/77jF0vgHhjF6wEZVKZnjPBtjbvXqeJwTqloRk5hg6fdHWhY/Dw+m7Zj3h8fEMr12TP3t0ZXidWthIVqy9fZ2Pd2/Rr+gkDQvxoHCgQCAwBCkhYeTO78H03z8mVz53reNMkR8GvYjCydGWTz5sQn5v95cuTMsP3V0cKFpQvQsuOfndjlfG6HySmbjMdD4Jj09g0JqNDNixQf+ikzTMfNcbCAcKBALDoExR8uuElSwe8QcVa5dg6a7xFE7tNqkNxtrxDaBQWJGYlEKFsvkZ8VFT7O1s3skJnRxs8c7lio21Ak+3d9vuG6sDmLE9GJucTFhwPFaSxLJWHRhbq4F+NwBm70BjdYEWCAQ5B1mW2fbrASIe+qOwtmL238O1dqHROoCtP8vMr7e+0QGscMH3dwDbuO8KfoHhDO1en4J53QG1A9OKTbTpIWGuc6JvcyMwiMCAaCRgQeNWNClUVL8bsAD/Cd6PKDwRmIz7lx9bVPHJvQeB/L7iOLlzuTB0UCOaNy6LJIF1RAqebo40q12K+tWK8Tw4guCw9z/Vs1vxSRopKhXPoqJwtLHBy1G9GKmIsXrZWrJxoaJMqN0Qe2trfrh4muvBgYCWlf8WsugGQjoCgUA/7l18xMh607hx7gGjv+nDuCX9sbFVaA5MxVhOvH7Ln0EjlrNp+yUiIuOwtbWmcf3Sqde+eQydwkoiOCyaizefUq64N3UqF31jLGP50BguDIqJodfqtYzesYtJe/Yx5+BhBqzbwLx9R1FFqXe6OVrb4Ghto3GsdLEQDwoHCgSCzBIdHsus7ouxd7Bl5p9DsbPX/rlp7PzwxGkftu+5Ru3qRRnQuy7Fi+bGNlKJSpaxs7WmWrmClC3mjV9gOAEvotIdIzsXn8QkJfHYT93ZdFztBoyqXlfvscx94hGEAwUCgeHY/98JxrWaR2RYDDGRuhXsG2vRTalUsffgTS5ceULr5hXo/EFVnJ3tXuaECoUVFUvlo2BeN548D+WRf0i6Y2dnD9rEWBEXl8zomvVY3LwdNlZ6LsMIBwoEghyKE8mUqlyIab9+iL2jrVYxxvLgidM+HDym7srSt0dtxo5sjUJhhSL83aL7qNgEbj8MwMHeBldn+5dfz64FmADuyQ5UzZuPP9t1pUupcvoNYub+E+57P6LwRGBS7l9+zNSO3+Ls5kDZ6kV0jjf25OK5i4+YNHsjubycGTuyNd8v6MPIoc34dnQnRvRuCMDOo7e44fM8w/Gza/HJiSdPSVaq6F+tCkXc3QFwSbSjYcEi/NqmE3mcnPnj2kU23L2JUtejL7Ng0U0kXAKBwJyICo1heoeF/Dd/C/ZOdiQnvbs7OiOM5cSw8FguXX0KQOtm5Rk3qjX1a5dIvfbVPStV6ud8QIh6sa1IPs93xs5OxScABx88ZMut26y6eg3/yCiSlKkTrzFW/NjqA9Z27k1uB0cNo7yHGGuzT7pA7HoTCASZx+/ecxYO+YUSFQvwxaK+OsXq0gnMEPnhXytPsnrjedq1rMSkMW0Z2LseLcoXZ/SAJozs1xh3FweOXXzA/afB7x3bEhbdMrvwVtDejfF1GjK9flPNHS/fh4U4UCAQCAzBvUuPGN1gBmFBUVhZSbTuXRerdHZRp0dWLbq97cK4+CRmL9zGw8cv+HhAQ0YNbU7VSoUo5epO91ZV+LR3I7zcnbl405fQiNj3jm0JHtQHpSwzdd8Bfjh5mu5lKvBX+24422i3cPoOwoECgSAHEhEcxcLBP1OgRB5GL+ytdZyxO4AlJCRTrnQ+RnzUFAf7dzuAOTva4Z3LFWuFAk+3N+cE9XGgMedF9XVgdGIi/Vat50LgM35o+QHDqtTU7wbM3H/Cfelj2JVcgUAP7l16xJAK44izUv/Ht1JYoVJq/8DNyjO9gXfOcbtzL4CPRv7DyGHNqFKhEFUqFgLgnk8g+8/fZf3eK1qNr+t5pqD/2WyZPdMN0OpctwM+D1h7/TqDqlejsrc3PqGhlPD0pGzu3NhaK1hx4/LLI3b0woDnu6WRI8/6VkkGL+QRCASGQaWSWblgC5IkIXm44+zmQEJcEinptOhPj6x04ts+BHjqF0pUdAKD+tQnJjaR67f8sY1UkuJujUqW8XB1oHjBXAD4BYanO7axfJiWZOnjQ11c+D7WX7rBj506sKVbfwbv2sTDiDD9BjLzs07TMAsPCt8JBBbLub1X+Xv2Rtp91Aw3L2ciQ3WbKdPWh4Zw4T+rT/EsIJwhfRvw0YCGL78eGhHDhv1XWLH1rMaxdXWhvk4zxVnfMjBx916iEhIZWqMmbnb2TD66T/eNCGBwBwKWf+a3cJ1AkK1RhYVTt2MNvlzUl0YdqvLNqBXERGp+xhgrL0xJUTF22jomf9GOZo3K0qZFxZevxScmc/DsPf7dfp4kDfmsPh405vyovg4E+OnMWZ5FRbGgTSt+b9eFftvX6zWOJeSBWeZA4TqBIMdy7fgd/pu3mSGzenDj7AP2rD6jVVxWeDC9fFCpVLH30C1qVClC62blCQuLYee+60RHJpDkpkChsKJCSW8K5nVP7QAW+s64xpwXNaYDY5KSGLpuC992asO0+k1xtrXj+wundL+BtOe/ARyYlfOgBiGb+M4iOp5IktRWkqR7kiQ9kCRpcjqvN5Yk6bIkSSmSJPV467XCkiTtlyTpjiRJtyVJKmq0GxdoTUxEHKqwcKo3KcvP+ybqdJ436F7FmNndbRGRccxftIsvpqxl1MRVjJu+jvEz1rNl3QVUOkygWUrnE9C+unHe4aPM2H8QaysrGhQpTJncuTj+5AnT9h1gzqkjmbsJMPiObxDV/gLzQLhO8DqyLGOTEMv328cyakEvnWKN2Q3sxu1n/LP6FIULefLRgIbUq6XufGIdkYKrsz0Nq5egdqUi+AeF8yI85x1D9zpHHj2m35r12Flbs7lbP2rnK6j/jRi44l94UGAshOsEurB+8U4+b/edzkUnaRiz88mBI7eZMHMD077azJ//Hue3v48yccZG/rfuJHEJyUha6CO7dQF7HRn46vARfjx1mp5lK/Jjyw/0vwkLyAVF9y+B8J3AkJzecYkfP/+bKvVL8ePOcRQu7a1VnDHzwoU/7GHeop38t+4MZy8+4uCx23y9eBdLVx4lNCI2SzxoKZ1PALbcus3QTVv0W3B7HTPf+Q0iB8xJCNcJjMXa73Zw8egdeo9qZfJjydNzYHx8ErO+3obPwyA+HNCQUcOaU61yYUq7udOzdVU+69MYL3dnLtz0JeQ9c6Pmngvq68AkpZIvt+zmr4uXOPvMV783T8NADswq/wleYfalM5IkKYCfgVaAP3BBkqTtsizffu0yX2AIMD6dIf4F5suyfECSJGdA9//BAqMR7RtE7nzufLN+FJN6/URIQITWsbpUMYJhdrc9D4zgeWDEW9cqSXJT4ORgS9liebl02y/DsdOkYqwdbvrEpaFtdeO66zc49PAh1pIVLnZ2+ISqKzmtsUICGlcoSp9ylfni0C5ik9OfvNWIgbufZGW1P2D6Xd8Cs0a4TpAeiXFJHFt3mv5TuuD/MIiNvx3WOtaYnU8OHbtDTEwCsyZ3YsKYNty+85z7D4OoUrEQJUvmxcbaij0nbnPTJyDDsS2pE5i+u91uBgXRc+Ua/uzZlSXN29FszXKSVXr+d7UgDwoHCkC4TqAfMb6B2HnnYuj0LuxaeQrf+4E6xRuz80labnj6/MM3X3BTIMtQsnBuHvi+yHBsc9/tBpnz4LLTZ4lMSCREfv+RC1ohHCgwY4TvBFnB7r+O8OS2PzNWj+aH7WNZOHIF5w/d0hhnzLzwxBkfTpzxwcpKQqV6zROpHnRzcSAyOuNnorl3AMuMA089TV1wc4ahVWpy60UQZ55nPGecLjm584nAbBCuExgTWZb5ZtAybHJ76XUsuVE6n6hkxs/YwITRbWnRpCytm1d4+Vp8YjIHz9zlPw0dwMw9F8xMB8wFR1JPQXCGtsVLcdz3CXEpeqwLGsiBZt/5xMKxhI4ntYEHsiw/kmU5CVgLdH79AlmWn8iyfJ23BCVJUnnAWpblA6nXxciyHGek+xbowb1Lj5jS4RvcPJ35Zv2oLO18AobZ3Zb+tUo+7d2IH6b0oGPTipoDyH67vUNi4wiMiXlZdJIWJQN5rJxpVqQ4G7v2o4Czq/43YwG73dIQFf8CDQjXCdLl33mbObrtEh9P60z9tpV1ijXmDrdzlx4zfMy/3PMJonLFQnzYvyHVqxQhNCSav7ec5b8dF7QaO7u5MD38I6PovXIdH+/Zon/RSRoW4kHhQEEqwnUCvXAihYYfVGX2X8NwcXfUHPAWxux8kv61ShpWL8F/CwfRt30Njdeb+243yJwH/718hd1X7gPQrHBxvJ30rIAxcCdM4UCBARG+E2QJt8/6MKrBTB7c8CMiVPtcz5h5IfBm0QlqD3qmWPPn3H5MHtoKhVXGDjF3D1rHSJnyoGO8DT3KVGBFhx50LlVOv0GE/wSmR7hOYFSiQmMIvfsUKyuJpl0051SvY6zOJwDfLd3LV9/t5J/Vpzh9/gEHjtxi/qKdLF11TKsOYJbgwMxQVOHOspYdWNO5N14Ouuf2gEE7n4juJ1mDVoUnkiTZSZLUUZKkjyVJaiVJkjF/cigAvF7+65/6NW0oDURIkrRZkqQrkiR9l1qNKTBj7l16xJSO6uKTxVu+wD2X9l1MwHyKT/74/SgXbz5l6vA2fNq7oVm1V85MHOgnmNfTzg03bvLRxs0UcHZha/f+VM2jXZvQdLGQRTcQSZcgQ4TrBO9l0Ye/cOfSYyYsHUiF2sV1ijXmJKP/83BmzN/CmMlrmDx7I9Pnb2Hc9PWsX3kWRZj6+obVi2Nrk/E/z+zswjQiExJ46BsGwOS6jZnfuCUKbX5QSA8L8aBwoADhOoGehAVGMLf3D+TydufrNSPJV8RL5zFMXXxy+cQDDp29x+gBTfmkVwON1xtzwtFUk46uiXYsat6Wrd36UyFXHv0HEotvAvND+E6QZYQGhDO+xVzuHr4GQN5CnlrFGbv45G1iYhM5fOQ2nZtXZv6YjtjZZPzsNveFN8jcsQN9V63nUuAzfmz5ASOq1tbvBgxYgCkW3wR6IFwnMAkNmpRi0rJBfDKrK5IO82jGLD45dfYBK9acZvq8LSxYsptTZx8Q/TQKAFmGPJ4Zr3eauwMzuxnvs63bKe3hxcYufSno4qbfQBaQA+ZkNBaeSJKUC7gGbAV+B/YCYZIk/SFJkvdr130hSdJdSZICJEk6L0nSfEmSdFsdMTzWQCPU7bxqAcVRt/d6A0mShkuSdFGSpItJsmivYw7cu/iICW0XcHzHZSJCdCskAfMoPomPT2LqjE1s232VQZ3rMH9MR+ztND8Qc8KCG8Dpp770WLmGuORk1nbuTTE3D/0Hy4JFNzHpKLAgtHIdCN9ZKsmJyczqtogze6/rfMQAGHeSMSVFxeOnIZy79JhTZx8QHhGHMnXHW3EnVxaO7cwPk7vj4pTxM1ZfF1rKZOOreCtIhP4VqvJHu644Wr97dINWiF3fguyPcF0O5875B8zp+wN5CnqwbPcEajUrr/MYuhSf6DLZqE1+mJKiYt787ezYe40hXepqveNbVx8aOy/MjAfjkpMZtG4jSllmQ5e+tCxaQu+xhAMF2QgxjynQmla96vDHkWmUrV5Uq+tNXXyy/L+TLP3tII1qlOTHqT1wdbLP8HpzX3gD/T0YnZjIx+u2sN3nDpPrNWZ6/ab634QFOFAgeAuR2wn05timc2z5ZR9dhjZl4rKB2NhqX7NkzOITUBeZvHmtkmplC7Lh+4/o0jzjrtbm7sDM5IFHHj1m0PqNeNjbs7lrX8p55dZvIOE/s0WbjifTUVchSsB9IASwAz4CzkqSVFySpAnA4tTr8gI1gcnAXUmS5mXyHp8BhV77fcHUr2mDP3A1teVXCurimepvXyTL8u+yLNeUZbmmrST+gZkLD6895Y9xKwAoUDwPs/4aikce7Y9lMYfiE6VSxZKf97Ps90OULeaNk4N2/74sqfgkM5J5FBZOz//W8N25kzyODNf/RsDgi24gJh0FRiXLXQfCd5ZMZEg0CwcsJToiDhtbBbWa67bgZupJRlB3RJn33U4qlsrPrzP7aFXhnxN2fC86cZLp+w/QpFBR1nXpTW5HJ/0Hs4CkKyLWQXgw5yJcJ8gUF/ZdZ2S9GTy85U9oUKReY5jShyqVzKJl+1ix5jSdm1emevlCGmNA99zQkopP7r0Iocd/a/AJD+X3tl34uLJubbPfwEIcKMgRiHlMgVE4ve4EIYERTP/9I63nS02dF27acZm532ynXPG8jBnYVOP1xirCNFXnk3Fb97D8wkUehofpfwNgEQ4UZDtEbicwGb9NWMXyGeto2rkGc/75BEdn7f9tSOHRBu+GqcvcqM+VZ1y48ZRJQ1sxoGOtDK81dwdmJg+88jyAvqvXo5RlqufNr/c4wn/miTaFJ21Rn5IxWJblcoA30B51EUohYBXqIhMJOAAMB2YAV1BXL06RJOnHTNzjBaCUJEnFJEmyBfoA23WIdZckKa1kqjlwOxP3IjABqrBwCno7UbVhGX7ZN5EaTctqHauLSCDrFts2brvE4E/+JDQiFoWVRJH8mlthWkrxCWROMmHx8aw4fQWAUh5ezG7YHBsrrU4BSx9RfCKwTITrBFqhCgun67BmzF3xCd2GN9MpVlcfZkWCdfj4XSbM2EBeLxf+mNOXEoVyaYyxBB9mthBz7bUbDN+8jeLunqzr3NtsPCg6gAkMjHCdINME+YYwoeVXPDh5E4DunzTT+piBNEydH/618iRDR6/gwk1fAKy0aBFtCcUn+nrwRWws/VetZ99jn8wVX4JFTDyKAswcgfCdwChEh8cyp9f3OLnYM+23D7HWcKRpGqb24JGT9xg3bT1LVx3TemxzXngD/edFZeDro8fZcEH9c03tfAVxtdXTPxbgQEG2QrhOYFLWL9nFd8N/p3SVwhQorvuxnVlRfKKNB5OSUpgxewv7T99lZN/GfNq7ocYYc3ZgZuZCfUJDabd8Batuq48PdLPLuBPaexH+Mzu0mdUuBKQAKwFkNXtRt8N6BNQG3IHTQFtZlv+UZXm+LMs1gf5APDBKkqS6+txgatXjKGAfcAdYL8vyLUmS5kqS1AlAkqRakiT5Az2B/0mSdCs1Vom6ZdchSZJuoC6O+UOf+xCYljM7L/N5w5mEh0Qz779P6TemjU7xWZlUaZtYJSamYBupZHDnOvwzfwBNapXUGGPsowZMfdxAo9xFGVKpOis69NA/0QJRfCKwOITrBLqwceFmju+4wrAZXRg4vr1OsVnVDUwXH1657svnE1YjSVC5tHZV7ZZQfAKZc+Gxx4/pt3o93547QbJK9z/vGwgPCswQ4TqBoXG3lek9qjU/7ZlA/bYZtyp+G1Mvuvk8DMI2UknlMgVY8fVACuZ11xhj7sUnoL8HE1JSGL1pJ4sPngTUGxLMZeFNTD4KdEX4TmBMHt/0Y8mIP6lQqzgj5nTTOs7UHrx+y594/xhsrBXMH9ORqmULaIwx54U3yPy8qHuSPX+068L6Ln3wdnLWbxCx+CYwEsJ1AnPg4KqTDC4/Fp/rfgA4u+k2x2To4hPQzoNKpYqvv97Btt1XGdS5Dg2rF9cYY84OzMwmhJikJKxjrCjnlZsT/YfSu1wl/W5C+M+s0KbwxApIlOU3T6SSZTkE+Ba1GAB+SueaNcC81GuG6XuTsizvlmW5tCzLJWRZnp/6tZmyLG9P/fUFWZYLyrLsJMuylyzLFV6LPSDLcmVZlivJsjxEluUkfe9DYFp87z5ndIMZHFh1koHj29Pwg6o6xWdVUgW67fbetfUKPr4vWPhlZwZ1qq3xemMeNZCZOMh8kvXv5SuM37WHGt752dKtH4Vd3fQfzIIW3cTCmwCE6wTak5Ks5Ot+P7Dnn6P0G9OGT+d2R9Jit3Qa5tAN7NGTFwwZ8RdbDl0HwN1F83MwJ7jwVnAwB68/BKBzqXJmc+QAiOITgWEQrhMYkrDACEbWn87zJyHM+ONjPpnVVevd3mD6RTcA62gluT2c+X1OXyqV0lyMqc+Eo6UsvMmpH47xNvz9QXc2de1HQRc980ELcaAg+yJ8JzAmxzadY9XCbdy9/ESnOHPoiOmlsqFEoVz8MLlHlmzQs6RjWGOSkhi5dQcFXFzZ3K0fpTy89BxILL4JjINwncAciA6PRRUWTsuetfnz2HRKVy2sU7ypik9UKpklP+9nytzNnLz8SKtxs7MD/Z9HcinwOd80bcPI6nX0G0T4z2zQpvDkGeAkSVJ6ZVdbX/v1+ffEp1Urau4ZJBBoICkhmSUj/uC3WZs5d+CGzvFZlVSB9olVeEQcYyesYf+pO3zapxEzRrTFxlrzBGlOWHAD2Hr7DoPXbcLD3oGt3fpTUt9ECyxqwlEsvAkEAl1QqWR+GPkXG37YTbOuNchdwEPnMUy94BYTm4htpJLiBb3Y8P3H9G6b7pG+b5BTXGgdY0WzwsWY0aAZXzdprf/ROxbiQeFAgUCgL0FPQxjbdDZbft5Hl6FNmbviE53iTe3Cm3ee8enYlcTEJbJsWk+a1ymtMcZYLjSVB5OUSibv2kduR0e2du+v/5nfMdZi8lEgEOQY/v1qEwc3XgBAYa197pBVmxK09WBYeCyjxq7k/tNgFozpRLeWVTTGmHsHsMw48KyvH31Xr0MhWbGxa19q5dPcCSZdhP8EAkEO49aha8TFJPDNulHUbFouS94jK/LB0+ceYBuppGgBT2aMaIudTcbP7+zqwPjkFD7duJ2tt24zoU4jZjdsjl4jGTAHFP7TH21+Ej2CumPJ12+/IMvyCyAx9bfP0wuWZTkUiAL0/ElJIHgTlUpmy6KtJCcpcXZ3pGz1ojrFZ9UxA6C9UJKSlcxfsIPl/52geZ3SFM6n3YKhJS24ZSbRuvjsGT1XruXgk4f4RkbofyOQJROOlr7wJinVi5rafAgEAvPmz2lr+aTmVIL9wwDdJhnB9AtuAMH3Q7lw8ylfDGrG5/2aoKl5iz6dwCyx+GTCtr38dPosfctXZmXHnnjY6+kIC1l4ywoHCt8JBDmD5KQUfpu4irl9l7J71Wmd403twmfPwxn55UruPQ5i/piOVCtXUGNMdi8+OevnR8+Va4lJSmRNp150KllW/xuxAAdmBuE6gUCQhiosnAbtqvC/w1PxyO2iU6wpi0+iohMYN2ktp648YsJHLen3QU2NMebeASwzDrz7IoReK9cSEhdL++KaC1Lfi4EX30zpQOE6gUCgiWcPAvmy2Rz8HwYz669htOheS+tYU+eDAJXz56Vtw/IsntgVR3ubDK/Nrg5MUamYsHsvf164yJBK1eml77E7YLHFJ9nFd9rc3RIgCeghSdJeSZLePhukHNBfluWE9IIlSbIGnADDbrkU5HhUYeF8vqAX81d+SokKutU1mcMxAwD/rj1D/2F/8NAvBAAPV/M7asBUk41PIyKYuvMASSol7nb2fFhJ8074DBG7vgUCQTYlNCAcVVg4vUe1Yt5/n+LgpNvzydQ+TEpWMverbWzecYl+HWoye+QHWdIJzFTHDWTmyIEfTp3my527qZonH1u79cfZxlb/m7GAhTfhQIFAkBlObb/I8f+OANC0Sw1KVS6kdaypXRgZFc+4SWtZsuIwV+/6axVjzOITUyy8PQ4Pp8e/a7gaHECX0uX1HgewCAcKBAKBIXh24xFe3m5M/e1DnY6fA9MWnyQmpjBr9mY2bL3IuetPtIox9yLMzOSCz6Ki6PHfWr46fRQAV9tMuEc4UCAQ5BDCgyKZ0Oorbpx9wPgfBlC0bD6tY019/NzegzeZv3gXVcoWZNm0nrg622d4vSU4UB9kYOHR43y+bQcb797U783TyCbFl5aIxsITWZbvAJ8AKqAVcEaSpOeSJG2RJGkaUBY4lMEQPQAFEGSA+xUI3uC3L/8hJiqeuf+OIG8hT53jTT3BCBASGoNtpJI2DcqxfsnH1KlcRGOMMYtPMhtriOMGehevxKyGzfm+RXvsFLolzm8gik8EAkE2JtjHn0p1S7BgzWc4uzvqFGtqH6pUMj/+dojf/jpK6/pl6d66qlZxlrDoBplz4Y47d+m7Zj3r7lwnJjmTxx5bSAcwgUAgyAzWcTEMGt+euSs+IX/R3FrHmXqyMSlZybb1F7GJUOKdy4Xpn7Qx+G43ME27ZX09GJGQwIdrN/PFll0AeNo76J8PCgcKBIIcwKPrvvzw2XIq1i7BsBlddI7PquITbVyoVMn89Mdh/G6qlxA6Nq2EvZ1hjxwAy+kAFp2YiFW0RC4HR3b3GsyEOg31vwlRfCIQCHIIcdEJTO+4kDkf/8GTuwE6xWbVSQna5oMHj95mxrwtlCiUm19n9sbTLeO5XWN1hDbFJoQ9932QUh347wfdKeLqrt9Awn8mQat+LLIsrwAaAidQH7vjDXQG5gK7gEBJknwlSdosSdI0SZLaSpKUS5KklsAvqAuVDmbJn0CQowkNCGdqx2+wsVUwb+WnuHk66TyGqRfb0rh5/inPgyNZPLEbPbRYcNNXLJZ43MDyC5dYfOIkXUuXZ02n3uR21P3v+SWi+EQgEGRTDq05zVf9llGifEG+3fA5HnlcdYrPSh9q68Q1m84zdto6Nuy7ovX4lrDoBplz4fXAQH4/cRGAKnm8+bhyDf1vxEI8KBAIBPqSlJDMtE7fIkkS81aO0MmHpp5sTKNKvry0aVieX2f2IbeHc4bXZncPJimVxCUnYxur4K/23VjZsSeemTl+zoAIBwoEAnPkyPozbFq6h04fNqZlz7cbl2vGHOZJy3l6MmloS36a1gs3l4yf+dm5+AQgOiSRk4+eMLJ6XRY1a4u1lZ7t9cXim0AgyCGkJCs5vfYEqrBwytcsxrgl/bGx1a543dT54OnzD5k0ayPPgiKJjdNu85m5F59kxoEFrdyomDsvm7v1o3pe7TvYvIHwn9HR+icVWZbPybLcFCgCDAF+Bs4B8aiLUQoCXXhVjBIE7APcUReenJYkKa/B7lwgSMXv3nNmdltM7vzuDJncUa8xzGGx7UVINKPHreLU5UeMG9KC8UNaoFBo/i+a3Sca0/j17HlGbdtBWa/cbO8+gLKeufQfzEImHCNiHSy2AEWSpAGSJMmpH0Pfeq3pa6+l97HQVPctEFg6Z3ZdZlrX7/Au5MXCNSOx0sIjr5NVu71B+yTr0tWnKMKS8XB1YPlX/alSRvNxejnFhdYxVnQvU4EZDZrxTdM22FqZftc35NzES7hOIDBfnj0IZHqX73DP5cK8/0botEEhKycbtXXhkZP3mDJnEwXyuvPH3H6ULJxx5xZjbkowlQdVsszysxeplDsvW7v3p4S77h1PAbUDxeSjTgjfCQSWx5/T13HlxD0KFM+jV7ypi08ePn7BzPlbKVk4F/+b1Yf8edwyvN6Yu76NvfNbKctM33+Q70+eokfZiixv11X/I1iF/96LcJ1AkD0pVtSDlj1rM3/VZ3jkdtEqxtTFJ1eu+zJ95iYSk1NwdbKnXHHNy+rmXHwC+jvwZlAQPVeuJTopkdWdetGueGn9bkD4DzCe63QukZVl2U+W5X9lWf5cluX6gCtQCRgMLAVOAbGoi1HS/jVZAb8DzyVJeiZJ0k5Jkr6SJKmbru8vEKTH7XMP+KrfUtYu2w+Awlr36m9zWGyLT0hm1pwtrNl4ju6tq1KjvHbnklvSgltmJhv33veh16o1+EZF8CI+Tv8bAYtadLO04hNJkgoBPwGa/rUcA+ak8yE6ZAkEmeDasTtMavc1W/48ikqpux+yKsEC3Xa5uSYrcHKw5adpPenSvLLG6y3JhZnhqz1HWHb6LL3LVWJlp554Oeh2rNJLxMJbphCuEwjMn/uXHzO3zw/kL5abuq0r6RRrDi48f+kxn09YhQT8NqsPFUtp3uGV3Xd977nvQ/+1G3CwtmFLt340Kqj5mNr3IhyoFcJ3AoFlolKqmN5xISu+2QlA0bL5sbbRrWjd1MUnJ88+YOzU9bi7OPDHnH6UL+GtMcYYC2+ZicuMA38+c44pe/fToGARJtdrrPc4wn/vIlwnEGRfdvx+iK+H/EKpKoX5ed8kqjcpq1WcqYtP1NcqGT2gCb/O7EOLumU0Xp9di0+eRkTQ67+13HwRzK9tOtGppHZ/h++Qw/1nTNdl+jsty7IKuJX68R+AJEkSUBqoAVRP/VwNdZFKvtSP9oDKEPcgEABc2HcdACtPD6b//hEhAZH88dVWkhK0f5hL4dHIHtpVPoJaKEpX7YoCbCOTSXLL+IxuAJVK5re/j3Ho2B1uhYYCYG9nTUJiSoZxdtEqEl10K7ixiYHkjDs3vzcO9IsFtWRSnGW9Yu++CKH/qg2kOKuwtrLi48o1+OfGFRKVGX9/0iVNNs56xKZDmnAcnBMNMt7rWErxSerz/28gFNgMjM/g8qOyLM82xn0JBDmNe5cece/SI6w8PWjQrgpt+9VjydhVhL/QLnEyBx8GBEby6Zh/mT6tE5OGtqJU0dx8v+IIKRkU06QlWbr4UF+n6etQyJwHZeDHU6fxCQnh2/Zt2NatPz23riUgVrek+CUx1gb1YFY40NwQrhMILIfLh28xvPpkXsQoAbC2UZCSrNQqNm2yUVsfZoULHz5+wYgv/mPY8CY88gvRamxj5YWZyQkz48FrAYF0+3c1v/fozPQGzWi/fgVKWb+xhAMzRvhOILBsUpKVEBaOk5sj36wfRcDTEBaOXEGgb6jWY+iSF2aFB2/eecbIcatYMKebxqPn0tDVg5mZGzW2AzfcuMnT8AhuxQcD6h2/eo0k/PcS4TqBIPtzdMNZHt3wZeqq0cxf+SkTey7jxtkHGuOyKh9MKz7RxoP/++0IBb09mDe6A4W83fln67kMrzeGA02RB4bFxzNozUbGNK/PMb8nur9xGjnUf8Z2nZ6HAmaMrOaeLMurZVkeL8tyM1mW3VEXo/QBvgMOA5FZ8f6CHE5kJL43fekwqCE/7hxHkTK6nf0lhUebvKofwOdRMLaRSiqWysfmH4fRoFpxjTH67nCzxB3f1jFWNPYsypR6TdjYtQ8FXbQ/u/0dLKj7iQUwGmgOfIi6+5VAIDAhqrBw7EihYp0S/LJ/ErWal9c61hx2e8fGJTFtxiZWbzxHt5ZV+aRXQ63iLGXHd2ZcuPveffqsXs+pZ74ExWXiRsDgVf85wIPCdQKBBRHkG4IqLJzi5Qvw5/HpVKpbQqd4U+eGoWExLFy4i7iEZOztrOn3QU2spIz9YQldwDLjwIDoaPqsXMeHuzahlGVsrRQoNHxP3ksO3/mmAeE7gSAbEBsZxw+fLSd/0dz8tGcCjTtW0yne1B70exbGhyP+4thF9SJhsQJeGmMsofOJvh487+9PbGgStlYK1nbuzeCKuv19vsSAHTAtPAcUrhMIcgC+d58zusEMfhn/HzfPPdQp1pQejIyKZ9zEtew9eZtPejVk5qdtsbHOuIOZuR/Bqq//kpRKvjtw4qUDJ9dtjKutHu4xsP8sBKO6LksKT96HLMsPZFleL8vyJFmWW8qyrPknRYFAR1RKFcunr2Nq5+9w83Tmx51j+WBgA53HyUqh6FKAEv44gsCQKL4d14UBHWtpvF6fc03BMotPjj95wvDNWyni6s6OHgNpXKio/oOJ4pNMI0lSOWAh8KMsy8e1CCkpSdIoSZKmSpL0kSRJpbL4FgWCHMnBVSf5vOFMQoMimbviE1r3rqt1rDkUY6pUMv/7+xgzF2zlvx3ntR7fmMUnpnLhzaAgpu08gEqWyePoxEeVq+t/I8KDWiFcJxBYLpGPn5MYl8S8/z6lflvNR7i9jqldqL5WSauKJfm8fxO+HdcZR/uMd8hZQk6YmYW32ORkggNjsY6x4ptmbfirfTdcbG31uxFRgPkOwncCQfbi1PaLjKw3HV+fQKb8MoTRC3tjpdB+WSCrjifX1oMpKSpsI5WULZaXfxcO4otBzQxehGnshTfIXC5oH29NVGIicxq14OsmrbGx0nOZJwcXYArXCQQ5i6SEZLb9egBlaBjehb1o0V3zWlsapswHk1OUfP31Tpb/d4KqZQvi7Kg55zH3DXmZXRes7pKfjyrXYHO3fhRycdNvEAPPg5orpnCdUQtPBAJjcungDUbUnsKNsw/pN6YNzm66H1WSVUIB7ZOrF6ExjBm/moNn7zGyb2NmfdYOO1vND0VLmGiEzO/4PvzwEV3/XU1QbAz/fNCdARWq6H8zYtFNbyRJskZ93JovMFXLsP7AMmA+sBy4L0nSRkmSPLLmLgWCnIvv3eeMaTSLC0duM/qb3hQtq3s3MG3JqmLMY6fuk/AsFmuFFUsmdqN5ndIaYyzJhZnBOsaK3uUqMbNBc35s+QF2Cj19JjyYIcJ1AoFl8+JZGGNbzOXhLX+m/vYh7frV0yne1ItuAPsO32LJz/upU6UYv87sQx7PjNs+67MpwRI9eOGRP/ULFGZz1/4UcXXXbxDhwJcI3wkE2ZMg3xDGNZ/LusU7sXOwRZXBEabpkVWbEnTJCZ9cD2Dzjsv0bludb8d3wdEh48U3c194A/0dGJeczGcbt/PzmbP0LV+Z1Z164eXgqN9N5MDiE+E6gSBn02tkS8Yu6UedVhW1jjH1ZoR/157hw0//JjwqHoWVRL7cGZ8AYKxc0BTFJxefPWPI+k3kcnBkS/f+VM+r2zz3S7J58YmpXCcKTwTZmojgKKZ3WMgXTecQExmPlcKKUpUL6TRGVk0wgvZSSUxMYf787fzx73Fa1y9Hx6aVtIoz9oKbqSYbn0ZE0PO/NWy8e5MrQQH63wQYtNUWZIsdb7kkSbr42sfw91w3E6j2f/buOjqqo43j+HcSCO5WtFgpbbHi7u7u7u4E99Li7u7uUty9UKx4cdeEQEiI7N73jyQtLyW7ezfZ7G7yfM7JCSQ7wySl+TF3npkBWmqaZu5/gtfAACA7EA9IBlQCLgJ1gB1KKcknIcJZgF8Ao+tNZlKvVTy4qf9npSPkIUDCAFfixo7BmB7VaF+vMOZO1o8qxSdzjv7BxGPHqfHdD6yr0YDkseNY15ENctBJWJJ3knVCOLkPHt70rziGP4/coPu4hjY9+QRss+i27fdLDBixkVTJE7BwdGOyfJvcbJvIvut7/V9XabVhM0ljx2Z73aYUS/OtdR1F/gyUuZ0QUZwh0MDiYesZ33wGAGkzp6BiI9sWYlrKkhw0GjVmLTgUVISZIz0LRjYiVXLTO50jcuEtohffNGDKiVN0376TbElTML1sFesGAJEp/yTrhBBmzem+mDt/PWHAzOZkypbG4nb2Lj7x9fXHzctA+/pFWfprM3L/aH6t09GLT8Jy9VzdlWv44OfHmuoNKJde35W6/3De4hOHfY4pgSgiPU3T/rnXu0br4kzZ3pumvSvZ7EhJsN3xyivXnaFrv1Vs3n8JwOzxymD9gpuzLbr5BgQyaOd+bj14A0DX3AX5PnFS6wcTmXe8Gf8NdXNvwBtN0/J+9jb/y+6UUgUIqpicpGnaaXN/vKZp1zRNG6dp2lVN07w1TXujadoeoCRwHygCVAvXr1kIAYCfrz8HF+0HINNPqfkxbwZd7R0hD99/+ESvfmvYufcKrWoV4rdell03EJGLbvaYcAHMPXuOjlu28V3iJGyv05TvEoXhVsvI8OAxHPNOsk6IyMPPx58RtScyt/8q/jh4TXd7R8jCcxce0LXvKl57eOPt62dRG2e4gi4sGXjm8WNqL1/NM+8PTCtbhTjRzc+VQ+VMGShzOyGEFTRNw+jhSdXmRekxviEDZrcgdryYFre398Lbtt8v0XfIepImjEPJfJadAO/oJ4CFJQN/v3WbhqvXMfzEIav7ABw3/yTrhBDhzM/Xn+G1J/De04cRS9qR9BvLr2vRcwKYrU7C3LX1Im88vZk2oA7VSpo/tcWRi0/A+gx84Bm0If3Ykwc88Hpn/QAcpfgkkjzHlMITEaX8Pms3B9ecpEmvioxf343kqS0/Cc8RHjACXLv5jGjvAkmaMA5rJ7aiefX8ZttYs+AGzld8EiK5IQ7Ns+ViS+0mVM+c1fqOInPxSTgJPq5rOXAbGBqWvjRNew+sDv5t8TAOTQhhgtHDk66/1WfUsg5k+im1rra2OmIZLM/DgEADE6bvYeqc/RT5OSMju1q2sysqZOGBO3epv2ottzze8PJjGAYBcgJYMMk6ISIfQ6CBLTP34v/yDQkSx6Ht0JpEd3O1uL0jZOH9h2/o1H05z155AVDk54xm2zhDDoalCPOxlxf1V6yhxa5NfAwI+j7GcLX8v+v/cdTFNxuSvBMi6pndbRGLhq2naKWczNzdjyw501nc1t45ePHKI1p2WsLqXecBSJzA/DUzjn4CWFgy8NqrVzx47AnA2JLl6ZWvCFb1FM6nfzkayTohRAiPF14MrTWB2HFi0txd/4lRtrp+zhIvXnrRtddKzl9/zKD2FejSqHi4nwjtLJsQ3n36RKcN27kfnIG1s/xIdBcrSh8iUfbZO+uk8EREKb7en5jUYQFjW88hww+pmLnHnXylfrS4va2LT/QUoPi/8OXizad0aliM33pWN3uvKTjHg0YI+45vD19faixbxdU3L5lerirDipQimjVhA1HhuOWwigtkAX4APimltJA3YHjwaxYEf2yqBf29Dn5v5R0RQghL/dJwGh8/fOKXlZ1IndH8Mf1fsveDRoAtOy/Sd8h65q47YXEbZ8pCa916/YY267bw3t+PGK6uNPkxp3UPHUGKMINI1gkRieXKk5Y67UsxanlHYsfV9zPKEbLQzctA2ULfM7FfLdxbl8XVzMmeEXXlQFjagfU56BsQyI37r4nm7UL7XPnYVKsxqeLGs24QUW8eKHknRBSjaRrrJ+2kb/kxuLi6MH5jN7Lkcp7ik7ce3rh5GUiRJB6rx7ekR9OSuLqYzo+IPAHMWmGZC8b46IproAs98hZiboUaxI5m5QlgkWgB7guSdUKIfzy49oT+lX9j1uCNVrW3Z/HJRx9/Bg3ewNZdF6lTLhdpUpjfZO8MBZhhkS9+aiaXqcyKqvVIEMPyk9z+EXmKL+2adVJ4IqKkw+tO07nQEB7dfsG7t/qKSawpPrHF5OqTXwBjxmxn5oJDFM2TicWjm/BtqsRm20X0gpu9JlqvP36k+eqNLDl/gdY58rCgYk3rBwKy6zt0fsCiUN4uBr/mRPDvzR7pBRQMfn8vfIcphPjS6ydvGVD5NwB+Xd2ZZKksPwUshC0fNOrZ6fb46ksA+rYsQ4OKuc22iQrFJ0HtXaiZ9kfGlCjH3Ao1rL92QIpPJOuEiMQOrzvNuDZzyZY/E+M3dCdRMn1FCvZedAM4vvc6K9efoVbZnEzqV4t4ccz/nHX0h44Q9hy898yDdPETsr1uM/KntPzu9v8TteaBkndCRFHXz96hW5Gh7F17hoc3n+tqqzcHbXHlwPsHXuw7eI2GlfMwoW8t4pjZnBeZi08MmsbAPfsYffAwZdNnYlPtxqSJZ/kVEv8nciy+fUmyTgjxf25fuI/v05fEihODCg0Lmm/wBVsVn1iSgwajxpTZ+2ndZQmPXwSd+GFug7qjZ2BY5oAXnz2n987f+fmblGyp3Zj0CRJa15Hz559ds04KT0SU9fz+K3qXGsGtI1cAqNaiGIlTxLeord6jlcF2Dxk3bD1Pn8HriBcnJq1qWRaM1oaLsy26BRqNjDl8hF47f2f9zavWDyKELLz9h6Zpvpqmtf3aG7A9+GXLgj+2DkAplfdrfSmlmgINAH9gfYR8AUJEcU/vvGBgtXHEjhuD+l3KWtWHrR40gr48jOWtkSRhHHo2L8XgDhVwi276aH1rd3zb46jJsGThpqvXGH3wMGXSZ2Jr7aZkSKC/wAiI0ieASdYJEfkdWnuK4fWnkDpjMiZt6UnyNOYL+j9ny0U3S7JQ02DBsmOMnfI7uX9My+LRTciQOonZdo7+0BHCNh88cu8+dVasxuuTL6uq1aPpT7ms6yiKZKDknRBRm9ebD8zqshC/TwHEjheTTNksL9iz1TNSPQtvMxccYsL0PeTLlo6FoxqTJkVCk20iMgPtsfi27MJF2mzaQqq48VhdvV7YToKORCTrhBChqVT7Z3pOaETNtiV1t7X3ZoRnL97h5mWgeqnsrBnfkizfmj7Z2tHngWHJv+03btJ83UYSxojJltpNyJdS3xXz/3Di/LN31knhiRBA4pjQckBVJmzsTvLUli/IOErxyaW/HtO+21ImLD4AQKL4sSw6WjKq7PjeceMm+6/cAaDRDzlokyOP9Z1J8Ul42KiUuqOUWquUmqiUmqmUOgusAAxAB03THth3iEJEHfeuPKJ3mdHMG7EZgNjx9B9FqLcg0xZ5aDAYGTFqC0tWnaRqiWzMGdaQFEnM71qPClm47MJFWm7YROJYsdhRtxkl0qa3fiBRYOEtnEjWCeFkzu+7gnul3/D28uWjjpwK4Qhzw90HrtJjwBqiR49GAgvzPLIvvN339KTO8jUcffyAkUVL810i8wU5oXLih482JHknRCRj9PCk22/1mbipu0NdT26JnXuv0HvwehLGj03TavnMvt4Zrp8LSwaeePCQ2stXM/DIPgKN+vP+H5J/knVCRAGbZ+zh5O7LdBheiz5Tmlh1DaulWWirdcK/Lz9FA+YOb0iJfJlNvjaiMtAeG/H+fPqMeivX8sbHh3huYXj2GLXyL9yyTgpPhADePPNkYNVxxEsYhwmbepAqfTKL29p6YmVpsLx+603AC19ifTAyyb02MwbXJ0lC81duOdOCW3hcOVAodVqGFinFrPLViBvd9LFjobLBjrdIvvj2pTnAfaAI0AVoCyQFlgJ5NU1bareRCRFFPbzxFP+Xb4gRy41pO3rTe3Jj3RMssH+Fv6bB0tUnGTR6M+lSJmLmkPq4upr/564zZaG1zjx6TM1lK/nzxVMevfeyfhAgGWgZyTohnNCt8/foVngIH9/7Ei26K+2G1dR19Y4jLLpdu/GMpm3mc+nmUwAK5khvNgudIQfDMh/09ven04ZtNFq7nr893wIQK5pjXD8XCUjeCREJze21lCd3XzF8cVvK1stvcTtH2JBw+epjOnRfxpTlhwGIG9v5r58Lyzzw4bt3nL31BIBW2XMztmR53FxMnw76VeH8LNTJSNYJEQUYjRq/NJjC8l82U6pWXmbucSd91pS6+7Fn8cnd+6/p2GM5dx+/ZmyvGrSsWcBsm4gqPonoDHz47h3Vlqzg0MOg22Hyp0yDq7Kir6iTfeGWdVJ4IkSwm+fu4l7pV2LEjM6Ejd1Il+Ubi9ta84DRVlWNBqPGxo3nyJohBct+bcbPWc0fjRmRV++EJWQg7Kef9N66m9+OHKVChu/YVrcpWRIntb4zOf3EJE3TRmiapjRNW/jFx8dpmlZO07S0mqbF0jQtpqZpmTRNa6Vp2mV7jVcIAf4v33B0wxlK187HrL39+Sl/Rt192PJBo6V5ePLMHTr0WM6kJQcxGCzLOGdYdIOw5eDzD960WbeFx0+DCk965i1M6riWXTP4H5KBgGSdEJGRpmkYPTzJnD4hVZoVYc7+ARSqkN3i9o6w6OYfYMDNy0CWBAmZMqAOMwbVJXGC2CbbOMOub7A+BzXgwtNnRPN2oWS6DBxr0pZCqdNaN4io8/DxH5J3QkQtni+96Ft2NJdP3aHP5Ca6r2W1dw6+ePUe7bUfsWJEZ+7whgxqV57o0cxfxaqXs+z8DmrvQkIVk4Y/5GBNjfoki21+s+JXReIMlKwTQhgNRlb9tpW+5cfg9dYbz9f61v1C6Ck+0XP9nCU8PD/Sq98a9h2+Rof6Rcn+XSqzbRx9Hmht/gUajUTzdiFz9MSsrl6fpVXqkDCG/pO+I1PxZURknRSeCPGZe1ce0afcLxiNGhl+MP8D+XN6HzCC7Y7UOnD0Bh17Lcfbx48ZQ+pZfLykNZzpYWOIRef+pNm6DcSL7saW2o1JGsv0Q1iTZOFNCBGJGAINLB+9ib7lfkHTNMZv6EaL/lVxjabvn4x6HzTaIg+fPPPkwvG7ANQpl4vR3aoSK4bp3c3WLrrZo9o/rFmYMVoiWufIw656zSiVTn+BEWCTE8CEEMJRXD97h65FhvH6mSfDFral7ZAautrbKgv1FGI+ePSW0RN28kPGb1g6pqnNHjpG5MIbhH0++PT5e959+sTKqvVom/OrV1mbF4kePgohxNf4en9iaPWxHN76J5WbFCZO/Fi62tsyBy1lfP2JEyduU61UdmYOrmdREaZezrLzG2DGqTN03baDH5IkZ0edpuRMbvmmy/8j+SeEiOSun/mbHsWG4fXWGxdXF9oNq0nSlAl19WGLIkxL54L+AQbGTNxF136r+OvvZwBEC+dTMJ3p9K8Hnu8Ysnc/+VOlYWfdZvyUNLl1HUn+WUQKT4T4wuNbz2idoy9Ht10AIFYc/Xe56WGr3d4PHr2lQ/flHPnjbyoW/ZEYbuZ/KFqz4AbOefXOuSdPqbFsFSNPHOKNrw8AVvcmC29CiEjm+tk7dMo/iH0rjpOjYGY0TX8fjlKM6eZlIFagolSB71g0ugnpUyc228aZstBaj955UWv5Kp55f2BJldr0K1DUuiMnQSZeQohI69HNZ/QoNoztc/dTp0NpGveooKu9I2ThgSPX6dR7JX7+gcweWp9aZXOabRPZF97ue3pSd/lq9t6/w5DCJZletopcvSOEEF8RGGBgXNPp9Cw5go/vfXFxdSG6m+XXtNgqBy19PqppsHjlCYb/to0s6ZOz+JemfJ/B9GKTM5wAFpYM3HP7b+qvWkOA0cja6g3k5BMhhDDB6OFJupRxqNioELP39adoZfNzqc/Z+wSwv64/xc3LQK6sqVk3qTVZM6Qw+fqIykB7bMTbePUajVavx8VFsalWI2pn+dG6QUj+mSWFJ0J8hZ+PP0YPT3IUyszS08PJUSizrvbWHK9si2Dx9fVn1Oht9Oy3Gj//QGLGiEamtOavlonoBTd7Lbq9/viRzeevE83bhXwpU/N7veZkTpTE+sGEc/GJrQpQlPHf77u5NyFE1OXr/YkpnRfRv8IvGA1GEiSOQ6UmhXX14QjXDQCs33qePoPXkyBeTBaPbkqFIj+YbRMVik8evntHvRVrWXf5L7rkLsiMctWsH4gDTrwk74QQ4SEwwMCsPitYMWYLR3dc1N3eEYpP7j14TYfuyzl9+YHFfTtDDoblwePHgAC6b97JhKPHqZLpeypnymLdIMCuGShZJ4SwNU3T8HjhhdHDk86j6zB6RSdix7P8mHpHmBMeOXGLrn1XoWkavZqXtqiNM+z8tjYDb75+Q+3lq+h/ZC+vfT5aNwCIsNO/JOuEEPZ078ojuhQawrMHrxk8rzU9xjckRiw3i9vbOwMBPr3wQSmYO7wBZQt9b/b1jlx8AtY/C73y4gU1l63i4svnpIgT1/oB2Cj7IkveSeGJECY8+vM2Hq/eM2p5B/KWNL9I9SVHeMgI8P7DJ9y8DHRsUIxFoxtTpcRPZttE5HUDIW2tFdajlgHcPrmSNHYcdtRpSt3vzX9/QiVX7wghIhn/TwEYPTyp2Lgw3cc2YNTyDiRKHl9XH7acZFmahxevPKJt12XcvP+SYZ0qkSG1+ULDqHD1jl9gIIP37afv77tZd+OKdQMIIdcOCCEisZW/buHx+VsAFCirb77gCItu3h/9GDp8E7s2BZ3sWTBHetKkSGiyTUTnoLXCMh+c98c5qi9fyaZb1wCsu/MbJP+EEFHCX4f/4qd8GZmwsTuJUzjOnNASf997RYfuyxk6YycAMdyiYe7Ax4g8Acxa1magp+8nfr90m2jeLhRJnY7lVeqQOKa+65T+IRkohIjknt17Ra8SI1gzfjvlGxRg0JyWutrb+/q5ew/f0LHHCm7ce8noblXp2rg4ri6m8yOiik8i+vQvDx9fWq7ZxILj5wHI800qksUyfRXfV8kz0FBJ4YkQJni88KJfudE8uv2SYYvaUrhiDt19OMrVOwCrl5/iyu1nDOlQkYHtyhMjumXX7+jljFfvnH38hOpLV3Lx1XMmlq7EpNKViO0gxy1L8YkQwhGsGbWeGT2Xkb1gZubs7687Ex3hJLC3Ht70cV9Dv2EbuP/0LQCxY5r/We9MWWitrdducPLGIwA65MpH55/zh+0KOiGEiKQKF8vEiCXtaTVA/ylR9i7EDLk6L/ZHDfc2ZVn8SxOK5s5otl1kv3rn1us3RPN2IX2ChBxt0pZueQpal4Hy8FEIEckdXneaoXUm8U26JEze0pPUGU1fW/Mley+8vfPyweu+FzHeGxjVtQrjetcktpld65E9AwGSu8SlQKq07KjbjBzJvrGuE8k/IUQkZwg0sHTkRvpXHsuqKXsAiBbdFWXhtdX2vn7unZcPffqvZfOOCzSpms8mp0Hb4/QvaxiCJ8axfKIxo1xVdtZrTu4UKa0bhPgPKTwRwoz3b73pX3EMf195zKA5LcmSK53uPmxZfAL6JljuA9ezfO0pqpfKzrwRDUmZzPwOhah09U7LNZuYdvIUtbL8SO3vrbznDcL9oaMUnwghHMHOBQfpUmgILx97MHRBG6q2KKqrvSOcBGYwapy/+AA3LwM//5CGzdPaUSKv+Sv1okLxSVB7F75PnBT3gsVZVLm27PwWQogvnNh2np0LDlK/S1la9K+qu70jFGIGBhrp6b6GJy/fMaFvLdrXK4yLmQemzpCDYd2M8OalD4fv3KNP/qLMq1iDeG6WH6H9fyQDhRCR2IWDV+lX4VdixHJjzKpORHdz1dXe3gtvEFSIeeHcfQrlysCCkY1InTyByddbewKYNexRfLLjxk3qr16LpmlsqNWQBj9kt64jyT8hRBRw5fhNbh6+DEC7oTUZs6qTxaeA2fskzMBAI9PmHqDPkPXsPnEdgOjRTOe4o5/+FZY5YIDRSNsNW/ENDGBtjYY0/SmndYMQ/0cKT4SwwEcvHwZW/pWFY7bz9+XHVvVhTajY4iGj0aixaMUJ+o/YSBwdd9FFlat3jJrGjFNnqLViFauuBf0DImWceNYPRiZdQohI5snfL+hZfDhLR2zg+M5Luts70klgb++/4+krL8b2rkG3JiVwdTX9T2NrJ1vOtuPNfftehu8/SJE06dhZrzk5k4dh15vkoBAiEprZazm7Fh2iYddyNOtbWXd7ez9wBHjx6j3de69i174rtKpViEn9a1v00NHRF97A+hz8FBhI39/3MOrgYUp/m4mtdZqSKWFi6wYh+SeEiMTuXHpAr1IjmdpvDQH+Bt3tHWFDwpadF+k7ZD1JEsRh8S9NyPuT+Y2GzrDz29oMvPbyFbWWr+KPZ08YV7ICxdOmt24Qkn9CiCjC6OHJ3XN/80Oe9Mze15/sBTNZ3Nbec8HzFx8Q/Z2BFEnisW5SayoWNX36SUTNA+0xB7z95g21lq3m+OMH/FK8HBNKVSSai5ROhIV894Sw0KePfmwevwVN0/gmXRIqNipkVT+OsuB25tw9mrdbyPPX7wGoWiIb0cwsuIFz7HSDsO92u/byFa7eLiSLHYff6zdnapnKxInuGFfvCCGEvRkCDayZsAPPvx8T3c2VYYvakqtIFovbO0ox5ouXXnTvvYrNOy7QuEpeZg2pT7LEcU22sWayBc6363vVpcs0WLUOTdNYW70BSa257zSE5KAQIpLRNI0ZPZaxe8kR6ncuS9rMKazqx94PHP0DDIyftofx0/bw8JkHAYGWLR46+sIbhK0Ic/mFizRbt4H4bjFonu1n6wch+SeEiMSe3XvFhe1nAajUpDDl6hfQ1d4Rik8uXnlEx54reO35kaEdK+IW3fzpLY6+8xusz0BP30+0WbeFXjt/59jjBwBmT0T7KtmAIISIIvYsO0rXIsPweuPN6BUdyVfK8hP07X39HID21p/nr70Y3rkyPZuVDPcNec6yEe+Dnx8dN2xj+snTRHNxIdCoP+vFv6TwRAidjB6e1GxTgh7jG9JnchPiJoiluw9HuXrHYNRw8zJQMH1qBneowLwRjUjzTUKz7ZxlxzeEfde31+tPLDl3gWqZs7KjbjN+Sqrv/tp/yKRLCBFJxTL6803aJIxe0ZEKDQvqausIDxsDAg1Mm3uAEWO3kzldMkrnt6yAxhmKTyBsOXj15UtqLF1Jz4O7eOPrEzQWa6v+JQOFEJGMpmlM67aEHiVH8vjOSwBixtZ/NYsti08szcJd+64we8YBAL77Nhm1yuQw2yYiF97scQLYuSdPqbF0Jb+ePgJAmngJiOGq7zoJQOaBQohIT/N8R6Hy2ek9qTHdfqtPrDiWXxXtCCeAPXvxji69VtJnwhb8Awy4KGX2hOiI3Pkd0Rlo1DR23LhJNG8Xvo2fkAMNW1Ektf5r54UQIqp4fPs5fcqO4tHtl/Sb3ozY8Sy/stqW189Z4p2XD30HrGP91vM0qJSHGYPqkjiB6Y1njr4Jwdr804Dpp07Tb9seANInSEih1GmtG0QUJ4UnQlhhbs8lrBq7jVK18jDv4EAKls+muw9rQsUWEyyAC5cfMXTMVtKkSMCyX5tRtYT5r8dZdnxD2K/emX3mLE3XbSBWtOhsrt2Y1jlyWz8YeegohIhk3r16T+/SI7l08jY9JzSiw/BauFhwglYIRyg+ATh8/CatOi1m/d4LAGRKm9TsSWDOUogZlhx87+fHwb/uEc3bhVLpMrK/YSuyJ7NuZ79koBAistE0jTuXHmD08KRo5ZwsOjaE/GV+0t2PI5wCFvRaAzVK58C9TTlGdq1C7JimT3x0hvlgWE4Ae/XxIwYvjVg+0VhWtQ6bajUmfYKE1g1EMlAIEUlpmsaI2hNYP2UXFRsX0n3dANh217clOejr68+jv17g5mWgde1CLP+tOT9mMn/dqKNfPxfWUzCj+wbt+l5RrR5dchcgbFv7hBAi8nr/1hv3Cr8wvOU8fD580tXWlsUnlmSgwWBk1oJDjB6/g6wZvqFNHfM3PTj66V9hzb9o3i70K1CMlVXr0TZnXqv7iaqk8EQIKxgNRpaP3kS34iPwfPOB4YvaUaKG/mIEvZX9YLtdbsdO3aZ15yVcv/uCwR0q0LtFaYvaOcPDRgh72Jx78pRqS1Zw7PEDciZPaf1AQB46CiEiHZ/3vgytPo7NM/dQs21Juv5aT1d7WxZj6snCl6/fE/2dgcSB0Zg9tD5zhzckZbL4Jts4SyFmWHMQwPudH9FdXNlUqzEts1t59YDs/BZCRFJPLt/l3VtvRi5tT6+JjXTtdAPH2PUNMGPaPuYvPUqZgllYMqYpmdMlM/n6iNr1HZZ2ELYizACjkfGHjpEmXnx21m1O9cxZretI8k8IEUkFBhhYNGQdfcqNwWAw8tuaLiRPnUhXH46yIeH8yTsopZg7vCH1K5qf8zj6zm+wPgMfeL6j7vI17Lhzk34FijG/Uk3iu1l+oo0QQkQlPu99ubb/IgBVmhel7dCaKAuvK3OEueCBozfo1HsFs1YfAyBubNM/7yPy9C9rhWUOOGjHPvbev8OQwiWZXrYKsaKZ3pQh/iWFJ0KEwd3LD+leZCiz+67g1J4rAMRPFEd3P45y9c7rt9707b+WOYuOcOz8HYv7d5Yd3xC2sHn36ROdNmxn4I59AGRMmIiCqaw8bksW3oQQkYzRYGRe/9VM6rSQbYuP6W4fEcWYlvL+6MeEaXtJlzIRy35tRqn835lt4wzFJxC2HLz47Dk1lq7k2OMHjChahrkVqlv/4FEyUAgRydz76zHdiwxlzfjtlKmbnzn7B5CzsPn8+JK9r97RNFi14Sy9Bq4jVkw3Fo5qZLNd3850AtjBu/eotnQlN9++Znq5qvxWorxcvSOEEF+4fuZvOuUfyKhG03n11BOApCkTWtzeERbert96TruuSzl96T69mpdmbK/qxDNzfZCj7/wG6zPQJyCAPlt3M/LAIUqkzUDn3AWsH4QQQkQBRg9PUqeKT532peg3vRnRols+Z7DlCWCWuP/wDYEvPxEjejRmDanPyK5ViBUjfE/BtGY+Z4854MeAALpv3smEo8epmjkrm2s3JkWcuNYNIoqRwhMhwigwwMC2Ofvxe/6amLHdmLazDwNntyRBEn0/hKyZXNligqVpsHbzH1w5dR+AdnUL07lhMYuuG3CmRbewPHT0NxiI5u1Cn/xFWV29Pr3yFcbVwurV/5CHjkKISGbf8mPcP30dgNaDqpOraBZd7R3pJLC23Zbx6Lknv/asTr/WZXB1Mf2z3lkKMcNahNlxwzZ+O3yUMt9molyGzFb3JRkohIhsAvwDWTpyI71Kj8LP15+ESa17MOUI88LLVx/TtstSNuy9xK0Hryxq4wzzwbDMBZ9/+EDT1RuYe+YPMiVKTKBR/9crhBCRnZ+PP2d+v4jRw5NsBTKx+PhQilfTd1qiva/e+eD9iaHDNzNzwSHyZf+W1MkTmm0TkTu/7TEPXHHxEvVXrWXq+VMAxHNzs7ovIYSI7Oa6r2LRsPWUqpmHkUs7EMtMAePn7H31DgBv/Thy5AZlCmZh4ejGpEtp+hQzRy/ADEv+zfvjHK03bub1R2/efdJ3IEBUJYUnQoQjvxdv2L3oEIXKZ2fewYEUrZJLdx+OsuAW9HoDiWPEpFn1/CwYZT5gIOIfNtpr1zfAwB372HLtOj3yFmZ19fqkjBPPuo4ieNebMvz7vTP3JoQQ1ooZ4Eu+0j/yy4qOVG1RVFdbRynGfPHSi+59VrFm0x/EjumGwaiZbeMshZhhLcJcdP5PKi1ZxqZb1wD4LlES6+78tmEGSt4JIezl1vl7dMo3kMNLDwFQpk4+fsqfUVcfjrDr+52XDwvmHsbVI4AE8WIxZ1gDs6efWLvw5iw7vwONRiYeP0Hz1RsxaBqJYsaixnc/WD+QMJKsE0I4srunrnPz4gP6z2xOxUaFdLV1hNMwN2w9T8NW87h5/yUAuX80f+qxo2dgWOaBV1++JPCdkdjRorOldhN+LVHOutO/dJKsE0I4o/WTdjKxw3xyFs7M2PVddZ98Ys+5YMgpmH2HrCdR/NgsHt2EEvlMbzxz9Kt3wpJ/Jx48pPW6LfgZDMRzc6PZT7msG4QZkSXvpPBEiHBkCDSwduIOuhQZyovHbxk8txUt+le1+C63EI6y4AYwZfZ+hvyyhZRJ47N0TDOql8putk1EPmwMaWutsCy6+QQE0H/3Xvrs2s1PSVOwu35zfkqa3PrByM5vIUQk4vPhE71KDOf84Rt0+aUeXcbUwzWavn96OsLDxsBAI3MXH2Hs2J0AfJsqMRWLml9gcobiEwhbDj7wfEc0bxdSxInLltpNWFKlDoljxrKuM8lAIUQkE+AfGPQLLy/qdirD+A3daDukBm4x9d0Nbe+rd0KkdI1JiiTxmDe8IU2q5sPcFDcid73ZY+dboNFING8XWmXPzbSyVZhUuhKx5d5vIYT4Px+9fBhcdSx/Hr1Jj/ENqd2+lK729l54A/B674ubl4EcWVIxa0h9xvepSfw4MU22cfSd3xC2DDR4GTlw6y6Nf8zJhpqNSBMvvvUDEUKISGz/yhMMrz+Fw5vPExhg0N3e3hl44fIj2nVbxoNnHrSuVcjsSdBgXQGmXvaaA0bzdqFe1myMLl6WuRWqEye6zP++RgpPhLCBhzee0qvECH5ffJisP3+Li6t1P8wc5fST46f/plXnJfz19zP6tipDmhQJLWrnTItuYQmcbddvUGPZSg4+vMcdz7fWDwRk4U0IEan4fPjEiNoTWD95J1WbF2XQ3Fa6+3CULNS0oJPAGlTMzfDOlRnSoYJF95xG5I43e51+8valD+OPHqNQqrTsrt+CgqnM7wT8KslAIUQkZDQY6VFsGLsWHqZOh9LM+L0vWXKl09WHI2xKuPfwDW27LOXYn3fp2rg4k/vXIVH82CbbOMN8MKwZOPPIGaaeOEXN735gR92mZE2c1Oq+hBAiMvLz9WdErQkc33mRdkNrkqOQ/qs6HaEI8+a5x0yfe4CCOdOz9NdmZPsupcnXR+ardwyaxsTjJ+i4ZRvpEyRiR91mlEib3rpBCCFEJHdu7xU2T9gKQLYCmfguh75nZra8fs4Sr998oEef1QwYuhGDUSN2zOgkim9605kznP5lreWnLjHm0BHKps/M1tpNre4nMnOKwhOlVEWl1C2l1B2l1ICvfL64UuqCUipQKVX3K5+Pr5R6opSaGTEjFiLo9JNp3ZYwtPo4DIFGEiSOQ6Jk+q9iseWCG1geMG89vHEfuI4ufVbx5OU7IGjXtznWPmyM6MkWhC1wHr57R//tezF4acSN7sa8CjXInCiJdZ1F8NU7wjFI1onIymjUWDR0PRPazWPfurNW9WFNFtrqJLAZ0/axdPVJKhX7iSVjmpI5XTKzbZxh4Q3CloOrL12hzsrVePv7sbp6fXrlK2z9QESkJnknoqJPH/2Y2WsZA6uPJ1acGEzc1INkqcxfY/o5RzgFzPujHyNHbWXijL38nDU13ZoUN9smsl+9Y9Q0Zp4+Q/P1G4nrFoNtdZpSMl0G6wciIgXJOiH+X2CAgV8bT2NMs5lcOX3Hqj4coQhz044LdO27CqNmZM7QBtSr8LPZNo6egWEpwDxw5y61lq/ihfcHeuQtbN3Vq8JpSdYJoY/m+Y6OI2ozbn03cpfIqqutreaClhZgBgQaeOvhjZuXgb6tyrJ0TDN+yuw4BZjWCEv+LfnzAi3XbyJxLCtPfY7kHL7wRCnlCswCKgE/Ao2UUj9+8bJHQEtgdSjdjAaO2WqMQpji/ykAo4cnfac1Y8r23qTPavoH8tc4ytU7mga37rzAzctAoVwZWD2+JV0bFyd6NNP301kTMuCcp58AZHJLTJ6UqdlRpymNf8xhfUdOVHyilBqnlDqolHqslPJVSnkopS4qpYYrpb5agaOUKqyU+j34tb5KqStKqZ7BP/ejFMk6ERUcWH2S0+uOA1CxUSHylNQ/yXKERTeDUWPJqpP0HryOOLHcWDiqMbmypjbbzply0Fo3X7+h1rLVbLl2nSSxTO+Cd0aSdWEneSeiugsHr9Ih70Amtp/P62eeALquobP1lQOWZuGOPZfp0HMFM1YdBSBBvFi4upr+OiL71TtnHz+h2tIV7Lx7iyuvXljdj71J1oWdZJ0QX2c0GDm2+Q+MHp58+31KOgyvhYsFR/Z/zhGu3rn59wvad13GsT/v8tHHz6I2kfnqnYfv3lFvxVo6b96OBsR3i0HCGKavInIEkndhI1knhH6apjGkxjiePXjNyCXtKV07r672jpCBABvX/0GAwcCcYQ2oXTan2ddH1tO/zjx+TKVFy6z7QyOIvbLO4QtPgPzAHU3T7mma5g+sBWp8/gJN0x5omnYF+M/fYKVUHiAFsC8iBitEaJYMXo1rNBcmbe5J3pI/WNWHo1w3AHD1zAO2/X6RJlXzsXBUY5uefmINe55+cu3lK6ouWc6550/4tUR55laobv2ky3mKT3oBcYD9wDRgFRAIjACuKKX+7ww5pVQNgiYXxYEtwEzADZhC0M/5qEayTkQdXl5UalKYkUs7ULNNCd3NHSULL155ROvOS9h84BLX7li2wBQVrt7xCQig/+69jNp9GICfkianVLqM1g3E8UjWhZ3knYjyPnr5cGTDGYwenuQqkoX5hwfp3pzgCJsS7j98w8fH3sR4b2Bc7xrMGdaAlMnim2zjDEWYYclADx9f+m/fy/s3frgqxeTSlciR7BvrBmI/knVhJ1knhBm586WjZtuS9J/ZgmjR9a/Z27sIM+QEsAM7/wKgTMHvyZEllck2kfnqnU+Bgbz56EM0bxfGlarAzrrNyJY0hXWDiDiSd2EjWSeEFTxeeNG37Giunr1Lv2nNqNOhtO4+7H31zp17r2jfbRl/XHlAv9ZlGdapEjHcTK9hOcPpX9bw8NV3+4Qd2CXrnKHwJDXw+LPfPwn+mFlKKRdgEtDXBuMSQpc7lx7Svehwnj14zYil7Wnco4LVkytbTbDA8oDx8wtk6pwDDBixiWSJ47J0TFOqlcxmtl1ELrqFtLVWWIpP3nz0oc26Lfx6+Ailv83EqGJlrB+Ic1y9E1/TtIKaprXWNG2ApmndNE3LB/wKpAIGhrxQKRUfWAAYgJKaprXRNK0fkAs4DdRVSjWM+C/BriTrRJRhNBjpV240Z/b9RYcRtek3rRnxEuo7HcNRrt7xeu/L3FmHUG/9iRPLjfkjG1E4l/nj9Z1h4Q3CloNGTSOatwtd8xRkSZXaDClcEjcXp98IJlkXdpJ3Qnzm/eMXuMWMzqTNPSlaJZeuto5wChgEnYq5ZfN5MqROwvLfmlO20PcmXx/Zr94JkdYlAQVSpWVjrUa0yZHHma4fkKwLO8k6IczYNH0P8waspni1nxm9vCNJUybU3YcjFGECxPxgpGXNAswa2oDm1fPjokz/xHf0DAzrSdDzT55DKcWm2o2s7iOCSN6FjWSdEFbyee/LkGpjObr9ApmypbGqD3tfvePt7cfgYZtYtOI4BXJ8S4K45q+cicxX7zgwu2SdMxSehEVn4HdN056YepFSqr1S6rxS6ry/ZtkReUJY4+1zT/qUGcWx7RcoWy+/2eOITbH1BMvSSdbpc3dp3XkJF248JiDQYPGf4Synn4QlcDRg8fkL1Fu5hnFngq6XSBgjJjFcHb6IRDdN0z6F8qn1we+/++xjdYFkwFpN085/0ceQ4N92CvdBRl4WZR1I3gnH8emjH6PrTWb56E0Ur/Yzs/f3J2ZsN119RMTVO3oeOCbFjVgxojPJvTb925YjVozoJl8fVRbe+m7bw8qLl2ibMy9b6zThu0RfPcnRKUjW2Z3M7USkE7I54dHfLxg8txU9xjckRizL89CWV7LqycHDJ27RputS7j95y+huVRneuRKxzXwdkf3qnSde76m+dCVHHt1jaJFSLK1Sh2ROcA2dZJ3dSdaJKGPzjD1MaD+frLm/Zd7BgWT80aI16//jCNcOGI0a3fus4vAft+nUsBgzBtcjeeJ4JttE5gz868VLai5bybTzp637gyOI5J1dyXNMEeUF+AfyW+NpTGw5E4AMP6Qid3HTBfxfsvdGBE2D5WtP06zdQl55fEApKJX/O7PtIuvpX47IXlnnDIUnT4HPj3tJE/wxSxQCuiqlHgATgeZKqbFfvkjTtPmapuXVNC2vm4oR1vEKYdKnj36MbTqdbkWG4vcpALeY0WnSqyKx4uj/u2fLcAHLJ1kenh8ZOHgjh3ZdBaBayWxUK5ndbLuocvrJtVevePnCm2jeLkwuU9lZjpwML9WC31/57GMhZ8jt+crrjwE+QGGlotQPZJtnHUjeCceiaRqrxm6jW7FhrJm2j08+/gDEjqfvajJHOf3k+QsvOnZbzqoNZ6heMjvLxzYnx/fmH546y0NHa3PQLzCQEQcO0W7TFpLHicuOuk3Jmdzprh0wR7LOcjK3E+ILb5970rvUSNZO2E75BgUoVTOP7j7s/dAR4MVLL3r0WcXilSfImiEFmlEz28YZijDDkoFenz7ReeMOhu47QIFUaZhdobp1g3AMknWWk6wTwkIHVp2gQ75B7N9wlge3ngPgGk3/coW9r9756OPPL79s57cpv5M1YwqW/9aMhPFM7/6OqJ3fYWlnbf55+n5i/rFz1v2h9id5Zxl5jilEGGmaRmCAAaOHJw26lWPMqs70GN9Q13NRW29EsIT3Rz/cvAyUyp+FX3tWZ5J7LRLFN11w7wxzwEjOplnnDIUn54DvlFIZlFJuQENguyUNNU1romlaOk3T0hN0dNdyTdMG2G6oQljug+dHjB6e5MyZisY9KzDv0EAKlP1Jdz+2DBfQd7xk0OsNFM+bmUHtyzPZvbbZKn9wrisHwho6y85eIK6bG1tqN6Zr7oK4mjmC09kopfoqpUYopaYopY4DowkKsM8nECHlu7e/bK9pWiBwH4gGZLT1eB2IZJ2Isu799Zid03di9PAkV5EsLD8zgspNi6B0/HyMiNNPLBEQaGD+0mN0778apaBN7UIWtbM2B53p9JPD9+5TZfFylv51kauvX1o/CAcgWRcmkndCfIUh0MCSERvpVmwEe9eeASBV+mS6+nCEXd8Go8ayNado23kpvn4BxIgejUaV8xDNzEmfzlKEaa01l69Qc/kqhh8/COAUJ2BK1oWJZJ0QOrx48JrZ3RYR+Pot8RLGZsGRwdRoXQIXF30/d/XkoK2eje45cJW2XZeyaPNp3n0I6j+qX73j6CTvrCZZJ0Q4mtBiFusm7aRc/QLMOziQfKV+1NXe3lfvAJzYd4Mps/eT58e0rBzXwuxV5HL1TsSJ6Kxz+MKT4C+oK7AXuAGs1zTtmlJqlFKqOoBSKp9S6glQD5inlLpmvxELoc/Z3ZfoVXo0Hz98YsSS9gyY3YJEycwXa3zJka4bGDJsE1Pn7CdX1jSsGt+CaiWzmW0T0Ytu9lp4O/nwEVUWL+f3e7fpW6AoG2o2IlVc/f+9I1DSkCMNg9/am3l9X2A40BMoSlCFZHlN015/9poEwe+9Qukj5OMJrRuy85GsEyLIs7/u8feVx3T7rT5D5rfWfRqYLU8/0ZOFf11/SpvOS/lt3E4AkiaMQ6a0SU22sWbCBc51+slbHx8mHjiB+qBIFDMWu+o2o2Q60xPRCKQn7yTrrCR5J4Rpdy49wPDWg4RJ4zF1Ry9GLetAgiRxdfXhCFfvBAQacPMyUPqnjHRvWpL5IxvxbarEJts4w2aEsGTgnbce3H74lmjeLva8dk6yLgJI1glhPZeP3jy+85KOI2szfmN30mRKrrsPexdhPn3+jm3rz+PmZeDHTN+wekJLsn2X0mSbiFx8iwK7v+U5ZgSQrBMifAX4BbB42Hp6lhzJBy8fRi3vQJFKOXX14QgbEbbuukj7Hst5+86bSe61aVmzgNk2kn9Wc9i5neNvswA0Tfsd+P2Ljw377NfnCDrOy1QfS4GlNhieEGF289xduhQYRL1eVWgyoCax48RkWIt5uvsJCRYtkWWFDCHhYohv+vjHEG5eAfgniG72dZoGW3Ze5Oz5+/TrU4lB7Stw++Erbt1/ZbJdSMj4xdNXExfdGwL0PY8NUzv4N3AC45o/QvpL7/386LN1N4ey3qNT4fx8DNB3qkxYuRh1BfobTdPyWvpiTdO+AVBKpQAKE1Q1eVEpVVXTtAu6BxuFSNYJEbTbrX+FX6jVpQLtfm3I5G29GNVmAc8fvrW4D71ZCEF5GN5Z6Ovrj6+vP25Al87FKVPwe+ZvOMnqnecxaqFnR4wPRqfJQWsyMESiwJi4KMXSKnVY9tdFfj19FD9DoNX9fY2t8k6yLmwk74Qwz+P2I5aO2EiHsY2Zva8/k3qt5MKxWxa3V54f7J6DAAeP3sDfP5C+PSqydExTpq86ypYDl0N9vTXzwZAHiHrzzNp2EPYMvPXgtfkXWUiyzjFJ1glhHc+XXgytPo4yjQrTaWIzZu1xZ+WUPWyccxDNxBzqS3pyUM+z0ZCFN0tzMIaPhlt0V+YOb8jSLWdZsvUMBkPoP7P1zgXDkoH2yD9ryXNMxyRZJ0T4u33hPt0KDaFK29KcPXDVqj7snYEPHr2lc48VtGtRnIs3nlg0Dsm/IJFlbufwJ54IEVUEBhhYM347HQsMYt7ILQAkSBxH9xHL4Dinnzx78Y7e7mvoMWDNP0UnWTOkMNsuqpx+suvmLaotXoGX3yeiubgwuFBJksUyff+ds9A07aWmaVuA8kASYPlnnw6pjkzwn4b///F3thmdEMLRbZm1l8E1J5IkRXx+LpbVqj4cJQsBZs86yPE/79KlUXFmD2tA6uSh/fgLYu2ON2c6/eTROy/qLF/D4vN/0iL7z+yo25Qfk+j/N489SdYJIWxp54KDdCs+nPeeHxmzqjNth9Sw6TV0tjr95Pjpv2ndeQmXbj3FvXVZOjcsZrZNZD/9JOKX7KwnWSeEsIeDa07RLvcA/jh0nR/ypNdVdBLCEXZ+X7v5jDadl7D/0HXa1CnE/BENSftNIpNtIvIUTGtEpqsHPid5J4RwFAH+gWydvQ//l29ImDQek7b0JEvOdLr6sHcG+gcYmLXwMDf+eARA+3pFaFgpN6ams3L1ju1FVNZJ4YkQDubJ3y94fP4WRg9PWg6oxuz97tTrXBbXaPr+d7Xlg0awfJKlaXDpr8e4eRnImCYJC0c3Zkr/2iRPbLrqMiKvHAhLOwhb8YkGRPN2IVeclDTLlpO9DVpSMeN31g/GwWia9hC4DvyklAq5ZyJku2aWL1+vlIoGZAACgXsRMkghhEO6ePgabXK58/vKkwB8k07/sfSOkoVe730ZOWorv0zcScY0SVg+tjl5fzI/aYzsOehvMPDr4aO03LCJBDFi0i1vIesHYUeSdUIIW3lw7QndCg9hx/yDJEgS1+qFNz1skYNvPbwZMGg90+YeYMeRoJ17ri6msyOiizCt5ewPHy0lWSeEiGieL70YXW8SYxpOBSDlt0lo3KMC0aK76urH3kWYH338+W3K7wz7dSupkyekVH7zz/zk6h37kbwTQjiSmAY/EiePz7gN3ShYPpvu9vYuwASI+cFIxjRJ6NGsFJP71yFJwjgmXx8Rc8Conn+2zjopPBHCgS0bsppzB6/TemA1pu3so7uyERxrx/fT66+YPvcAOb5PzerxLahWMrvZNs5y+klYKx4vPX9O9WUrefLhPXMr1GBS6UrEc3Ozuj8Hkyr4vSH4/aHg9xW/8triQGzglKZpfrYemBDCsXm9/oDRw5MUaRMza687vSY2IrqbvgeN4DhZuP/wdVp1XsLxP+9y68FLi9pEhdNPTjx4SNUlyxl0dL91f7hjkKwTQtiE/6cAZvZaxqRWswBI910KytU3f1f256zJwfBeeNM02LzjAi9vvQFgZNcqDGhbjtgxTR/VHJE7v6Pyw0cLSdYJISJcgF8ARg9PCpfMQrO+lZm+qy+Zs5u8veM/HKEI8+jJ27TstJhVO88BkO27lCSKb/rUY0cvwIwMu79DIXknhHAIz+6+pEeJ4Ty89ZyhC9pQvaX50yO/ZO8CTKNRY9iILUycsZdc36dmxdjmFM2dyWQbZzj9JBKwWdZJ4YkQDszjhRej609mRIOpJEgSl8nbelGqlsXXVP7DUXZ8axps3XWJVp2XcPP+Swa1L8/4PjXNtosqp5/c8/Ck/oq1zDh1mhrf/cD0slWtH0gEUkplUUr95wgupZSLUmoMkJygUPIM/tRG4A3QUCmV97PXxwR+Cf7tHBsPWwjhRF5cucfm6bsp36Ag49Z3I1Hy+Lr7cJQsfP3mA7/+ugO/Zz5Ec3VhyoA6lC7wn2Ly/4jsOejp+4kPbxz3OZ1knRDC3oxGDaOHJ9VaFqf3pMYMmNWCOBbcxR1Cbw6CbXe9vXjsSbWS2Vk+tjk5v09t8vXOcPpJZFh8k6wTQjiyDVN+Z1jdycRLFJup23tTo3UJXe3tfe0AgIfnR1w9A4nlrTGqaxVWjW9BsTyOs/gWVRbgJO+EEM7k3av39Cs3mjP7r9JpdF2qNC+quw9HyMAdey7TrvsyXr39wC/dq5g9+QSsK8DUy9r8c/T5nz2zLlqYRi6EiBCnd17g8rEbNB1Ui0snbplvEArl+QEtkekrbj7n+t4Xg4UPM0MCxj+B6R1rAC9eetGn/1pqVM6FSzzzrw8R44MRv3j66uVCQiMgrq5mVreDfydcgXH1H4UdaDQy7eRpjty7j49b0Pc0VrToGDX9C44RqDLwm1LqBHAfeAukAEoAGYEXQLuQF2ua9l4p1Y6gMDuilFoLeADVge+DP74uQr8CIYRD0zSNFWO2cP/aE/otaM/0nX0Y3W4Rty8/0t2Xo2QhQFLNjfhxYjCmRzX2nLjO5GWH+PAx9OKLkAmXniwMSw5ak4EQlIPWZKCDk6wTQjiEWV0W8OruM1oMrUPWn79lXLfl3PjzgcXtHSEHjUaN+UuPcfqPuwzsV4XZQxuwauc5Fmw8RUCgIdR21s4HI3ou6MQZKFknhHBoZ3dfon3uAfRd3JmOI2vj/8mf3atP6+pDTw6GLLxZkoN65oIGg5H+QzcyZEA1xvepyfbDfzF1+WF8/UJfvNObgRE9D3Sy/JO8E0I4FT8ff0bXnUSDvtU4vuOi1f3ozcDwngc+euJB554ryPpdSt6++wjAN0nj8+LN+1DbOHr+OTC7ZZ2ceCKEk/B578v8Aat5e+sRLq4ujFzanrL18uvux1F2fIecfrJ5bdARk8XzZmbKgDp8k9T0LvaocvrJ5ecv+PvhW6J5uzC8aCm2121m/UBs7wCwCEgG1Ab6AXUICqaRwE+apl3/vIGmaVsJCrljwa/tBgQAvYGGmjUX2AshIr0TW8/Rs9QoAgMMVmVgCFteOQD6drx17bmSxStPULZQVlaObUGBHN+abecs1w44cuW/FSTrhBAOwWjUWDdxJ73KjMZo1JiwsTu5i3+vqw9HycG/rj+lTeel7Np3haolsxE3dgyzbeT0E5uSrBNCOLyPXj6MqjuJGT2WcmjzeQBcXPT9zHWEnd8PH7+lU4/lrFx/hqolsrF8bHMSxjO9wBeR80BrOFH+Sd4JIZyO0aixZvx23t19QrTorrQfXoukKRPq7kdPBtpiHhgYaOTqjae4eRkoXSAL6ye3pknVvLio0PPD0a/ecVB2yzo58UQIJxQr8BOx4sSgz+QmlK2bn1mDN/D4zktdfTjCTrd/X28gvtGVHFlSsXp8S+ZvPMmGPRcwGEP/ORYVTj8Jse/aHbIkSmp1e1vTNO0q0NWKdicJqrwUQgiL3b/6mK5FhuIXPSYA3+VIi+9HP57cfaWrn5BJlr2z0GDUWLbmFGfO3WNgvyp0bVyCc3+twGjm3/Jy+knEkqwTQjiaW+fv0bnAIOoNrMNfZ+4AECtODHxNnJz1uYjIQUvmg76+/kycsZcFy47hqfxxUYoapbOz48hVAg2hP1yMyNNPokoGStYJIZyFpmnsXHgIgJipkjN1e28ObT7HloVHMARavjBl79NPAgONLFh2jLPn71Gi7A+8+2B+cS+i5oGR+fQvyTshhLPLkCY+lRoXokLDgiwdu5NdK05gNLGW9jW2PP3E0nXBK6fvc6LQXbo2LkGxPJn5Ze4enrx8F+rrI+L0k7DknyOxZ9bJiSdCOKEPnh/pV3Y0U7suJuOPqZi1150W/aviFtPya2vAcU4/Adh3+DotOi7iz+uP6NG0JAtHNyFL+uQm20SV00+O3X9A/RVrrf/DhRAikvng4Y3/yzcYPTzpOLIOs/f1p1nfyrpzEBxn1/etOy9o33Upg4dvwqhpxInlRpmC5newy+knQggRdfl8+MSyQavwe/GGWHFiMOfAADqNqkN0N1eL+7BlDrp5BVicg17vfXHzMlA4U1rc25Rj6ZimZM2QwmSbiNz5JhkohBCOKfonH549eE2bwTWYtrMPWXKm09XeEU4/uXLtCTOm7cfNy0CaFAlZMqYpP2VOabKNM5x+IoQQwjZu/XmP9nkHcv38fTr/UpeJm3uQPqvp3PgaW60NWjoPfP/hEyNGbmXMxJ1kTJOE5b81p3Lxn0y2kdNPHJ8UngjhpDRNY/eSI7TJ5c7hdacoViUX1p7qZ83DRkvpedj46vUHBg/dxLBft5I0YRzSp05sUTtrJ1vy0FEIIZzfyHqTOLb5LI17VGDO/v66rxsA/Q8bwTZZ6B9g4MWr97h5GahdNhe/dK/KZPfafJPU9A4EZ7h2AOThoxBC2FLgWw9ObT9H9VbFmbi5J8nTWDaXAsfJQYA//rxP/xEbiRcnJgtHN6ZTw2K4RTddSCMZKIQQUdcHD29G1Z3EyIbTSJAkLpO39aLD8FpEj6HvoHd7L76FSKrcSBA3FvNGNKSzmQx0hgJMIYQQtvHy4RsGV/mNsa3mkPLbpPSa1NiqfhyhAHPf4eu06rSYy7ef8snPsjaSf45LCk+EcHJerz8wqeNCuhQcTIBfIDFiRqfbb/VJliqRrn4i4vQTS4Pm6MnbNG23kCO/XwOgcrEfKZwrg8k29jj9RKr+hRDC/t69es/4NvPoX2UsRqPGmFWdyVfqR6v6cqSTwDasPsP0uQfImTU1q8a3pEHF3CbvOwU5/UQIIaIyPx9/5vZbxYj6U0mVPikzd/clX2l9eegop4CdOXePlh0XsXv/VZpXz8+YHtXMtnGGxTfJQCGEsJ1TO/6k3c/9+X3RYb7LkY5Af4PuPmxdhGmJ67ee07rTYn7f9xfNqudn6Zhm/JDR/AlgetijAFMIIYRtHF5/mrY/92d8i1kAxIkfi+wFM+nux94FmK/fetN/4HpO7LsBQK2yOaleKrvJNnrngJJ/EUNf6a8QwmH5en8CPpGpyPeUrp2X0rXzsmrKHpvebwr67ncDfXd9A8R4b6BW2Zxk+y4VB07fYsryQ3h4+YTaLqLuOv28rbX3nQJ2ufNUBWq4eemfgAshhCO7dOQ6HfMOpEKzYvx5NGiSkiZTcp7df23Te05B/12nYP6+b6NRY9OOC5w4c4cePcrTs3kpUiSNx/SVR022i8gcdPR7vyXvhBBR0eldF+haZBhD1/akZpuSnDt0XVf7kAeO9s7Bjz7+TJi+h8PHb/LBNehneQy3oEdYfv6BobaLiHu/Q9pZe+93eBafSNYJIcS/fN77MrPXMqJFd0WLF58ESeLSakA1lo3fiedryxbU9OZgyMKbJTloaQb6+PozccZejp68Rb+elahc/Cdu3Htpso2188CInANaS7JOCCFM++DhzQePoB/Qdd2r0LBbefauPcPCMdvwfhf6WtqXrMlAPfNAS9YFg15roMjPGSnyc0ZKFcjC2AX7ePk29By3Zg7oiPkXWfJOTjwRIpK5evIW7XIP4MLxW7QZXINZe9zJVkBfhaMjnX6iadCj72oWLj9OsTyZWDuxFTVL58DMpm+nun5HCCFE+AjwC2DnwkMEvvEgTvxYTNzUgynbe5MpWxrdfTnK6ScvX79n0JCNjBy3nQ17LwKQMF4sYkQ3XT8e0aeAWUN2fgshhG08v/+KniWG82uTaQAkTBqPBEn0PSFzlBw8f/EBt84/xs3LQIf6RVgxtjm5sqY22cYZTj8RQghhO4EBBowenmT9Phmla+Vl3qFBlG9QUFcfjnD6ybkLD2jVcREL5x0B4PsMycmaQU4/EUIIEbrVI9ezbtJOytbNx/xDgyhR/Wfdfdj79BOAwUM3MmX2PnJkScWqcS2oVlJOP3EWUngiRCT06vFbRtaeyLC6k4kZ243mfStb1Y+jHLUcGGhkxbrTtO6yhFsPXtG/bTm+T296ogX2uX7HGrLwJoQQ4e/Dg2fM6r2MpCkTMm1nHzqMqE3suDF092PrB46WZuGhYzd5e8cTNy8DQzpWZMW45uT+Ma3JNtYuvEkRphBCOD//TwF88PDG6OFJ70mNmbmnHz/mNX196dfYck6oJwcBzh6/g4uLYs6whvRuUZpYMUzvmJPFNyGEiNpO77xAxwKDeHDzGb0mNmLc+q6kzpDM4va2LMK0NAM/+vjj+ykANy8DXRoVZ8GoxnRsUJTo0VxDbSMFmEIIEXX5+fqzeNh6uhYdxqunHgyY1ZLGPSvq7seaDLSUJfmnabB11yVad1nCzfsvGdC2HJnSJjXbTvLP/qTwRIhI7OzuS7T7uT9jm80AIFHy+FRsVAhl7riQz+gNGLDdotuTZ570cV9L574ruXk/6IjJwrky2GzXt9z5LYQQzu3oxrO0zeXOrgUHqd6qGDP3uJMgcRzd/TjSSWAAmzaeQynFrCH1GdiuPPHimC6okSJMIYSI2hYNXIWfbwDjN3SjVruSuttHxJzQEhevPKJ1xyVs3HaeOuVysXJcC37M9I3JNrL4JoQQUduTv1/Qr+xopnReRIYfUlu9+KaHLTIQYMSorew/dI0WNQqw9NemFmWgHlKAKYQQkce9vx7Ts9gwZvddwYGNfwAQN0EsXFz0PXuzdwHm8xde9Om/lu79V3P38RsAyT8HJ4UnQkRyfr7+vHnmidHDkwoNC9JjfEMmb+1Jpp9MH0/8JUc5/QTg2o1nuHkZSBcjDuP71mTl+Bbky/atyTbOcvoJyM5vIYQITx+9fJjVZwV9yo3h1J4reHl8tLovW1b7g+VZ+Oelh7TutJjVG89SufhPrJnQymYLb7L4JoQQzu/+1cd0LTyEM/uv0n5YLQbPax1hp4CF94PHT34BzJh/iO79V/PR148PH/0s6l8ePgohRNSlaRp7lh2lXe7+zB+1BYA0mZKTNXd6i/twhAz0/ujH2Km7cR+2gTixYjB/ZCN+/sH0tbJSgCmEEFGX0aixbc5+Xly5C4D7jOaM29CNpN8k0NWPI5x+8tf1p7h5GciSPjkLRjZmsnttkieOF2qbiMo/8V9SeCJEFLJ6xDrGtZlLirRJmLarLx2G19L1wNGRTj8BePHqPX0Gr8doNDJ9UF2Gd65EovixTLaR00+EECJqun7mb+b3XorRw5M0mZIzZlVnUqRNbFVfjnD6iZ9fIPOWHKVjz+XcvPeCR889Leo/IideMmETQgjH4fPel1F1JzF/4GrSf/8NLq7WPQ5ylNNP/rr+lLadl/LyVtCut57NS1EwR3qTbWTxTQghojbPl154/v0Yo4cnTftUYtKWHnT+pa7Fz0YdJQPP/nmfVh0XsXjlCa7cegpA7FhuJttIAaYQQkRtRg9PDq08Rqaf0jBzjzt5SmbV3Ye9Tz8BeHjlOdPnHSBX1jSsGt+CaiWzmXx9ROWf+JcUnggRxRxae4q2ufqxa+EhqrcuTgv3qrr7iIjTT/Qct9ym0xKWrj5JmYLfs+zXZrhFD/2eU5DTT4QQIqpLnigG3+dKx+y9/SlVK69VfUTE9TuW+PveKwYN3YT/cx9cXV2YPbQBdcrlwtytenL6iRBCRE2bpu+hY96BeHv5Et3NlUIVslvVjyPs/A6R2BCN/Nm+ZcqAOgzpUIH4cWKafL0svgkhhJjcZg7b5uynSrMizDs0SFceOkIGfvTxZ+W6M7h6BpIgXizWTWxFj6YlieEW+nXkUoAphBBR26G1p+haZCger97zy4pOtHCvontDgiOcfrJl50Vad1nC7QevGNS+Ar/1rG6yjZx+ErGk8ESIKMj7nQ+zei+nR4mRrJm+D4CU6ZOSRMcRW9ZW+dti0c0/wMCSVSdp03UJM1cfwz/AAEC6lIlMtnOWawfk9BMhhAhf5/ddoVOBwdy78RT36c0YvaIjqTMmt6ovRzoJLHFgNPwDAunbqgwLRzXmh4wpTL5eTj8RQoioKcA/EKOHJ5WbFmXYwraMWNKelN8m0d2Po+z89vb2o33XpaxYd5oKRX9kzcSWVCz6g8k2ETUXlMU3IYRwTJ8++jHXfRU9S47ivedHhi1sS5k6+Sxu7ygZCMAbP46fvE3DynlYPb6lRSeA6SFZJoQQkceTv1/Qo9gwdi89Qtm6+YkRM7pV/dj79JNnL97R230NU+fs5/y1R/+Oy8QymuRfxJDCEyGisNsX7uNx+xFGD0+6j23A3AMDKN+goK4+9E6ywHaLbg8fe3Bk9zXcvAwUzZ2RNRNa0b9tORLGixzX7wghhAg/Lx+9oV+ZUczpt5IfcqenfP0CVvflKKefeL33xX3gekaP30HyxPFYOKoJ/duUJVYM05NIZyjCFEIIEf62TdnO/IGryV4wE3MPDKRJr4q4WfHg0RF2fvsHGFi4/Djteyzj6SsvujctSdzY5q9OiKiTMCX/hBDCMd368x5dCw1hdt8VHN91CYDobqZPUv6cI2Sg76cAps45QPf+a/APCGTKgDqM6laF6NFC/zpk97cQQkRdfr7+TO2ymK5FhuL70Y9o0V1p4V6FeAlj6+rHUU4/2bHxTwDKFPyeeSMakSlt0lDbSP7ZnhSeCCEAmNZxPveuP6XXxEaMWdWZ5GkSW9w2ok4/0VPtf+3sQzZuO0/V4j+xbnJr6pb/GVeX0Msdnen6HSGEEOHDaNTYOnsfrXP2Y9XIdQBkL5iZEjVyW9WfrSdclubggaM3aNZuIRu2nueHjN/gHxBoto2znAImhBAi/BgNRjZN30ObnO6c3HOFpr0r0WtiI6v6ioid35bk4N37r+nWayVd+6zC28cPF6VoUDG3Ta4ekNNPhBAicjAEGtg2Zz+fnr0iZmw3Zu5xp1GP8riYeI74OUc5/eTy1ce07byURSuOo1AEBBrMtpHd30IIEXV5vvTC6OHJD1mTUbdjGRYcHUz5BgVR5u7v/oKeHLTVFaxuXgbUh0DSpEjI0jFN6VC/CDGim54D6iH5ZzkpPBFCAPDs3ivcy/3CjB5LyZr7W+YeGMAPedLr6sORrt/56OPPrIWHad11Kbfuv6RPy9JMdK9ttl1UWHhTBu2f0Db3JoQQUcG7V+/59NEv+NqBwgyY2YLxG7qRPmsqq/qz9eknlvx89vH1Z/aiw3TqsQKDUSNu7BhM7FszSl2/I3knhBCW8XjxjrFNpuFe6TfWztgPQIIkcUmR1vLNCCEcYT6oafDkmSduXgYKpk9Nz+alWDOhJUVzZzTZLqIyMDzzT7JOCCHCmZcXd68+oXnfKoxd35VkqUxf4/05W56EaenP8oBAA8vXnmb0L9sASJMiITMH1yNzumShtonIZ6HWkKwTQgjbunz0Bp0LD+HxnZf0mtiIiZt7kOEH/c9E7Xn6CcCxU7dp0WER+49cp2XNgqwY15xcWVOH+npHO/0ksuSdFJ4IIf6haRo7Fx6iQ96BHN5ynrvXngLg4qrvR4VjXb/zlr791zFkzBa2Hrgc9Ge5upAscVyT7eT6HSGEiJrGNZ3O1K6LSZflG2bu6UfHkbWJE9/0lW1f4yjX7xgMRty8DGSMG58sGVL8c/1O/LgxTbaL7EWYQggh/uvysRvcP30do4cnLftXZd6hgTTuUYHoMULfKfY1tt6QoGc+eP7SQ7q5r8bXL4AJfWsxtncNUiSJZ7KNIy++CSGEsC2fD58Y23Q649rMJdOPaZi9z53i1X7W1YejZKCbl4G0seOSMW1SloxpSpdGxYlpItMdbQFOCCFExHl4/Sl9So1kQvv5pEqflO7jGljVj71PP/F678vYKbvpNWgtAInim78+SG/+yfzPNCk8EUL8x6vHb5nWYd4/R0zO3utOnQ6ldRWgONr1O8dP/c3pg7dw8zJQp1xO1k1sTYsa+cP9vlNw3J3fQgghzDMaNXYvOUKbHP3YtfAQVVsUo2jlnFb35yjX79z8+wXNg6/fqVoyO+sntaZG6ewm20T0KWBCCCEcx4phazmz/yrN+lZmzv4B/Jg3g+4+HOX6nSvXntC281LmLj5C/mzf8luv6mbbyOKbEEJEbYfWnqJTwcE8vvOS6i2L2/TagRC22Ijw56WHNGu3kD37/6JptXysHt+SQrlMZ7oUYAohRNR1YNUJ2uZyZ1yLWQDESxibYlVz6e7HlieAWeLC5Ue06rCYk/tvAlC7bE4qFPnBZBuZ/4UPKTwRQpgUzfcjTx+8pu2QGkze2pNvv0+pq70jXb8T4szh25y98oCODYqxenwLClsw4ZKd30IIEbV88PzIrN7L6VRwMPvXnwWgYPlsZMmVzqr+HOn6nbbdlnL38RuK5clsUf8RVYQp+SeEEI7jzTNPfm04lQFVx+HiopiwqTtl6uSzqi9HmA8aDEbWbPqDFh0XMXZB0HVCsWNGJ8f3oR+9DLL7TQghorIXD17Tp/QoRtSdiKZpJEgSV/d80BFOP/ng/YkJM/bSzX01n/wCKZAjvdk2UoAphBBR1wfPjzy7+xKjhydVmhdl0JxWjFnVmdQZQr+27WtstRlPz/VzQa83UKpAFkZ0qczUAXVIlTxBqG2s3Ywu/iWFJ0IIk96/9WZk7Yn82nwW36RNwozf+9KoR/kIu37HFkdNvnj1nuEjt9Bn8DoCDUYmudemW5MSZttF9PU7zkIpVVcpNUMpdVwp9V4ppSmlVoby2vTBnw/tbW1Ej18IIUx5eP0pgW880Dzf0bxfFabt6EOP8Q1JkDiO7r4c5fqd+w/f0KvfGn4Zsx2AVMkT0LdVGZPX7zj6vd8RQfJOCBEVXTx8jY75BrJl5l7+PBq0W0zvXBAc5/STl6/f8+DKc9y8DDSsnJd5wxsypEMFEsYzfa1eVMlAyTohhPh/hkAD7996Y/TwpIV7FSZv6UnDbuVwcbH8BJSIuILOEleuPaFtlyUsmn8EgBzfp6ZOuVy4mDjNJTLmn2SdEEJYbu2o9czouYwsOdMyZ/8AmvWtjFvM6Lr6sOXpJ5ZmYL8B65g6Zz/ZMqdk1bgWNK2WD1cT89rIUHxir7zTd1GvECLKOrrpLJeOXqfz9Nb8XPR71k7fr7uPkIDREpm+U/tLru99McQ3/SAwREjQ+CcwH37nLz2kTecl1K6Wh7+evgSCdr0ZNY1PfoFfbRMSOH7x9D1sje4NAXF1Nflnoqa3nR0MAXIC3sATIKsFbS4DW7/y8avhNywhhAg/mqbRu+QImgysSc3OFShSKScrJv7OrpUnMRr0TUb05qGtctDH1x83X8ibLxU1SuegbMHvmbPuODsOX8WoaV9tY00OWpOBDkryTggRJX366Mf8gWsAiJY0Mb+t6cyNPx+waspuAvwNuvqyJgMBXTloSQYCbFx9llhGFxrWzkexPJmYvfY42w//RSgRGFUyULJOCCFCsaDvcmLHjUkL96rkLp6V8d1X8Ob5O4vbK88PNnsmauk8MDDQSGCgETc/KF8oK3XK56JSsR8Zt2g/fz98/dU21uafA5OsE0IICxmNGjsXHOTEtnO0+7URjXtUIEHiOMwctEFXP3ozUO+zUHP5ZzRqbNl5kROn/6Zbt3J0alCMc1cfcuv+q1DbWLsW6EDskndSeCKEsJjXmw/81ngaMWK5ocWKQ6Lk8ancpDDrZx8gIJRCja+xpgDFmgeOYNmEa/2Wc0FtgPbNS1Ey33fMWHmEg2dvh9ouLJMuawtQHFgvgoLrDlACOGxBm0uapo2w5aCEECK8+Xz4xIJBa9m7/Bidp7ai8y91eXj7OVdO37GqPz2TLlvlIMCeA1e5/fcLuncrx4C25aleKjsTlxzkxr2XobaJ8cEYIRnoYCTvhBBRnov3B57dfkaDruUoUC4bk3qt5M5fT3T3Y82DR7AsBy3NwE9+ASxYdox9h67Rs3t5BrQtT/pUSZi28ojJdpE8AyXrhBAiFB+9fPi10VT+aFKULpObM3tff0a2WcC1P+5Z3Ietn4nqmQdOn7aPa5ce07VDaRb/0pT1uy+wcNMpfP2+vns8EizAhZCsE0IInd69es+EtvPYu+woLz39AUieJjGaUeP1M0+L+rDlJgRL8+/1W2+GjdhCxvTJuOXpAUDpAlk4c/k+Pp9Czz8nzT675J1TfqeEEPbl5+uP0cOTQkUz0bR3JWbu7kfW3Ol192Pr63fA8uMmQxw7cAOvD7780qMaMwbXI2OaJCZfH5HX7zgqTdMOa5r2t6aFtjdQCCEil0c3nzGg4hh6lR71T9FJ7hJZiRHLTXdfjnL9zr2Hb+jZdw2jJ+wkeeJ4VCmezWybqHb9juSdEEKA/6cApnRexJDaE4mbIBZTt/emae9KRIvuqrsvR7l+5+Hjt/Tqt4ZfJ+1iy8HLACSKH4vYJo6QtjYDHZ1knRBCmHdg1Qm6FBrCX2fv8Pjv0Iv1TYmIDLTEwaM3aNZ+Ibv3/0XjqnmpWOxHs22c/foByTohhLDeleM3eXn1HkYPTzqNqs38wwOp06GU7ivo9LDF9XP3HrzGzctAuhhxGNm1CqsntKJYnkyhvt6a+Z+92SvvpPBECGG1nQsPMajGBGLEcmPSlh50/bUeCRLH0dWHNRMtsM0DR4CrN57SodsyJs3cx3fpkrHst+ZUKfGTyTbWho4zPHi0oVRKqQ5KqUHB73PYe0BCCKHX9bN3MHp4kiC6kRGL2jHv0ECKV/vZqr6sKUCxlJ4cPHDkOs3aLWTRgiMA5MiSirrlfzZ772lkXHwLJ5J3QohI6dzeK3TIM4DD609TqlYeqwpPQjhKEebeQ9d4cfMNbl4G+rYqy9qJrahU7EeUiWeoejPQmQswTZCsE0JEOc/uvWJk7Ym8u/sEF1cX3Kc3I2/JH3T3Y+vFN0sy0Nvbj4kz9tKh13K2H7oCQN6f0pEqeYJQ2zjjAlwYSdYJIcQXZnVbzKWTt2k7pCaTtvYkU7Y0Fre1ZQGmnuegL169p1u/VXz4+InxfWoytneNqJ5/Yc47KTwRQoTJnwf+okOeAWybu5+KjQrRtE9lq/qxNmhssfBmNGps332Jpu0WsGP3JS7fegpA4gSxieEW+g1lUW3ndxiVA+YCY4LfX1ZKHVZKpbPvsIQQQj/Pl14MqDqWj+99GTi7JRM39yBLLut+nNl64c2SHPTx9eejjz9uXgZKF/yePi1Ls2pcC5OV/6B/51sUyUDJOyFEpOX9zocJ7ebTrchQPvn4EyNmdLr+Wo/kaRJb1Z8jLL6FWL/uLK89vRnWqRILRzUhR5ZUJl8fyR8+miNZJ4SI0pLFdeW7HGkZvaIjIxa3I2X6pLra2/qZqKUZePP2C1w9A4n5wUj/NmVZPb4lHeoXIVYM0yeARRGSdUII8YVXj98yvOYEfms5mxRpkjB9Vx8KV9RXp2DLTQiW5t/1W89p33UZc5ccIV+2dCwa3cTkOiBE6vwLc95J4YkQIsx8vT8xt98qOhYYzKopewDI9FNq8pcxfVLI1zhSAcr7D5+YOucAr269BaB/m3JsmNyaqiWy4WJi25sUoJjkA4wG8gCJgt9C7pcrCRxUSuk7NkcIIRzA1VO36VJgEFO7LCZV+mRM3NSDhEktv7f7cxGx89vSxbfZMw4wYMQmNE1jfJ+azBxcjyzpk4f6esnAf0jeCSGiDO93Phg9PMmUIRFl6uZnweFBNO9XmZixbX8FHdjmFLAbt57TuccKxkzcSbJEcZg3opHNTsJ0YpJ1QggBPL//ig55BrBg0BqyF8rMvAMDadm/Km4mrmz7Gke5fsdo1OjhvoajJ2/RsmZB1k5sRfnCWUN9fSTPP8k6IYQw48iGM7TJ2Y/1k3dx8fgtABIl0/dM1N75ZzAYWbPxD5q3X8iYeXvx8w8EIM+PaUNtE8myL9zyTgpPhBDh5tHNZ3jcfoTRw5OabUsycml7Ri3vQJpMoS9Qhcba63dsMeEKeq2BDev/4OXbDwzuUIHlvzWjYM70JttYe/2OrRfflNH4z/fK3BuQVCl1/rO39mH5szVNe6Vp2jBN0y5omvYu+O0YUB44C2QG2ob9qxRCiIhnNGrsXnqE1tn7MLrRdN69CcqykjXzRMjim61y8PS5u7TutIQps/eRMW1ScpuYdIVwhOt3JO+EECJiXT15i7Y53Tmx7RyNuldgwdHBlKqV16q+HKEIU9Ng3+GgK+gWrzzBiQv3AEiVPAExY4TvSZjWkqwTQgjHEBhgYOO03bTJ2Y8jG89QqHx2jAbrssARTj9589abMRN30aXvKjze+zCyaxV+zmr6CgVb5Z9knRBCOL6PXj4sGb6Bj49fEC26K2PXdmXCpu6kz2r65MjPOcLpJ6/fevPH4du4eRkonCsDM4fUZ8agumRIncTicVkrsuSdFJ4IIWxiSts5zBuwmh/zZGDO/gG0HVqT2PFi6urDmkp/sN2Ry1euPaFLz5UM/20bMdyiMaV/HWqVzWmyjbWTLgfa+f1G07S8n73Nt8UfomlaILAw+LfFbfFnCCFERPH58Imzey5h9PAk3Xcp6D+jOQuPDaFsvfwoEydmhcaaiZelLM1Bg8HI1l2XaNJmPjs2/AlA2ULf07ZOYbNHLzvJDgDJOyGECAevn3owrvVcepUehcfL91RqUihM/TlCEabvpwCWrTmF75OgSdroblVZP6k1FYv+gKlYd8AMlKwTQggb83jhxcT28+laZAiBAQZixYlBr4mNSJ46ka5+HOX6nas3ntKp+3L6j9jIxZtPACiaOyMJ48UKtY2d80+yTggh7Czg1Vs2TtlJ2swpmLm7L+2H1yJ23BgWt3eEAhSAi8fvMmX2PrKkT87ysc3p0bQkcWLp31hoIw6bd1J4IoSwicAAA5tn7KFVjr7sX3mcWm1LUKVpEav6CksBiqX0BM6RE7do0WERU+fs58DpmwB8920yvjFxpYJcPWCR18Hv5YhKIUSk8eDsTXqVHsXrZ570mdyEaTv7kK1AJqv6svfOb4CPPv4EBBpw8zLwU6aUtKlTiHWTW1O5+E9mF9/EPyTvhBCR3vWzd+heZCij6k0GIFHy+HQf14DEKeJb1Z8jLL4FvdbArDkHee35keGdK7NgZGOyfZfSZJsomoGSdUKIKM3Pxz/oGrr0CSlW9WfmHRpIrXYlcXHVtxzjCAUoRqPGmXP3cPMykCjAlRFdqrBucmvqVfgZV51fTyQjWSeEEF+haRp7lx+jTY5+7Fl6lBqtizP/yGBSZ0imqx9b5585BqPG1l2XaNpuIbv2XqF+xdzMHFJf15giCV15F6X/ZSCEsD2v1x+Y2nUxXYsOZ9uSYwDkLPwdP+bNoLsvW0+2wPKHjoGBRrbsvIjfMx/cvAz0a1WWtRNb06VRceLFCb16UwpQTCoY/P6eXUchhBDh7PrZO/QsNpyxreeQMGlchi1sY9XVO+AYVw+EmDPrIJ37rOTlm/cM7ViRxb80Nbn45oA7v+1F8k4IESVomsb7t94YPTz54ftklK2Tn4VHh9Cgazmim7iqJjTWzgctpWf3d+cey/l10i6SJ4nHgpGNKfJzRpNtomAGStYJIQRw5fhN2ucZwOWTf9N+WC2mbu9N5uymr6z5mojYkGeJjz7+dOq1ght3X9C7RWmW/9aMfNnS6R5bJCFZJ4QQJnzw/Mj0HkvpUWIkfx65wfOHbwCIETP0k5O/ZMs5oKXzP6/3vkyetY/2PZcze03QGmf0aK58nyG5rnE5MV15J4UnQogIcffyQz49e4XRw5MmvSsyaUtP3Kc3I+k3CXT35UgFKCFGjtnGoWM3aFwlLxuntKFhpdxEM1H1HwUfPAKglMqtlPrPN0YpVQboFfzblRE7KiGEsD1N0zi87jRtcvRjcPUJfPLxRylFrXYldV9FB45x9QDAtZvP6NprJaPH7yBhvFgkSWC++D0qZKDknRBC/L8T287TNnd//jx6k5b9qzLv4EAKls9mVV+OUISpabD30DWatV3AnEVHOHvlAQCZ0yUjpomimsiUgZJ1QghhmVeP3zKs5nhGN5lB4hTxaeFe1ap+HOH0E4CHj9/iPnA9g0Zvxi16NKYMqEPKZNadaOboJOuEECLsbl+4z6TWswl840HchLFZdGIorQdV17UpzxGu3/n77ksun7yPm5eB2mVzsnRMM4vH4+jCM+/0bzGxA6VURWAa4Aos1DRt7BefLw5MBXIADTVN2xj88VzAHCA+YADGaJq2LuJGLoT4msFVx9KgTzXq9axMoQrZWTdzP5vmHybAL1BXP8rzA1qi0K+3+ZqQsDHED/0u0s+5eQXgn8B8BebrNx8YO2U3G7aep337UvRoVoqPvv7sOHLVZLsYH4z4xXPuGkClVE2gZvBvvwl+X0gptTT41280Tesb/OvJwHdKqVPAk+CP5QBKB/96qKZpp2w6YAclWSdE1ODn68+tP4MKxH8qlIW2Q2rQoEs5Vkz6nd2rT2M06FuMCpl0WZqHenIwZNJlLgc1DQ4cvcGxU7fxDzBAAleaV89PgrixWLL1DN4+fl9tF7Lw5iw5KHkXdpJ1QkRtLx68ZnS9SeQs8QOdp7SkSKWcnNlner5kii0zECybC/p+CmDt5j9wAaIljs7EfrVQwKy1x9h/6iaaZtEf5TAk68KH5J0Q4mtObD3HhUNXiRUnaONB0pQJyfRTas4euKarH735B7aZB548c4dzf94nV450PH/9HoDSBbJw6tI9Pul8xhuRJOvCh2SdEMIamuc7zu25RL1OZShZIzfzR27hxO+XLW6vd03QFvkHsGf7ZZLFsmxeaS/2yjuHLzxRSrkCs4ByBH2x55RS2zVNu/7Zyx4BLYG+XzT3AZprmva3UioV8KdSaq+mae9sP3IhRGj8fPxZPnoTe5cfpf3EFrRwr8rT+685vvOS7r6smWyB7QLn7v3X9B+4ntw503Hu8XMAivyckXcffLl25/lX2zjbwttX5AJafPGxjMFvAA/59+fzCqAWkA+oBEQHXgLrgZmaph239WAdkWSdEFHTtdO36VZsOB0ntaDrr/Wp2qIYC0Zv5cLRm7r7smbxTc/CG5jPQf8AQ/DrDSSPHZtalXNTucRPLNx4iq0HL2Mwfn31zYmKMHMheWc1yTohRIjLR2/QOf8gYsR2g2gxyfhjaopWycm6Gfvx+2T5qZMhHOXho4tHAKPHbqdLpzKM7FKFeuV/Zsryw1y/+8LisTmAXEjWhYnknRDCFJ/3vvgE51Cd4bWo2bYkJ3ZdYs7wTXi8fK+rr4goQLFkDvjHn/dxA1KnSsToblV57enNrNXHOLPG4mFFtFxI1oWJZJ0QwlofPD8ypfMi9i47StcZrRk8rzV/Hr3B6LaLLJ4LWpt/4boBwdef+UuPWfzn20ku7JB3zvCENz9wR9O0e5qm+QNrgRqfv0DTtAeapl0BjF98/LamaX8H//oZ8ApIFjHDFkKY8/LhG0bXm0SPkiP/KTopUikn6bOm1N2XNcdNgm2O3AK4cPkRrh4BuHkZaFe3MAtHNWZUtyomj5501mOXNU0boWmaMvGW/rPXLtI0raqmaek1TYuraVoMTdPSaZrWIKpO1oJJ1gkRRd259JC+ZUYxqtF0YsSMTqdRdXAxcVWbOfa+eiDE9HkHad9jGXcfvaZvqzKsGNuCbN+Fnu/OkIGSd2EmWSeE+Ich0IDPe1+MHp7kLpCeRt0rMO/wIIpUymlVf7a8+xssz8C/rj+lU/fl/Dbld75JGp9Fo5vwY6ZvzLZzFJJ14ULyTghhkfl9lrF4+HrylfmR+YcGUaV5UZRSuvux9nmoJfTMAZ8+86Sb+2o83/swrFNF3WOKKJJ14UKyTggRJtfP3qFroSHM6rMCz1cf/ik60ZODjnD9jiOzV945Q+FJauDxZ79/EvwxXZRS+QE34G44jUsIEU5unruL0cMTvLxoO6QGM/e402l0HRIkjqO7r7AUoFhKb+j06LuapatPUix3JtZObEW3xiVIGC/06kpnWHwT4U6yTogo7uT287TL5c7QmhMwGozEjO1Gi/5VIyQLbVWA8ve9V/R2X8vg0ZtxcVH/nIjiYsXDVBEpSNYJIb5q/aSd9Cn3Cx/f+zJkfmvGrOpEuizWFWs4QgZqGuw5cJWm7RYyccbef048+TlrGmLHNH/A59lbAAAvDklEQVRyinB6kndCCIsYAg2sm7iTjvkGcevSI7qOqUf9LmWt6svaAszwXoC7eiOoALOb+2pdYxFOR7JOCBFmRoOR7XP3M6HlTIwenqRKn4x5hwZSuGIOXf04Qv6JfzlD4UmYKaVSEnRMTCtN0/6zmquUaq+UOq+UOu+vff0OeiGE7RkNRroWGcquhYeo0rQIi44PpX6XssSw4uGcrSdcYHno+H4KYMmqkzRpt4CDR27QsHIesmZIYbadFKAIPcxlXfBrJO+EcGAB/oE8u/sSo4cn2bOnpF6nMiw+MYyG3csTI5ab7v4iYvHNEifO3KFF+4U8uBx05dzA9uUZ06Mq6VMntvjPEgIk64SIzK6euk2XgoOZ1WcFWXKmo2jlXGHqzxEy0NfXnx17LuPmZSBenBhMdK/FxqltqV/xZ4v/HBE1yXNMIaKWZ/deMbDSGMa2msOulScBSJflG5J+k0B3X45QgGI0aly/9fXrxoUIIXM7IcSXYhj8CAw0MHRBG35b20XXzQgRkX/CMs5QePIUSPvZ79MEf8wiSqn4wC5gsKZpZ772Gk3T5muallfTtLxuKkaYBiuECJsPHt7M6r2cDvkHc+XMHVoNqEaGH1JZ3Z8jFaC8eevN2Km7adJuAWeuPACgZc0CtKtbmLixQ//ZI8UnUYLNsw4k74RwJmd3X6JDvkFcOnmbFv2qsPj4ECo1KWz10cv2PnpS00Jeb+DV43cUyJ6eVeNaMrxzJdJ8k9DisQmnJlknhDApZMdby2x92TB2EwAFyv5Ek14ViRlbfwEmOM7uN79nPvQasJZbD16R5dvkusYknI48xxRCWOXw+tO8v/cUo4cn3X6rz4JjQ2jSq2KEbEIAWYATusjcTggR7u5efkjn/IOY2WsZGX9Mzcw97nQYUVtXH7a8glVOP7FMNHsPwALngO+UUhkICq+GQGNLGiql3IAtwHJN0zbabohCiPD2+NYzRtSaQLqsqXjyKugHf71OZXh4+wV/HLymuz/l+QEtUTxdbUICxxA/9GtxPhcSOv4JTJ/Q8uzFO0KmjGlSJKRKiWzUq/Aza37/k3V7LuDj6/+fNuFafBJotOo6ImFTknVCiP94fOsZI+tM5McCmWk7tinFq/7M7lWnrO4v5Ge/pXmoJwctzUCAZWtOsXXXRRrWyU/tarkpWygrv8zdw96TNywal8Uk7xyNZJ0QwiIfPLyDfuHjT7YCmajbsQyVmxRmxaTd7Ft/FqNB39xIb/6BbTLw5t8v6D9wPdGjuVo8DrMk6xyR5J0QIszGt5xFm7FNadq7EpWaFGbpuJ0c3HgOLaSi30K2fB6qZw4YJpJ1jkiyTghhE0aDkR3zD3JkwxmaDa6NweXfuZNSyuIcdJRnoLpEkrxz+BNPNE0LBLoCe4EbwHpN064ppUYppaoDKKXyKaWeAPWAeUqpkFXp+kBxoKVS6lLwW66I/yqEENZ6dPMZRg9PXD96U65+AUYubc+Ubb3IVTSL7r6sqXYE252AAjB+/O+07rqEC9cf075eEbZMa0uJvJl1j1E4N8k6IYQp18/eoXepEYysNwmApCkTMnZdV7IVyGRVf9acgGIpSzPQ670v85YcpVGb+WzecYEL1x+bbSOcm2SdEMIaC/oso2epUTx/9JYe4xsye687uYronwuCY1w/ABAQaNA1BuFcJO+EEOHh5cM3/NpoKr1Kj+L1U0/6TG5C2Xr5reorLM9DLSE7wKMeyTohhK198PzI7L4rmN97KQDZC2Zm1j53chb+Tlc/cvpXxHOGE0/QNO134PcvPjbss1+fI+g4ry/brQRW2nyAQgibC/ALoEOeAZRvWowmg2vx25ouXDn9NzMHbeDxnZe6+rJmxxvY7gSUu/dfM2zEFrJkTkGLFkV58vIdAInix8bXz59PfoG6ximck2SdEMIcn/e+gC/JsyYjdYZkTNjYnbMHrrFk7A4eWnGHtp48tFUGenh+ZNaCQ0FtLOpZODPJOiGENW78cYfeJUdQpHpe2vzaiCTfJAhTf9aegBLeGSgiL8k7IUR4uX72Dj2KDaNojbycO/sAgJyFv+PtSy+e3H2lqy9HOQFMRA6SdUKIiGL08ET5fCRW7BiMXdeVk7svs2D0Vl4+9rCova3zT/w/hz/xRAghQhgCDexeeoRW2foyq88KUqRNgl/wtTTR3fQfV+xoJ6DcvvOSwUM38fhqUCFNz2Yl2TS1LQ0r5SZGdKeoExRCCBEBrp66TevsfVk0dB0/5cvA7H3u9BjfEKWUVf3pyUNbngImhBBCmHJy+3na5XLnwML9ANRsU4J+05qRPHUiq/qz5gQwyUAhhBD2cGLbefxevMHo4UnHUXWYe2AAnX+pS4IkcXX35SgngAkhhBCWunTkOm1zubN0xAbylMjK/EODqNepjK4+bJ1/IogUngghnE6AXwDb5+6n5Q+9ePHXPQCGL27H0IVtSJ81pe7+HK0AJei1BrZu+ZM7j97Qo1kpNk5to3t8QgghIi8/X3/WT95Fy2x92TxjD4EBhn/uOY0R07odZrL4JoQQwtEZAoPyzujhSXQtkCKVc7DgyGBaD6xGHCt3o0kBihBCCGfiXuEXdi08ROUmhVl8fCj1u5TFzYo5oCzACSGEcCYBfgGsmbCDNjndObHtHH6fguZYejfiSf7ZlhSeCCGcltEYtMCmeb7j6tHr5Cz0HbP2utN/ZnNSZ0yuu7+ILECxxLUbz+g3YB3d3Fdz8uI93eMSQggR+X3w8GbBoLXM6DQfo4cnWXKmY8W5kdTpUDpCHj7K4psQQgh7WTdxJ21y9ufY5rPU6ViaRceHUKxqLqv7kwwUQgjhDLxef2BWnxV0yDeIy6f/ptWAauQu/r3V/ckCnBBCCGfy5pkn41rPZeukbQCUrZefiZt78F2OtLr6sTb/hGlSeCKEcHqaprF63DZa/NibdRN3UqBsNuYdGkihCtmt6i8iClD0PHS8cu0Jkyft0T0eIYQQUc/Hp6+48ecD2g6pwYIjgylbLz8uLvqv4JHFNyGEEM7g9ZO3TGg3n65Fh3P36hPevQnKrhix3HBxte6RV0RkoBBCCBFWj28/Z0StCXQrNpwz+64CUK5+AXIUymxVfxGxGU8IIYQIT0YPTz699iRV+mRM3dGbnhMakTBpPF192Hr+F9VI4YkQItL44PmRpSM30uKn3myYsovLJ28D8F2OtCT9JoHu/hytAEUIIYQw5/Ht5wytPo5+FX/F45UXfSY3YcKmHlb3JwUoQgghnMHdyw8ZWOlXLv9+HoBWA6oyZ19/ClfMYXWfsvtbCCGEM7h94T5GD09494467Usxbn03RixuR9rMKXT3FVGnQQshhBDh5eims7TOEXQVeZk6+Vh0bAhl6+XX3Y+cfhI+pPBECBHpeL3+wJLhG/B+9AKjhyc9xjVk0fGhtB9eS3e1I0gBihBCCOdz5fhNehQdxugmMziw8Y9/Pv5T/oxW9ScFKEIIIZyF0cOTi3svolwUQxe0Ycr2XmHa/S0FKEIIIZyB0ajRtfAQFg1dR7YCmZizvz9df61HgiRxdfclBShCCCGcic97XxYMWkuHfAO5+sddPnh+BNB9Cqa1+Sf+JYUnQohIb0TdSRxce5LqLYux+MRQmverTOx4MXX3IwUoQgghnM2JrefYNWMXRg9PCpT9iYmbejB+YzeHLUARQgghwsPpnRdon7s/kzotJEmKBIxb3416ncta3Z8UoAghhHAG/p8CWD95F62y92XH/IOUb1CQZKkSWt1fWJ6FCiGEEBHtyd8vGFp9HKfXnwCgSc8K/Lq6Mxl/TK2rHylAsZ4UngghIr1Xj98ytcti2uUZyB8Hr9Goe4UwH7ksBShCCCGczfmtZ5jZaxmp0ydj4qYe/LKyI1lyprOqLylAEUII4eiMBiP7lh+jVba+zBuwmpO/XwYg5bdJSJ0xuVV9SgGKEEIIZ+D15gNz+q2k6fc9ufPXEwDaDqlBleZFcY2mf0lIFuCEEEI4G6OHJ6/vPidTtjTM2N2XPlOa8E26JLr6kPzTL5q9ByCEEBHl6Z0X/NpoKmuzp+XRCx8AytUvQKw4Mdi79gx+vv66+gsJHC2Rvut7Qh46GuLHMvvakOIT/wTRdf0ZJhkCg+5+FUIIEaUE+AeyY/5B9q04TrX2ZajftxoDZ7ekTfFfMBqMVvWpNwv1ZGCYSd4JIYQAAvwC2Dxjzz+/bzm7JUUq5WDfurOsnrqHNy+8dPdpzVzQJhkoWSeEEMKEd6/eA+AazZXM2dNSp0NparUtydJxOzmx65Lu/qx9FhomknVCCCGs9PviwxzddJaGfatSo3N5StbIw9wRm9m1/ISufiIk/yJJ3smJJ0KIKOfeX48JfP32n2sHOo2qw/KzI2jauxIJEsfR3V9EnYAihBBChAc/X382TttNix96M7LBFIwGI9FjRKPzL3VJlT6ZVX3KCShCCCGcxazui9gx/yBl6+Vn0fGhtBlcnXgJY1vVl7UnoAghhBARyRBowL3caIbVmUyAfyCD57Zi6o7epMvyjVX9yQ5wIYQQzuKjlw+Lhq6n5U992bXwEDcvPAAgcYr4JEqmr5BE8s88KTwRQkRpo+pOoleZ0Vz94y5NelVk2dkRVGle1Kq+IqIARQghhAgvvt6fuHflEUYPTzJ9m5By9fIz//BAek5oRPLUiazqUwpQhBBCOLp3r94zp99K2uRy5+ims9RuX4pa7UqGqU95ACmEEMIZnN1ziU55BzCxw3zixIvJx+C5WPQY1h2ML/knhBDCWXi8eMfsviv4+9hfGD08aeFelcUnh9F6UHXiJ9K3IV3yL3RSeCKEiPKun/mbkbUn0vbn/hxac4ond14CQRWPWXKm091fVC1AUUqlUUotVko9U0r5KaUeKKWmKqWsW70UQggRYa6f+ZsWP/Vh29z9lKqZh4XHhtBpdJ0IewDpLBkoWSeEEJHHy4dvmNh+Ph0LDGbz/MMA5CqShWotihEtuqtVfUaGB5CSdUIIEbkZjRr7V56gbU53Xl9/AMDo5R0ZtqgtmbOnsapPZ8w/yTshhIja1ozewImt56jToRRLTg2jWZ9KxImIq8EjkD2yTgpPhBAi2OPbz5nadTEXd/6B0cOTmq1LMG1nH35b24U8JbPq7i9k0mXN0cvOsPj2OaVUJuBPoBXwBzAFuAf0AE4rpZLYcXhCCCEs8O7Ve+b1X03rHP3Yt+IYGbKmIsAvECBKL8CFkKwTQojI6eH1p7y//wyjhydFKuek8y91WXBkMKVr58XFRVnVp7Pmn2SdEEJEHZqmBf3i3TsuH7xCtvyZmPF7P0YsaU+WXPo34oHz5J/knRBCiGf3XjGh7Tw65BvE+cM3aNyzIk17VbT3sMKNvbJOCk+EECIUq0auZ96A1aTOkIxfVnRi9r7+lKiR26q+okABymwgOdBd07SamqYN0DStNEFh9j0wxq6jE0IIYbHXTz2Y3n0p7uVGY/TwJEGSuCw7PZymvSsRO15Mq/p0lgeQZkjWCSFEJDej03wG1ZiAt5cP/aY1Y+Zed/KV+tHq/pww/yTrhBAiijEaNVaN3UbzrD1ZMnwDP+RJz7QdfShWNZfVfTpB9kneCSGEAODRzWeMaTiFToWGsH7OQQCy/vwttduXwi1mdDuPLkzsknVSeCKEEKHw9f7E5hl7aPljbya0m4dyUf/30NGa0ImMBSjBlZPlgQfArC8+PRz4CDRTSum7KE8IIYRdGY1BO+Ci+Xzk2rl7NOlVkaWnhlGvc1lixHKzqk8nXIADJOuEECIq+fPAX3QtNIQxzWYSLZoryVIHnUIcLbor0d0i7wlgknVCCBG1+Xz4xNqJO2ietRfzBqzmj4PXAchVNAs/5cto59GFH8k7IYQQX3PvyiPe3nyI0cOTQhVy0G5oTRafGErVFkWtngfaiz2zTgpPhBDCjMAAAwdWn6RD7v7M6LwAgIw/pmbluVG06F+VRMni6e7TGR486lAq+P0+TdOMn39C07QPwEkgNlAwogcmhBAi7F4/9eCXBlPoUmQo188/oPXAaiw+PsTq00/AKXNQsk4IIaIQTdM4tvkP2v/szu7ZuwGo0LAgi08Mo2bbkpG1AFOyTgghxD8b8XyfvsTo4UnjnhWYuLkHv63tQvaCme09vPAgeSeEEMKkRe7L6VthDM8evKbLL/VYcHSI1bch2Indsk4KT4QQQgdf708YPTzxe/WWyydvU79zGZadHkH3sQ1InTG57v5CHjw68MNHS3wf/P52KJ//O/h9lggYixBCCBu5c+khw2qMo1fpUWxfehyfD58AKFkzDwmSxLWqTyfKQMk6IYSIgoxGDUOgAaOHJ/fP3ebp/dd0GF6LZWeG07hHBeImjG3vIYYnyTohhBD/MbjKWOa6ryJd5hSM39CN8Ru6kTV3ensPKywk74QQQpj114lb9C09ioHVx+P5+j3JUycGQCmFi4uy8+jMslvWRQvvDoUQIip4fPs5o+tPJlWmFNTpVpHyzYqTs8h3tC0+Bk3TrOozZOFNS6T/BBU7SxD83iuUz4d8PKHthyKEEMLWrp+9w/WzdwBIkvVb+k5pwul9VxnTYbHVfTpB8YlknRBCRHFXT93GvdxofsifmYaDatGsb2USp0jAzEHr7T208CJZJ4QQ4j/8fP3ZMmsvuxYdomLLkjToV40UaRJx88IDew/NWpJ3QgghLHbh4FUuHLyKazRXiB+f4tV/5s3zd1z74569h2aK3bJOCk++8N7o4b3PZ8Ute4/DQkmBN/YehAVknOHLWcYJzjNW68f5FyxtPxHah+NonoT6me9D/YwO740ee/f5rEhq4ctjKqXOf/b7+ZqmzQ+PcQj7cqK8i/w/RyKWs4wTnGesUXucF2BN9F/Cs8dwyTqQvBOSdTbiLGOVcYYvGedn9h2BaUeGh7UbmduJcOFEWQfysyS8yTjDn7OM1T7j9IGdkxfTdbLFLSTrRLhxoryTnyPhy1nGCc4zVhlnWLyHPbP+7yPyHPMLUnjyX7c0Tctr70FYQil13hnGKuMMX84yTnCesTrTOMOjH03TKoZHP58JqY5MEMrnQz7+Lpz/XBE2TpF3zvT/p4wzfDnLWGWc4Su8sg7CPe8k65yTZF04c5axyjjDl4wz/Dno3E6yzjk5RdaB8/w/KuMMX84yTnCesTrTOMOjH3mOKYI5Rd450/+fMs7w5SxjlXGGL3mO+V8u4d2hEEKIKCek2jy0++C+C34f2n1yQgghhKOTrBNCCBHZSdYJIYSICiTvhBBCRHZ2yzopPBFCCBFWh4Pfl1dK/V+uKKXiAUUAH+BMRA9MCCGECCeSdUIIISI7yTohhBBRgeSdEEKIyM5uWSeFJ//lEHcgWchZxirjDF/OMk5wnrHKOMNA07S7wD4gPdDli0+PBOIAKzRN+xjBQxOmOeTfp6+QcYYvZxknOM9YZZzhyyHHKVnntBzy79NXOMs4wXnGKuMMXzLO8OdwY5Wsc1oO93fJBGcZq4wzfDnLOMF5xirjDAPJO6flkH+fvkLGGb6cZZzgPGOVcYYvhxynPbNOaZoW3n0KIYSIYpRSmYBTQHJgG3ADKACUIui4rsKapr213wiFEEKIsJGsE0IIEdlJ1gkhhIgKJO+EEEJEdvbKOik8EUIIES6UUmmBUUBFIAnwHNgCjNQ0zdOeYxNCCCHCg2SdEEKIyE6yTgghRFQgeSeEECKys0fWyVU7n1FKVVRK3VJK3VFKDbD3eEKjlFqslHqllLpq77GERimVVil1WCl1XSl1TSnVw95jCo1SKqZS6g+l1OXgsY6095hMUUq5KqUuKqV22nssoVFKPVBK/aWUuqSUOm/v8YRGKZVQKbVRKXVTKXVDKVXI3mP6GqXU98Hfy5C390qpnvYe15c0TXusaVorTdNSaprmpmnat5qm9ZTJmmORrAtfzpJ3knW2IXkXfiTrRHhzhryTrAtfknW2IVkXfiTrRHhzhqwD58g7Z8k6kLyzBcm68CV5J8KTZF34cpa8k6wLf86SdeAceSdZFzo58SSYUsqVoKNlygFPgHNAI03Trtt1YF+hlCoOeAPLNU3LZu/xfI1SKiWQUtO0C0qpeMCfQE0H/X4qII6mad5KqejACaCHpmln7Dy0r1JK9QbyAvE1Tatq7/F8jVLqAZBX07Q39h6LKUqpZcBxTdMWKqXcgNiapr2z87BMCv5Z9RQooGnaQ3uPRzgXybrw5yx5J1lnG5J3tiFZJ8LKWfJOsi58SdbZhmSdbUjWibBylqwD58g7Z8k6kLyzBck625G8E2EhWRf+nCXvJOvCn7NkHThf3knW/T858eRf+YE7mqbd0zTNH1gL1LDzmL5K07RjgIe9x2GKpmnPNU27EPzrDwTdHZXavqP6Oi2Id/Bvowe/OWRFllIqDVAFWGjvsTg7pVQCoDiwCEDTNH9HDq/PlAHuSoAJK0nWhTNnyTvJuqjLSfNOsk6ElVPknWRd+JKsi7ok60QU5RRZB86Rd86SdSB5F1U5adaB5J0IG8m6cOYseSdZF3U5ad5J1n1GCk/+lRp4/Nnvn+CAP3CdkVIqPfAzcNbOQwlV8FFYl4BXwH5N0xx1rFMBd8Bo53GYowH7lFJ/KqXa23swocgAvAaWBB+DtlApFcfeg7JAQ2CNvQchnJZknQ05et5J1tmE5J1tSNaJsJK8sxHJunAzFcm68CRZJ6IiyTobcfSsA8k7G5Cssx3JOxEWknU25Oh5J1kX7pwh68A5806y7jNSeCJsSikVF9gE9NQ07b29xxMaTdMMmqblAtIA+ZVSDnccmlKqKvBK07Q/7T0WCxTVNC03UAnoEnzUnKOJBuQG5mia9jPwEXDYeyIBgo8Vqw5ssPdYhBD/zxnyTrLOJiTvwplknRCOS7IufEjW2YRknRAiXDhD1oHknQ1I1tmA5J0QjssZ8k6yLtw5Q9aBk+WdZN1/SeHJv54CaT/7fZrgjwkrBd+9tglYpWnaZnuPxxLBRzYdBiraeShfUwSoHnwX21qgtFJqpX2H9HWapj0Nfv8K2ELQsXiO5gnw5LNK2Y0EBZojqwRc0DTtpb0HIpyWZJ0NOFveSdaFH8k7m5CsE+FB8i6cSdaFK8m68CdZJ6Iiybpw5mxZB5J34UWyzmYk70RYSdbZgLPlnWRd+HCSrAPnyzvJui9I4cm/zgHfKaUyBFcoNQS223lMTksppQi6g+uGpmmT7T0eU5RSyZRSCYN/HQsoB9y066C+QtO0gZqmpdE0LT1Bfz8PaZrW1M7D+g+lVBylVLyQXwPlgav2HdV/aZr2AnislPo++ENlgOt2HJIlGiFHdomwkawLZ86Sd5J14U/yzmYk60R4kLwLR5J14UuyLvxJ1okoSrIuHDlL1oHkXXiTrLMpyTsRVpJ14cxZ8k6yLnw5S9aBU+adZN0Xotl7AI5C07RApVRXYC/gCizWNO2anYf1VUqpNUBJIKlS6gkwXNO0RfYd1X8UAZoBfwXfwwYwSNO03+03pFClBJYppVwJKsZar2naTjuPyZmlALYE/RuGaMBqTdP22HdIoeoGrAr+h+s9oJWdxxOq4H8QlAM62HsswnlJ1tmEs+SdZF34k7wLZ5J1Irw4S95J1oU7ybrwJ1kXziTrRHhxlqwDp8k7Z8k6kLwLb5J1NiB5J8KDZJ1NOEveSdaFL2fKOnCSvJOs+zqlaZq9xyCEEEIIIYQQQgghhBBCCCGEEEIIIZyQXLUjhBBCCCGEEEIIIYQQQgghhBBCCCGsIoUnQgghhBBCCCGEEEIIIYQQQgghhBDCKlJ4IoQQQgghhBBCCCGEEEIIIYQQQgghrCKFJ0IIIYQQQgghhBBCCCGEEEIIIYQQwipSeCKEEEIIIYQQQgghhBBCCCGEEEIIIawihSfC5pRSWvBbSXuPRQ+lVPrPxp4+nPt+ENxvy/Ds11a++F58/pYwjH2lD//RRgylVMJQvifp7T02IUTEk6z7at+SdU6eC5J1QojPSdZ9tW/JOifPBck6IcSXJO++2rfknZNng+SdEOJzknVf7VuyzslzQbLOMUjhSQRTSo0I5S++n1LqmVJqr1KqrVIqur3HamvBPwRGBL8ltPd4hMXeAC+D34x2Hos9Gfn3+/DGzmMRwqFI1v1Lss5pSdYFkawTIhSSdf+SrHNaknVBJOuEMEHy7l+Sd05L8i6I5J0QoZCs+5dkndOSrAsiWecAotl7AFHcy89+HQ9IGfxWHuiglCqvaZqnXUYWMRICw4N/vRR4Z6+BCF3yaZr2wN6DsDdN094D30BQRShw364DEsJxSdZJ1jkjyTok64TQQbJOss4ZSdYhWSeETpJ3knfOSPIOyTshdJCsk6xzRpJ1SNY5CjnxxI40Tfvms7c4wLfAguBP5wWm2290QgghRNhJ1gkhhIjsJOuEEEJEBZJ3QgghIjvJOiGECBspPHEgmqY90jStPXAo+EP1lVJx7TkmIYQQIjxJ1gkhhIjsJOuEEEJEBZJ3QgghIjvJOiGE0EcKTxzTnuD3bsB3X3uBUiqeUmqAUuq0Usoj+L65x0qptUqpQqF1rJRKpJQapZS6oJR6r5TyV0q9UEpdUUrNVUqV+eL16T+70y69iX4fBL+mpSVfoFLqCP9/zNH9L+7PO/LF67MqpeYrpW4rpXyUUp+Cv94zSqlflVJZLflzvzKO1EqpecF9+SmlniilliilMlvY3k0p1VkpdVgp9eaz7+c2pVQlK8eUQSnVXym1J/jr/aiU8lZKXVdKTVVKpftKm0TB3xdNKVXfTP+jg193TymlrBmjBV+DVd9XpZSLUqqMUmp68H/bJ8Hf07dKqaNKqY5fu0sx+ONa8P8LMc30H/J3dcQXn7PJ3zEhRKgk6yTrJOsk64SI7CTrJOsk6yTrhIgKJO8k7yTvJO+EiOwk6yTrJOsk64QlNE2Ttwh8A0YAWtC3PtTXuIe8Bsj7lc/nAh5/9ppA4P1nvzcCA7/SLg3w8LPXGQCP4PYhHzvyRZv0n30uvYkxPwh+TcuvfC6kfcnPPrYZeP3Z514DLz572/zZa8sBnz57rT/g+dnvNWCEFf8tcgd//SF9+AAfgn/tBdQ39bUTdMza1S++7+++GNccK75fRz5r7we8Cf5vFfKxd0DRr7RbGvz5Aya+ZlfgSfDrBun4Xln09yCs39cv/hwtuN2X39NjQKwv2sX77M9oamJsFfn3/5m04fV3TM/3R97kLSq8IVkX8jHJOsk6yTp5k7dI+oZkXcjHJOsk6yTr5E3eIvEbknchH5O8k7yTvJM3eYukb0jWhXxMsk6yTrJO3sL8JieeOKYKwe81/r/CEKVUSv7X3r3HWlaWdxz/PjBcxmFwQGCGS2WqRILFEgVKS6WZIlDbRtPaq21K8dpRSdDSpleChagVGmxslbtQrEQlVimmyBAbIk01FQLUUqnFMAEK2FJAZqjAwDz94303e501+3r2Ppxz9vl+kjd773V517vWXnv99h9P1oKbKIH0d5Tnyq3OzH2B9cD5lAvehyPiF1r9fhB4OeUCegqwZ2buD+xF+RG+B/jGtHeml8x8C3B8Y9LxOff5eW9pzLu4jnEL8JrM3DMz9wNWA0cD51L2aWQRsRb4IrAfcD9wGrAmM9cCJ1L+JFw6YP01lCrXH6GEzibK97AOWAf8LrAd2BwRZ40zNuBO4H3Aq2qfB1D2/4S6zZcCn4uI1a31Lq6vJ0fEK/r0/XPAoZSL+KfGHNdQkx7XOq7PAG8GXpaZa+sxXQu8DXgIOAn4UHOlzNwG/G39+K4B/Xfm3ZiZDzSmT/0ckzSUWWfWmXVmnTTrzDqzzqwz66SVwLwz78w7806adWadWWfWmXUaxWJXvqy0xoDqSUrAXEa3Cuv6HstcWed9ZsA2PlCXubM1/d/r9LeOMd6NjFAVxpjVk6P2DRzUWObgKX4PnQrVZ4CjeszfwNwKwI2t+efU6bcAe/TZxi/SrQxdNerxGjLu3YG76FMlCNxR532kz/o31PlfGHO7o54HEx3XEcZxXF1vO7B3a94xjX6P7LHuekpVZAJvmuY5NurxsdlWSsOsG6vvaVyH+vRr1o233VHPA7POrLPZwKwbq+9pXIf69GvWjbfdUc8Ds86ss9nINO/G7Xsa16I+/Zp342131PPAvDPvbDYw68bqexrXoT79mnXjbXfU88CsM+te1OYdTxZRfa5Ypz1FuaVWp7rrHuC9reX3Bn6jfvzogK6vqa/HRMT6xvQn6uvBEw38xbWNcjssmO64f72+XpeZ327PzMxHgEsGrP+O+npRZu7os8yXKLdTOwA4dp7jbI/rebrPE3x9j0U6FZRntJ+rFhGHAp1n2A2qYJzEpMd1oMy8DfhvYA3l9nXNeXcBX68f391j9bcBe1BuW/YPjekLdY5JwqwbkVk3d1xmnVknLStm3UjMurnjMuvMOmnZMe9GYt7NHZd5Z95Jy4pZNxKzbu64zDqzTg0Wniyu9Y32ksb0a4DXZuZ/tZY/Fti7vt/SCsEXGnB3Y53DG++/XF//PCIui4g3RsS+09ud6cvMHwBfrR+/EhHnRcQJEbHnfPus676mfvzHAYv2nFfDoHNcrxzwPTwM7FOXO7xXXwPGeFJEXB0R90TE9ojITqNUKEK5dVvbtZSL8gbgTa15b6dUX94H3DzOeEYc80THtdlPRGyOiC0R8VBEPNPa/4Pqor32vxOQpzfPkYgI4J3145X1zwCwMOeYpDnMuiHMOrPOrJOWPbNuCLPOrDPrpJlg3g1h3pl35p207Jl1Q5h1Zp1Zp0EsPFlEmRmZGZTv4RBgM6XC8XTgzB6rHNJ4v35I62iG44XA5ykVZO8CbgSeiIhvRcSFEXHkFHZrIbyTcquqAym3y/oGsC0i/ikifj8i9h+zv/2BVfV9+49C04N9pje/hwMY/D10fmPN72GgiPgo8DXgt4EjKX9cHge+V9tTddE17XUzczvd56a9UEEYEbvRrfi8PLPca2rKJj2uRMRBwG2UKtBTKdWMO4FH6e5/p9Jxl/2nnN+PUb6X5jMHTwZeSXmW4hU91pv2OSapMutGZtaZdWadtEyZdSMz68w6s05axsy7kZl35p15Jy1TZt3IzDqzzqxTTxaeLAFZPJyZl9J9xtgFEXFya9HdG+9Xd0JwSLulsZ0dmflrlNsdnUepYvs/4Gjg94C7I+LshdvT+cnM+4HXAW8EPg7cTjl3fxK4ALi3x7FaSM3v4agRv4erR+k4Ik6lWx35SUo14l6ZuX9mbsjMDcDHOov36ebi+npqRGys70+jVHA+B1w10l4ujo9R9vl/KdWeB2fm6sw8sLH/D9Vld9n/zHwauLp+bN66q3M7vBszc5cQXYLnmDRzzLrBluB1yKxbOGadNKPMusGW4HXIrFs4Zp00w8y7wZbgtci8WzjmnTSjzLrBluB1yKxbOGadxmLhyRJTQ+fTlB/oX0VE84L5SOP9WLeBam3jrsw8NzPfAKwDTqFU6+0OXBgRxzQWf67xfm/6e+l8xzOKzNyZmTdl5lmZeRylUu83gfuB/YBrx7jN0mOUKjqAQwcs12/eVL6HPjrPW7spM9+Xmf+WjVtMVRsGdZCZ3wL+mfL77lRMdi7i12d5ZttCmOi4Rnm+Xafi8czMvKo91vp7OGDIOC6l/BHcFBFHRMQBlD+HnXk9TfkckzSAWdebWTeHWTeYWSctcWZdb2bdHGbdYGadtAyYd72Zd3OYd4OZd9ISZ9b1ZtbNYdYNZtatIBaeLE3nUS4Gr6bcuqnjm8Cz9X37WWDzkpnPZeZXgZ8HnqGE5ymNRR5vvP+hXn1ExKsoYTiunY33/SoBe8rMbZl5Ld2L9Hq6zyobtu6zwL/Wjz89YNGe1XKZuZXubamm8j00dI7xHb1mRkT0G1dLp4Ly7VGeb9cZ52WTDa+/SY8r5ZZZnT9KPfcfeD2D/0yRmd+hVAYHJbxPB/YEHqDcqm4kk5xjkkZi1g1h1g1l1pl10lJn1g1h1g1l1pl10nJg3g1h3g1l3pl30lJn1g1h1g1l1pl1K4KFJ0tQZn4X+Fz9eE6tKiMznwKurdP/ICJePqif9jOuImKvAYs/Q7fy7YVwqdv8bv34S33W/ZNB4xjgycb7db0WGKFa7QeN9zv7LrWrzvH9lV7PyavPLds8YP3L6+s7IuK1gzY05rPGvl9fj+kzfzPwihH6uY5y66tDKOfMHsB9wM1jjGU+JjmuT1KqHqHH/kfEKuBDI47jkvp6Bt3bd32qRyXqQp5jkgYw67rMul2YdaMx66QlzqzrMut2YdaNxqyTlgHzrsu824V5NxrzTlrizLous24XZt1ozLqVIjNtL2IDPkj5oeaQ5Y6m/GAS2NyYfjClci/r628BaxvzD6SEzRcpt35q9vkI8BHgxynPIOtMPwL4fO3zeeDVrfXOr/OeBd5LeVYdlEq/K4CngafqMmf02JesbVOPeQ/WeR8HVvWYv4lSkfcB4Chgtzo9gBPrvKRUxu0+xvewb10nKRf3NwBR551Q+328MfaNrfX3aWz7CeBM4GWN+euAnwWuAe7usf2tvY4XpVKvs81zgDWN/v6Ychu1R+v8W4bs4180+krgjyY4bzf2OxZTPq631ukPUqosO9/30cCWeq5t73euNfpZBTzc2M5zwGF9lp34HBv1+NhsK6Vh1rXnmXVzp5t1Zp3NtuwbZl17nlk3d7pZZ9bZbDPRMO/a88y7udPNO/POZlv2DbOuPc+smzvdrDPrbOOcm4s9gJXWGDHE6rJfavx4mqFzFPAfjR/N85RKue3MvWjd3OovW+s8RqkM60zbCby/xzj2Ae5urdu5ED1LecZZz4tya7ubesz708b8pynP5toKfLbO39Qa97OUi/iOxrTvAyfN47s4rnVBfQrYVt8/CfzqoAsTpTLx663j93gdT3PM/9lj3Z7Hi1Ll+LVWn53nsCXwZbp/Km4Zsn9H0P0jtAPYMMF5u3HQsZjWcQWObZ3HT9OtqtxB+dPW91xr9XV+o58bBiw38Tk2zvGx2VZCw6xrzzPr5k4368w6m23ZN8y69jyzbu50s86ss9lmomHeteeZd3Onm3fmnc227BtmXXueWTd3ulln1tnGaD5qZ2nr3KLoMOB3OhMz89vAj9ZpWyg/uH0p1V73Um7Z9G7KxaLpNEr15K2UYFxdp98LXAUcn5l/2R5EZm6nPKfrIkpF3HOUH/gXgJ/IzM9OsI8fBs4Cbqt9HgYcDmyo879Z9+Ni4PbGvj4N3AlcAByVmbeOu+HMvI1yHK+gVKKuolys/gZ4HfAvQ9Z/iHJc3gr8PaVa7yWUZ5NtBW4A3g/81Bhj2kH5nv4M+A7lmEQdy3uAN9O9vdqwvu6lHCOA6zPzkVHHMYlJjmtm3g78GKWa91HK48C21c8nZuanxxjKdY33lw5YbsHOMUkjMevMOrPOrJNmnVln1pl1Zp20Eph35p15Z95Js86sM+vMOrNOA3RupyNpxkTEBsqflVXAz2Tmlgn62kj5AwPww5m5deIBLrCIOJty67IHKGMeKfznua2NLLPjI0mzwKwz6yRp1pl1Zp0krQTmnXknSbPOrDPrVgLveCLNrs2UALsXuHmRx/KiiojdKdWmAJcvZIBJkhaVWVeYdZI0u8y6wqyTpNlm3hXmnSTNLrOuMOtm2KrFHoCk6YuI44Cz68eLcrq3NrovIjrv98vMJ6bY98QiYjfKbc9eSXlW3SULtJ11lOfiSZIWgVln1knSrDPrzDpJWgnMO/NOkmadWWfWrRQWnkgzJCK2AnvRfd7eHZTntk3qeeB7PabvnELfUxERv0y5Tdf+wNo6+dzM/J8F2uROeh8TKzUlaQGZdWadJM06s86sk6SVwLwz7yRp1pl1Zt1KY+GJNFsOr6+PAF8B/jAzd0zaaWY+QDcYl6p9KPu/A7gH+OvM/MRCbSwzn2TpHxNJmkVmnVknSbPOrDPrJGklMO/MO0madWadWbeixHTv5iNJkiRJkiRJkiRJkqSVYrfFHoAkSZIkSZIkSZIkSZKWJwtPJEmSJEmSJEmSJEmSNC8WnkiSJEmSJEmSJEmSJGleLDyRJEmSJEmSJEmSJEnSvFh4IkmSJEmSJEmSJEmSpHmx8ESSJEmSJEmSJEmSJEnz8v9IXIBFG6qh+AAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot\n", - "levels = np.arange(0,105,5)\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 5\n", - "\n", - "fig,ax=plt.subplots(4,5)\n", - "for i in range(len(examrate)): \n", - " cp = ax[int(i/n),i%n].contourf(T_T,beta,totinfected[i,:,:],levels)\n", - " ax[int(i/n),i%n].set_title('Tested: '+str(100*(examrate[i])/population)+'%',fontsize=25)\n", - " for l in levelcourves:\n", - " cp2 = ax[int(i/n),i%n].contour(T_T,beta,totinfected[i,:,:],[0,l,100],colors='white',linestyles='dashed') \n", - " if np.min(totinfected[i,:,:])" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "color = False\n", - "\n", - "levels = [0,5,100]\n", - "colors = plt.cm.jet(np.linspace(0,1,int(len(beta)/2)))\n", - "\n", - "n = 3\n", - "fig,ax=plt.subplots(1,1)\n", - "#ax.set_title('Beta: '+str(round(beta[j],2)))\n", - "ax.set_xlabel('Results delay [days]',size=25)\n", - "ax.set_ylabel('Tested [%]',size=25) \n", - "ax.tick_params(labelsize=25)\n", - "for j in range(int(len(beta)/2)):\n", - " if color:\n", - " cp = ax.contour(T_T,examrate*100/population,totinfected[:,2*j,:],levels,colors=colors[j]) \n", - " else:\n", - " cp = ax.contour(T_T,examrate*100/population,totinfected[:,2*j,:],levels,colors='black') #colors[j]\n", - " #cp = ax.contourf(T_T,examrate*100/population,beta) # Agregar gradiente de beta\n", - " ax.clabel(cp, inline=1, fontsize=15,fmt=str(round(beta[2*j],2)))\n", - "\n", - "if saveplot:\n", - " if color:\n", - " plt.savefig('plotContourBeta_color.pdf',dpi=100,format='pdf')\n", - " else:\n", - " plt.savefig('plotContourBeta_byn.pdf',dpi=100,format='pdf')\n", - "plt.show() \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estudio de tiempos Seriales\n", - "Estudio del efecto que tienen distintos tiempos seriales en el efecto de las estrategias de examinación. El tiempo serial se puede calcular de la siguiente manera: \n", - "$T_s = (\\frac{1}{\\gamma} + \\frac{1}{\\sigma})$ \n", - "\n", - "Para este estudio fijarémos $\\frac{1}{\\sigma}$ en 2 días, y utilizaremos un rango de valores comprendidos entre 1 y 20 días para $\\frac{1}{\\gamma}$.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'Quarantine' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;31m# Quarantine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0ms1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQuarantine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'Quarantine' is not defined" - ] - } - ], - "source": [ - "mu = 0 # E0/I0 initial rate\n", - "sigma = 1/5\n", - "gamma = [1/i for i in range(1,20)]\n", - "\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 100000\n", - "# Initial Active Infected \n", - "I0 = 100\n", - "I_ac0 = 100\n", - "\n", - "# Quarantine\n", - "s1 = Quarantine(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 1\n", - "period = 1\n", - "testaccuracy=0.9\n", - "lambda_Tr = 0 # traceability\n", - "lambda_Q=1 # Proportion of effectively quarantined detected infected\n", - "T_Q = 14 # Time in quarantine\n", - "\n", - "# Exam Rate\n", - "examrate = np.arange(1,21,1)*population/100# Persons per day\n", - "\n", - "# Delay Time\n", - "T_T = np.arange(0.0,7.5,0.5)\n", - "# Beta\n", - "beta = np.arange(0.11,0.21,0.005)\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims_reference = []\n", - "for i in range(len(beta)):\n", - " sims_reference.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta[i],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = 1,T_Q = T_Q,lambda_Tr=lambda_Tr))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "levels = np.arange(0,105,5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reference Simulation\n", - "Here we simulate situations without exams that will serve as references" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - 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"end = time.time()\n", - "easygui.msgbox('simulation finished - duration: '+str(end-start)+' seconds', title=\"SEIRTQ\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(beta)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta[j],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T[k],T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " aux.append(aux2)\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1239s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1120s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1291s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1059s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0982s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1429s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1125s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1292s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1230s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1105s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1248s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1367s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0792s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0802s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0767s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1032s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0988s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0853s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1278s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1234s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0877s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1205s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1013s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0964s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1121s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1101s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1407s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0959s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1479s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1204s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1554s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1366s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0941s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0487s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0620s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0694s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0718s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0806s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1325s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0901s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1381s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1272s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1350s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1339s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1112s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1122s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1202s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1322s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1047s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1114s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0970s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1479s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0718s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0549s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0719s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1164s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0795s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0777s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0984s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1011s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1339s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0881s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1338s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1310s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1341s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1508s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1369s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1284s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1302s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1176s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1280s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1351s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0734s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0545s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0610s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0581s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0613s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0929s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0537s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0695s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1278s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1048s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0982s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1190s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0981s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1404s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1488s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1283s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0970s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1161s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1425s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1028s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0674s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0598s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0466s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0537s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0910s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0668s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0666s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1080s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0746s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1148s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0924s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1180s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1159s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0849s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1110s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1119s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1245s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0904s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1471s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1548s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1048s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0664s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0732s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0533s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0701s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0692s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0653s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0904s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0618s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1076s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0824s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0824s.) Setting batch_size=4.\n", - "[Parallel(n_jobs=12)]: Done 2 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0943s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0710s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1167s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1509s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1154s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1306s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1513s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1577s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1241s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0966s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1067s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0669s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0892s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0784s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0784s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0682s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0560s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0822s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0845s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0915s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0699s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0920s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1169s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0850s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1343s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1224s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1268s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1344s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0846s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0688s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1117s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1042s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0781s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0692s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0640s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0617s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0690s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0675s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0782s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0498s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0866s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0661s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0685s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1562s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1161s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0789s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0806s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1260s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1099s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1044s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1156s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1131s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1262s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0983s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0657s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0907s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0796s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0736s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0604s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0903s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0734s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0683s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0588s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1084s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1148s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0788s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1103s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1039s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.3s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1083s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0863s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0919s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1351s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0889s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1033s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1138s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0846s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0813s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0719s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0719s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0706s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0748s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0681s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0686s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0763s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0710s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1075s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0697s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0686s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1113s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0770s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1195s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1044s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0965s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0736s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0764s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0811s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0791s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0796s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0875s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0971s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0720s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0895s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0480s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0697s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0970s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0803s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0582s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0917s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0941s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1026s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1207s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0805s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0837s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0677s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0834s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0819s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1069s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0943s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0771s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0722s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0879s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0929s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0926s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0470s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0657s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0609s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0665s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0622s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0808s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0994s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0779s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0828s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0895s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1094s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1090s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0976s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0988s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0743s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0720s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0942s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0951s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0733s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0844s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0698s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0843s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0591s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1431s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.3s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0673s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0456s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1171s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1232s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0605s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1154s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0660s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1321s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0657s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1203s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1037s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0958s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1005s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0787s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0639s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0846s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0891s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0869s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0611s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0624s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0781s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0853s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1005s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0855s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0844s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1032s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0613s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0963s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0684s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1003s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0750s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0822s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0823s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0829s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0761s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1130s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0759s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0761s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0625s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1086s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0583s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0941s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0892s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0941s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1358s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0874s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0755s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0829s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0931s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0823s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1035s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1111s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0988s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0795s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0822s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0921s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1048s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0782s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0795s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0709s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0870s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0733s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1505s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0886s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0859s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0786s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1304s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1268s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1239s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0888s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0776s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1016s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0879s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0766s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0908s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1092s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1020s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0755s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0690s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0811s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0975s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0697s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1305s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1060s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0953s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1235s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0764s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1055s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1061s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0948s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0949s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0648s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0753s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0793s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1967s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.3s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0657s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1102s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0674s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1010s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0639s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1106s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0838s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0865s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0742s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1036s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0910s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0674s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0859s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1201s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0866s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0857s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0841s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0855s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0938s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0892s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0886s.) 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Hacerlo para ambos ejes independientemente y ver si calzan" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scipy.optimize import curve_fit\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "# test function\n", - "def function(data, a, b, c):\n", - " x = data[0]\n", - " y = data[1]\n", - " return a * (x**b) * (y**c)\n", - "\n", - "# setup test data\n", - "raw_data = [2.0, 2.0, 2.0], [1.5, 1.5, 1.5], [0.5, 0.5, 0.5],[3.0, 2.0, 1.0], [3.0, 2.0, 1.0],\\\n", - " [3.0, 2.0, 1.0], [2.4, 2.5, 2.2], [2.4, 3.0, 2.5], [4.0, 3.3, 8.0]\n", - "\n", - "# convert data into proper format\n", - "x_data = []\n", - "y_data = []\n", - "z_data = []\n", - "for item in raw_data:\n", - " x_data.append(item[0])\n", - " y_data.append(item[1])\n", - " z_data.append(item[2])\n", - "\n", - "# get fit parameters from scipy curve fit\n", - "parameters, covariance = curve_fit(function, [x_data, y_data], z_data)\n", - "\n", - "# create surface function model\n", - "# setup data points for calculating surface model\n", - "model_x_data = np.linspace(min(x_data), max(x_data), 30)\n", - "model_y_data = np.linspace(min(y_data), max(y_data), 30)\n", - "# create coordinate arrays for vectorized evaluations\n", - "X, Y = np.meshgrid(model_x_data, model_y_data)\n", - "# calculate Z coordinate array\n", - "Z = function(np.array([X, Y]), *parameters)\n", - "\n", - "# setup figure object\n", - "fig = plt.figure()\n", - "# setup 3d object\n", - "ax = Axes3D(fig)\n", - "# plot surface\n", - "ax.plot_surface(X, Y, Z)\n", - "# plot input data\n", - "ax.scatter(x_data, y_data, z_data, color='red')\n", - "# set plot descriptions\n", - "ax.set_xlabel('X data')\n", - "ax.set_ylabel('Y data')\n", - "ax.set_zlabel('Z data')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# QA:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### QA: Population consistency" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "j=0\n", - "k=0\n", - "max(simulation[i][j][k].S+simulation[i][j][k].E+simulation[i][j][k].I+simulation[i][j][k].R-population+simulation[i][j][k].Q+simulation[i][j][k].I_T)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i=-1\n", - "j=-1\n", - "k=-1\n", - "max(simulation[i][j][k].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation[i][j][k].dI_T(0,10,100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "psi[-1](100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## QA: No delay time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sim_NoT_T= SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[-1],beta=beta[-1],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = 0,T_Q = T_Q,lambda_Tr=lambda_Tr)\n", - "sim_NoT_T.integr_sci()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sim_NoT_T = []\n", - "for i in range(len(examrate)):\n", - " sim_NoT_T.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta[-1],sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = 0,T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - "\n", - " # Reference simulation\n", - "# Run simulation\n", - "start = time.time()\n", - "num_cores = multiprocessing.cpu_count()\n", - "sim_NoT_T = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate_reference)(sim_NoT_T[i],tsim) for i in range(len(examrate)))\n", - "end = time.time()\n", - "easygui.msgbox('simulation finished - duration: '+str(end-start)+' seconds', title=\"SEIRTQ\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,int(len(beta))))\n", - "plt.plot(simulation_reference[-1].t,simulation_reference[-1].I/np.max(simulation_reference[-1].I),color = 'black',linestyle='dashed')#label='I_d No exams'\n", - "for i in range(len(examrate)):\n", - " plt.plot(sim_NoT_T[i].t,sim_NoT_T[i].I/np.max(simulation_reference[-1].I),color = colors[i],label='ExamRate: '+str(int(examrate[i])))#label='I_d No exams'\n", - " \n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sim_NoT_T[0].T_T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "for i in range(len(examrate)):\n", - " print(max(sim_NoT_T[i].S+sim_NoT_T[i].E+sim_NoT_T[i].I+sim_NoT_T[i].R-population+sim_NoT_T[i].Q+sim_NoT_T[i].I_T))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/Serial_Time_Study.ipynb b/research/ExamDynamics/Serial_Time_Study.ipynb deleted file mode 100644 index cfa1a63..0000000 --- a/research/ExamDynamics/Serial_Time_Study.ipynb +++ /dev/null @@ -1,8012 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examination dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIR/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "import time\n", - "import easygui\n", - "#import matplotlib\n", - "#matplotlib.axes.Axes.text\n", - "#matplotlib.pyplot.text\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows': \n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "from class_SEIRQ import SEIR\n", - "from Quarantine import Quarantine\n", - "from Quarantine import Exams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estudio de tiempos Seriales\n", - "Estudio del efecto que tienen distintos tiempos seriales en el efecto de las estrategias de examinación. El tiempo serial se puede calcular de la siguiente manera: \n", - "$T_s = (\\frac{1}{\\gamma} + \\frac{1}{\\sigma})$ \n", - "\n", - "Para este estudio fijarémos $\\frac{1}{\\sigma}$ en 2 días, y utilizaremos un rango de valores comprendidos entre 1 y 20 días para $\\frac{1}{\\gamma}$.\n", - "\n", - "Trabajaremos con un beta fijo y con un rango de valores para tasas de exámenes $(\\lambda)$ y demora en la obtención de los resultados. \n", - "Valores: \n", - "\n", - "* $\\frac{1}{\\sigma} = 2$ días \n", - "* $\\frac{1}{\\gamma} \\in [1,20]$ días \n", - "* $\\beta = 0.15$ \n", - "* $T_T \\in [0,7]$ días \n", - "* $\\lambda \\in [1,20] \\%$ de la población de exámenes diarios \n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "mu = 0 # E0/I0 initial rate\n", - "sigma = 1/5\n", - "gamma = [1/i for i in range(1,21)]\n", - "\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 100000\n", - "# Initial Active Infected \n", - "I0 = 100\n", - "I_ac0 = 100\n", - "\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "duty = 1\n", - "period = 1\n", - "testaccuracy=0.9\n", - "lambda_Tr = 0 # traceability\n", - "lambda_Q=1 # Proportion of effectively quarantined detected infected\n", - "T_Q = 14 # Time in quarantine\n", - "\n", - "# Exam Rate\n", - "examrate = np.arange(1,21,1)*population/100# Persons per day\n", - "\n", - "# Delay Time\n", - "T_T = np.arange(0.0,7.5,0.5)\n", - "# Beta\n", - "beta = 0.15\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims_reference = []\n", - "for i in range(len(gamma)):\n", - " sims_reference.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,sigma=sigma,gamma=gamma[i],mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = 1,T_Q = T_Q,lambda_Tr=lambda_Tr))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reference Simulation\n", - "Here we simulate situations without exams that will serve as references" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 20 | elapsed: 1.4s remaining: 12.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 20 | elapsed: 1.5s remaining: 8.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 20 | elapsed: 1.5s remaining: 6.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 20 | elapsed: 1.5s remaining: 4.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 20 | elapsed: 1.5s remaining: 3.6s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 20 | elapsed: 1.5s remaining: 2.9s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 20 | elapsed: 1.5s remaining: 2.3s\n", - 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] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For parallel simulation\n", - "def simulate_reference(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "# Reference simulation\n", - "# Run simulation\n", - "start = time.time()\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation_reference = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate_reference)(sims_reference[i],tsim) for i in range(len(gamma)))\n", - "end = time.time()\n", - "easygui.msgbox('simulation finished - duration: '+str(end-start)+' seconds', title=\"SEIRTQ\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta,sigma=sigma,gamma=gamma[j],mu=mu,I0=I0,population=population,expinfection=0,testaccuracy=testaccuracy,lambda_Q=lambda_Q,T_T = T_T[k],T_Q = T_Q,lambda_Tr=lambda_Tr))\n", - " aux.append(aux2)\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1424s.) 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1050s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1556s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1215s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0738s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1134s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1145s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0893s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1262s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1248s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0889s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1173s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0712s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1006s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1248s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0890s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0536s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0669s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0709s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1056s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0689s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1114s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1257s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1081s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0805s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1256s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1100s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1181s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1199s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1231s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1265s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0757s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1021s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1024s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0729s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0849s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0750s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0751s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0704s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1191s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1126s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1127s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0959s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1130s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1205s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1160s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1237s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1323s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1249s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1291s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1277s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1022s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0984s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0708s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0812s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0794s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0744s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0452s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0651s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0861s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0661s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1038s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1275s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0932s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1075s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1202s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1034s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1347s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1284s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1188s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1053s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1132s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1197s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0655s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0909s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0928s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0962s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0742s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0632s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0591s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0515s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0491s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0784s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1147s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1009s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1240s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0902s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1367s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1234s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1106s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1379s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1411s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0709s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0821s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1148s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0664s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0587s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0623s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0790s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0497s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0649s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0557s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0954s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1102s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0825s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1264s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0768s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1140s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1133s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1313s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1398s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1046s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1310s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1222s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0789s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1084s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0678s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0855s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0982s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0546s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0574s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0581s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0558s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0952s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0715s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0863s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0786s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1174s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0966s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0769s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1495s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1172s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1133s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0610s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1246s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0715s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1024s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0686s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0630s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0620s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0622s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0599s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0688s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0501s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0685s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0748s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0865s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0500s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1401s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1324s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1286s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0629s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1218s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0912s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1041s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0986s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0777s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0756s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0703s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0686s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0600s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0690s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0655s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0818s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0803s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0780s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0546s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1090s.) 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1402s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0862s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1086s.) 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Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1015s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0841s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0727s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0616s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0647s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0877s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0640s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0722s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0649s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0568s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1130s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1752s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1158s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1287s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1066s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0905s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0946s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0832s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0911s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1207s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0799s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0961s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0787s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0846s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0860s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0772s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0587s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0576s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0881s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0549s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0570s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1195s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1483s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1733s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1256s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1255s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1078s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1033s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0789s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1185s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1007s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1068s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0767s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0727s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0976s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0747s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0936s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0745s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0584s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0871s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0790s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0947s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1421s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1517s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1191s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1207s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1197s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1298s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0693s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1064s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1083s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1039s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0872s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0900s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1027s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0662s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0766s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0609s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0807s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0772s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0829s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1032s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1521s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1159s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1068s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0829s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0996s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0953s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1224s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1047s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0927s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1057s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0849s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0972s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0855s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0818s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1149s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0857s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0835s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0844s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0998s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1333s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1558s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1133s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1338s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0659s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1266s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0896s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1291s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0759s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1118s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0925s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0790s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0901s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0789s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0985s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.4s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0790s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0920s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0782s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0771s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1305s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1079s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1534s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1127s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1209s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1033s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0686s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0964s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0971s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0898s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0843s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1126s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1169s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0834s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0786s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0804s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0754s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0588s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0893s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0971s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1639s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1658s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1482s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1133s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1140s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0751s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1289s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1203s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0877s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0786s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0762s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1037s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1109s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0997s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1058s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0745s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0849s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0721s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0904s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0988s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1606s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1327s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1458s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1167s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1097s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1181s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0951s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0799s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0759s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1142s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1147s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0872s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0778s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1082s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0810s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0907s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0613s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1142s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0784s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0817s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1479s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1383s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1334s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0973s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1074s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1193s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1091s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1225s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0820s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1292s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0985s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1003s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1011s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0797s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0904s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0960s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1151s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0939s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1110s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0701s.) Setting batch_size=2.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1329s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.3s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1446s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.3s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1435s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1477s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.2s remaining: 1.0s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0777s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1068s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.2s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0793s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1077s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0805s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0982s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.2s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1206s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.5s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.1s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0727s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1048s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.9s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.2s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.2s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.3s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.1187s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 15 | elapsed: 0.1s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 15 | elapsed: 0.1s remaining: 0.6s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 15 | elapsed: 0.1s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 15 | elapsed: 0.1s remaining: 0.3s\n", - "[Parallel(n_jobs=12)]: Done 6 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 15 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 8 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 9 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 10 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 11 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 12 out of 15 | elapsed: 0.2s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 13 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0846s.) 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"[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 15 out of 15 | elapsed: 0.2s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "'OK'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#%%capture\n", - "# To show the cell's output comment the former line\n", - "# For parallel simulation\n", - "start = time.time()\n", - "def simulate(simulation,k,tsim):\n", - " simulation[k].integr_sci(0,tsim,0.1)\n", - " return simulation[k]\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i][j],k,tsim) for k in range(len(sims[i][j]))))\n", - " simulation.append(aux)\n", - "end = time.time()\n", - "easygui.msgbox('simulation finished - duration: '+str(end-start)+' seconds', title=\"SEIRTQ\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(100*simulation[i][j][k].peak/population)\n", - " aux.append(aux)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)):\n", - " aux2.append(simulation[i][j][k].peak_t)\n", - " aux.append(aux2)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(simulation[i][j][k].prevalence_total[-1]) \n", - " aux.append(aux2)\n", - " prevalence.append(aux)\n", - "prevalence = np.array(prevalence)\n", - " \n", - "# Calculate indicators\n", - "peakvsnoexam = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(simulation[i][j][k].I/np.max(simulation_reference[j].I))\n", - " aux.append(aux2)\n", - " peakvsnoexam.append(aux)\n", - "#peakvsnoexam = np.array(peakvsnoexam)\n", - "# Calculate indicators\n", - "totinfected = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(100*simulation[i][j][k].I_ac[-1]/simulation_reference[j].I_ac[-1])\n", - " aux.append(aux2)\n", - " totinfected.append(aux)\n", - "\n", - "totinfected = np.array(totinfected)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "test = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(gamma)):\n", - " aux2 = []\n", - " for k in range(len(T_T)): \n", - " aux2.append(100*simulation[i][j][k].peak)\n", - " aux.append(aux2)\n", - " test.append(aux)\n", - "test = np.array(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "peak_reference = []\n", - "for j in range(len(gamma)):\n", - " peak_reference.append(simulation_reference[j].peak)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 1: Grid Epidemic plots" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "if saveplot:\n", - " %matplotlib inline\n", - "%matplotlib inline \n", - "\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56 #8,6\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gamma[4]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "examrate[9]/population" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,int(len(T_T)/2+1)))\n", - "levels = np.arange(0,105,5)\n", - "n = 4\n", - "i = 9 # 10% of exams\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+(int(len(examrate))%n > 0)*1, n)\n", - "for j in range(len(gamma)):\n", - " axs[int(j/n),j%n].plot(simulation_reference[j].t,simulation_reference[j].I/np.max(simulation_reference[j].I),color = 'black',linestyle='dashed')#label='I_d No exams'\n", - " for k in range(int(len(T_T)/2)+1):\n", - " axs[int(j/n),j%n].plot(simulation[i][j][k*2].t,simulation[i][j][k*2].I/np.max(simulation_reference[j].I),color=colors[k])\n", - " axs[int(j/n),j%n].tick_params(labelsize=15)\n", - " if int(i/n)==4: \n", - " axs[int(j/n),j%n].set_xlabel('Days',size=25)\n", - " if i%n==0:\n", - " axs[int(j/n),j%n].set_ylabel('Active Infected',size=25)\n", - " #axs[int(i/n),i%n].set_title('Exam Rate: '+str(round(examrate[i]/population*100))+'%')\n", - " #axs[int(i/n),i%n].plot([],[],label='Exam Rate: '+str(round(examrate[i]/population*100))+'%')\n", - " axs[int(j/n),j%n].text(1000, 1,'Infectious Time: '+str(round(1/gamma[j]))+' days', {'fontsize': 25},horizontalalignment='right',verticalalignment='top')\n", - " #axs[int(i/n),i%n].legend(loc=0,fontsize=15) \n", - "\n", - "# Legend \n", - "for k in range(int(len(T_T)/2)+1):\n", - " axs[0,0].plot([],[],color=colors[k],label=str(int(T_T[k*2])))\n", - "fig.legend(loc='lower center',bbox_to_anchor=(0.5, 0.025),ncol=int(len(T_T)/2)+1,title = 'Results delay [days]',fontsize='15',title_fontsize='25') \n", - "\n", - "if saveplot:\n", - " plt.savefig('plot1.pdf',dpi=100,format='pdf')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 2: Tests vs Delay para distintos gamma" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total infected normalized by the dynamic with no exams" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = False" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "if saveplot:\n", - " %matplotlib inline\n", - "%matplotlib inline\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot\n", - "levels = np.arange(0,105,5)\n", - "# Colormap:\n", - "# https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html\n", - "# jet , hsv , Greys , YlOrRd\n", - "colors = plt.cm.BuPu(np.linspace(0,1,int(len(levels)+1)))\n", - "\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 2\n", - "\n", - "fig,ax=plt.subplots(5,2)\n", - "for j in range(int(len(gamma)/2)): \n", - " cp = ax[int(j/n),j%n].contourf(T_T,examrate*100/population,totinfected[:,2*j,:]) \n", - " for l in levelcourves:\n", - " cp2 = ax[int(j/n),j%n].contour(T_T,examrate*100/population,totinfected[:,2*j,:],[0,l,100],colors='white',linestyles='dashed')\n", - " ax[int(j/n),j%n].clabel(cp2, inline=1, fontsize=20,fmt=str(l))\n", - " ax[int(j/n),j%n].set_title(r'$\\gamma$ = '+str(round(1/gamma[j*2],2)),fontsize=25)\n", - " ax[int(j/n),j%n].tick_params(labelsize=20)\n", - " if int(j/n)==4: \n", - " ax[int(j/n),j%n].set_xlabel('Results delay [days]',size=25) \n", - " if j%n==0:\n", - " ax[int(j/n),j%n].set_ylabel('Tested [%]',size=25)\n", - " \n", - " cbar = fig.colorbar(cp, ax=ax[int(j/n),j%n]) # increase fontsize\n", - " cbar.ax.tick_params(labelsize=20)\n", - " \n", - "#fig.suptitle('Total infected proportion to the dynamic with no exams')\n", - "#plt.tight_layout()\n", - "plt.subplots_adjust(hspace=0.4)\n", - "\n", - "if saveplot:\n", - " plt.savefig('plot2.pdf',dpi=100,format='pdf')\n", - "plt.show() \n", - "#fig.colorbar(cp) # Add a colorbar to a plot\n", - "#fig.colorbar(cp, ax=ax)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot 3: Test vs Beta para distintos delay" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "saveplot = True" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "if saveplot:\n", - " %matplotlib inline\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = 38.40, 20.56\n", - "plt.rcParams.update({\n", - " \"text.usetex\": False,\n", - " \"font.family\": \"sans-serif\",\n", - " \"font.sans-serif\":[\"Arial\"]})" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":12: UserWarning: No contour levels were found within the data range.\n", - " cp2 = ax[int(k/n),k%n].contour(beta,examrate*100/population,totinfected[:,:,k*2],[0,l,100],colors='white',linestyles='dashed')\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot\n", - "levels = np.arange(0,105,5)\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 2\n", - "\n", - "fig,ax=plt.subplots(4,2)\n", - "for k in range(round(len(T_T)/2)): \n", - " cp = ax[int(k/n),k%n].contourf(beta,examrate*100/population,totinfected[:,:,k*2],levels) \n", - " ax[int(k/n),k%n].set_title('Results delay: '+str(int(T_T[k*2]))+' days',fontsize=25)\n", - " for l in levelcourves:\n", - " cp2 = ax[int(k/n),k%n].contour(beta,examrate*100/population,totinfected[:,:,k*2],[0,l,100],colors='white',linestyles='dashed') \n", - " if np.min(totinfected[:,:,k*2]):12: UserWarning: No contour levels were found within the data range.\n", - " cp2 = ax[int(i/n),i%n].contour(T_T,beta,totinfected[i,:,:],[0,l,100],colors='white',linestyles='dashed')\n", - "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Contour Plot\n", - "levels = np.arange(0,105,5)\n", - "levelcourves = [5,25,50,75]\n", - "#levelcourves = []\n", - "n = 5\n", - "\n", - "fig,ax=plt.subplots(4,5)\n", - "for i in range(len(examrate)): \n", - " cp = ax[int(i/n),i%n].contourf(T_T,beta,totinfected[i,:,:],levels)\n", - " ax[int(i/n),i%n].set_title('Tested: '+str(100*(examrate[i])/population)+'%',fontsize=25)\n", - " for l in levelcourves:\n", - " cp2 = ax[int(i/n),i%n].contour(T_T,beta,totinfected[i,:,:],[0,l,100],colors='white',linestyles='dashed') \n", - " if np.min(totinfected[i,:,:])" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "color = False\n", - "\n", - "levels = [0,5,100]\n", - "colors = plt.cm.jet(np.linspace(0,1,int(len(beta)/2)))\n", - "\n", - "n = 3\n", - "fig,ax=plt.subplots(1,1)\n", - "#ax.set_title('Beta: '+str(round(beta[j],2)))\n", - "ax.set_xlabel('Results delay [days]',size=25)\n", - "ax.set_ylabel('Tested [%]',size=25) \n", - "ax.tick_params(labelsize=25)\n", - "for j in range(int(len(beta)/2)):\n", - " if color:\n", - " cp = ax.contour(T_T,examrate*100/population,totinfected[:,2*j,:],levels,colors=colors[j]) \n", - " else:\n", - " cp = ax.contour(T_T,examrate*100/population,totinfected[:,2*j,:],levels,colors='black') #colors[j]\n", - " #cp = ax.contourf(T_T,examrate*100/population,beta) # Agregar gradiente de beta\n", - " ax.clabel(cp, inline=1, fontsize=25,fmt=str(round(beta[2*j],2)))\n", - "\n", - "if saveplot:\n", - " if color:\n", - " plt.savefig('plotContourBeta_color.pdf',dpi=100,format='pdf')\n", - " else:\n", - " plt.savefig('plotContourBeta_byn.pdf',dpi=100,format='pdf')\n", - "plt.show() \n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/Vaccination.ipynb b/research/ExamDynamics/Vaccination.ipynb deleted file mode 100644 index 340d095..0000000 --- a/research/ExamDynamics/Vaccination.ipynb +++ /dev/null @@ -1,1217 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vaccination Dynamics EDOs Study\n", - "\n", - "This jupyter notebook shows how periodic examinations affect a pandemy evolution.\n", - "\n", - "## Table of Contents:\n", - "* Model definition\n", - "* Import Libraries\n", - "* Single Simulation Study\n", - " * Parameter Settings\n", - " * SEIR plot\n", - " * Accumulated Infected\n", - " * New Daily Infected\n", - " * Ammount of exams\n", - "* Sensitivity Analysis: \n", - " * Examination Rate\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence\n", - " * Examination Period\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence\n", - " * Examination Duty\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence \n", - " * Examination Accuracy\n", - " * Peak Size\n", - " * Peak time\n", - " * Prevalence \n", - "* Multidimensional Analysis:\n", - " * Examination rate vs Examination periods\n", - " * Examination Rate vs Duty\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Definition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{equation} \n", - "\\frac{dS}{dt} = -\\beta \\alpha \\frac{S I}{N+k_{I}I+k_{R}R}\n", - "\\end{equation}\n", - "\n", - "\\begin{equation} \n", - "\\frac{dE}{dt} = \\beta \\alpha \\frac{S I}{N+k_{I}I+k_{R}R} - \\sigma E \n", - "\\end{equation}\n", - "\\begin{equation} \n", - "\\frac{dI}{dt} = \\sigma E - \\gamma I - \\lambda_{e} \\sqcap(\\omega_{e} t) P_{I}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "\\frac{dR}{dt} = \\gamma I + \\lambda_{e} \\sqcap(\\omega_{e} t) P_{I}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "S_i+E_i+I_i+R_i = N_i\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "P_{I} = \\frac{I}{N}\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "e(t) = \\lambda_{e} \\sqcap(\\omega_{e} t)\n", - "\\end{equation}\n", - "\\begin{equation} \n", - "e_{I}(t) = \\lambda_{e} \\sqcap(\\omega_{e} t)\n", - "\\end{equation}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epidemiological Parameters\n", - "* **beta:** Infection rate\n", - "* **mu:** Initial exposed obtained from the initial infected mu=E0/I0\n", - "* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported)\n", - "* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics\n", - "* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from pandasgui import show" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIR/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " #%matplotlib tk\n", - " #%matplotlib qt5\n", - " print('Linux')\n", - "elif OS == 'Windows': \n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "\n", - "from class_SEIRTQM import SEIRTQM\n", - "from Quarantine import Quarantine\n", - "from Quarantine import Exams\n", - "import Events" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 500\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "\n", - "testaccuracy = 0.9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single simulation study\n", - "We'll vaccine 1/4 th of the population." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = 20000 # Persons per day\n", - "period = 1\n", - "duty = 1\n", - "# Construction of vaccination dynamics\n", - "psi = 0# \n", - "#values = [0,5000,0]\n", - "vaccines_init = 30\n", - "vaccines_end = 80\n", - "vaccines_daily = 10000\n", - "days = [[vaccines_init,vaccines_end]]\n", - "values = [vaccines_daily]\n", - "vaccines = Events.Events(values = values, days = days)\n", - "\n", - "tot_vaccines = vaccines_daily*(vaccines_end-vaccines_init)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "t = np.arange(0,120)\n", - "f = [vaccines(i) for i in t]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(t,f,label='Vaccines per day')\n", - "plt.scatter([],[],label='Total vaccines: '+str(tot_vaccines))\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "simulation = SEIRTQM(tsim=tsim,alpha=s1.alpha,beta=beta,mu=mu,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,vac_d=vaccines)\n", - "simulation.integr_sci(0,tsim,0.01)\n", - "# No exams dynamic for reference:\n", - "simulation_reference = SEIRTQM(tsim=tsim,alpha=s1.alpha,beta=beta,mu=mu,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,vac_d=0)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIR Plot with Active infected" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.S,label='S',color = 'blue')\n", - "plt.plot(simulation.t,simulation.E,label='E',color = 'cyan')\n", - "plt.plot(simulation.t,simulation.I,label='I',color = 'red')\n", - "plt.plot(simulation.t,simulation.R,label='R',color = 'green')\n", - "plt.plot(simulation_reference.t,simulation_reference.S,label='S - No Vaccines',color = 'blue',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.E,label='E - No Vaccines',color = 'cyan',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.I,label='I - No Vaccines',color = 'red',linestyle='dashed')\n", - "plt.plot(simulation_reference.t,simulation_reference.R,label='R - No Vaccines',color = 'green',linestyle='dashed')\n", - "\n", - "plt.title('Epidemiological Plot')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimulation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvac_d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/covid19geomodeller/src/SEIR/class_SEIRTQM.py\u001b[0m in \u001b[0;36mauxvac_d\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mauxvac_d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" - ] - } - ], - "source": [ - "simulation.vac_d(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Accumulated Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_ac,label='I_ac with Vaccines')\n", - "plt.plot(simulation_reference.t,simulation_reference.I_ac,label='I_ac No Vaccines', linestyle='dashed')\n", - "plt.title('Accumulated Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot New Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_d,label='I_d with Vaccines')\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='I_d no Vaccines', linestyle='dashed')\n", - "plt.title('New Daily Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sensitivity Analysis: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vaccination Campaigns \n", - "We will simulate different vaccination campaings. We will analyze the pandemic evolution for Immunization campaings with different: \n", - "* delay in the beginning of the vac campaign\n", - "* Amount of daily vaccines \n", - "* Amount of total vaccination " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 1.5 # E0/I0 initial rate\n", - "SeroPrevalence = 1\n", - "expinfection = 0\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 1000\n", - "\n", - "I_ac0 = 100\n", - "# Kinetic Saturation: 0 for mass action mixing\n", - "k_I=0\n", - "# Immunity Shield\n", - "k_R=0\n", - "# Quarantine\n", - "s1 = Quarantine(1)\n", - "\n", - "duty = 1\n", - "examrate = list(np.arange(0.1,5,0.1)*population/100)# Persons per day\n", - "period = 1\n", - "\n", - "psi = [Exams(i,period,duty) for i in examrate] \n", - "\n", - "testaccuracy=0.9\n", - "\n", - "\n", - "examrate = 20000 # Persons per day\n", - "period = 1\n", - "duty = 1\n", - "# Construction of vaccination dynamics\n", - "psi = 0# \n", - "#values = [0,5000,0]\n", - "vaccines_init = 30\n", - "vaccines_end = 80\n", - "vaccines_daily = 5000\n", - "days = [[vaccines_init,vaccines_end]]\n", - "values = [vaccines_daily]\n", - "vaccines = [Events.Events(values = values, days = days)\n", - "\n", - "tot_vaccines = vaccines_daily*(vaccines_end-vaccines_init)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "def simulate(simulation,tsim):\n", - " simulation.integr_sci(0,tsim,0.1)\n", - " return simulation\n", - "\n", - "sims = []\n", - "for i in range(len(examrate)): \n", - " sims.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1,testaccuracy=testaccuracy))\n", - " \n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],tsim) for i in range(len(sims)))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicatros\n", - "peak = []\n", - "for i in range(len(examrate)): \n", - " peak.append(100*simulation[i].peak/population)\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " peaktime.append(simulation[i].peak_t) \n", - " \n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " prevalence.append(simulation[i].prevalence_total[-1]) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,peak,label='Infected during peak')\n", - "plt.title('Peak size (%) per examrate')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(peak)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,peaktime,label='Time when peak occurs')\n", - "plt.title('Peak time shift')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(peaktime)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(examrate,prevalence,label='% Percentage of total population')\n", - "plt.title('Prevalence at the end of the simulation')\n", - "plt.legend(loc=0)\n", - "plt.ylim(0,np.max(prevalence)*1.1)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Period\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Duty\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination Accuracy\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multidimensional Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examination rate vs Examination periods\n", - "All examination campaings will have 50% of examination time during the different time periods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 0.5\n", - "\n", - "examrate = [1000,5000,10000,20000,50000] # Persons per day\n", - "period = [1,2,5,10,15,30]\n", - "\n", - "psi = [[Exams(i,j,duty) for j in period] for i in examrate] \n", - "\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Exam campaings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = 3\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]\n", - "#plt.plot(time,exams[i][j])\n", - "\n", - "fig, axs = plt.subplots(len(examrate), len(period))\n", - "for i in range(len(examrate)):\n", - " for j in range(len(period)):\n", - " axs[i,j].plot(time,exams[i][j],label='Examination Rate:'+str(examrate[i])+' Period: '+str(period[j]))\n", - " axs[i,j].legend(loc=0)\n", - " #axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Period Analysis\n", - "\n", - "### Period analysis over a single amount of exam rate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "j = 0\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='No exams',color = 'black',linestyle='dashed')\n", - "for i in range(len(period)):\n", - " plt.plot(simulation[j][i].t,simulation[j][i].I_d,label='Examination Period: '+str(period[i]))\n", - "plt.legend(loc=0)\n", - "plt.title('New Daily Infected | '+str(examrate[j])+' exams/day')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple rates for each period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(examrate)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(period)/n), n)\n", - "for j in range(len(period)):\n", - " axs[int(j/n),j%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for i in range(len(examrate)):\n", - " axs[int(j/n),j%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination Period: '+str(period[j]))\n", - " axs[int(j/n),j%n].legend(loc=0)\n", - " axs[int(j/n),j%n].set_title('Period: '+str(period[j])+' days')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exam Analysis\n", - "\n", - "### Exam analysis over a single amount of period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "j = -1\n", - "plt.plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - "for i in range(len(examrate)):\n", - " plt.plot(simulation[i][j].t,simulation[i][j].I_d,label='Exams per day: '+str(examrate[i]))\n", - "plt.legend(loc=0)\n", - "plt.title('New Daily Infected | Campaing periods: '+str(period[j]))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple periods for each exam rate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(period)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(period)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination Period: '+str(period[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contour plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "duty = 0.5\n", - "population = 1000000\n", - "examrate = np.array([0.1,0.3,0.5,0.8,1,1.5,2,2.5,3,4,5])*population/100 # Persons per day\n", - "period = [1,5,10,20,30,40,50,60,70,80,90]\n", - "\n", - "psi = [[Exams(i,j,duty) for j in period] for i in examrate] \n", - "\n", - "time = np.arange(0,100,0.1)\n", - "exams = [[[psi[i][j](t) for t in time] for j in range(len(period))] for i in range(len(examrate))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "simulation2 = simulation\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(100*simulation2[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)):\n", - " aux.append(simulation2[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(period)): \n", - " aux.append(simulation2[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak size proportion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size Proportion')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time shift" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SeroPrevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(period,examrate*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Time period')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Examrate vs duty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "examrate = [1000,5000,10000,20000,50000] # Persons per day\n", - "period = 30\n", - "duty = [0.1,0.25,0.5,0.75,1]\n", - "psi = [[Exams(i,period,j) for j in duty] for i in examrate] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,psi = psi[i][j],beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# To show the cell's output comment the former line\n", - "\n", - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Reference simulation\n", - "simulation_reference = SEIR(tsim=tsim,alpha=s1.alpha,psi = 0,beta=beta,mu=mu,k_I=k_I,k_R=k_R,I0=I0,population=population,expinfection=0,SeroPrevFactor=1)\n", - "simulation_reference.integr_sci(0,tsim,0.01)\n", - "print('simulation finished')\n", - "\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(examrate)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(100*simulation[i][j].peak/population)\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(examrate)):\n", - " aux = []\n", - " for j in range(len(duty)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GridPlot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(duty)))\n", - "n = 3\n", - "fig, axs = plt.subplots(int(len(examrate)/n)+1, n)\n", - "for i in range(len(examrate)):\n", - " axs[int(i/n),i%n].plot(simulation_reference.t,simulation_reference.I_d,label='I_d No exams',color = 'black',linestyle='dashed')\n", - " for j in range(len(duty)):\n", - " axs[int(i/n),i%n].plot(simulation[i][j].t,simulation[i][j].I_d,label='Examination duty: '+str(duty[j]))\n", - " axs[int(i/n),i%n].legend(loc=0)\n", - " axs[int(i/n),i%n].set_title('Exam Rate: '+str(examrate[i]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(peak).transpose()[j],label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Relative Peak size (%)')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Peak Size')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(peaktime)[j],label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Peak day')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Peak time')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for j in range(len(examrate)):\n", - " plt.plot(duty,np.array(prevalence).transpose()[j]*100,label='Exam Rate: '+str(examrate[j]))\n", - "plt.legend(loc=1)\n", - "plt.ylabel('Prevalence %')\n", - "plt.xlabel('Duty')\n", - "plt.title('Duty Study: Prevalence')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contour plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak size')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak time')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Seroprevalence" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(duty,np.array(examrate)*100/population,prevalence) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Prevalence')\n", - "ax.set_xlabel('Duty')\n", - "ax.set_ylabel('ExamRate')\n", - "plt.show() " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/ExamDynamics/readme.md b/research/ExamDynamics/readme.md deleted file mode 100644 index 09f8467..0000000 --- a/research/ExamDynamics/readme.md +++ /dev/null @@ -1,72 +0,0 @@ -# Examination Dynamics Study - -This work studies how different examinations campings can affect a pandemic evolution. In order to facilitate its lecture, the work is split in different jupyter notebooks - -## Model Definition - -\begin{equation} -\frac{dS}{dt} = -\beta \alpha \frac{S I}{N+k_{I}I+k_{R}R} -\end{equation} - -\begin{equation} -\frac{dE}{dt} = \beta \alpha \frac{S I}{N+k_{I}I+k_{R}R} - \sigma E -\end{equation} -\begin{equation} -\frac{dI}{dt} = \sigma E - \gamma I - \lambda_{e} \sqcap(\omega_{e} t) P_{I} -\end{equation} -\begin{equation} -\frac{dR}{dt} = \gamma I + \lambda_{e} \sqcap(\omega_{e} t) P_{I} -\end{equation} -\begin{equation} -S_i+E_i+I_i+R_i = N_i -\end{equation} -\begin{equation} -P_{I} = \frac{I}{N} -\end{equation} -\begin{equation} -e(t) = \lambda_{e} \sqcap(\omega_{e} t) -\end{equation}_ -\begin{equation} -e_{I}(t) = \lambda_{e} \sqcap(\omega_{e} t) -\end{equation} - -## Epidemiological Parameters -* **beta:** Infection rate - -* **mu:** Initial exposed obtained from the initial infected mu=E0/I0 - -* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported) - -* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics - -* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected ) - - - -## Table of Contents: -* Single Simulation Study - * Parameter Settings - * SEIR plot - * Accumulated Infected - * New Daily Infected - * Ammount of exams -* Sensitivity Analysis: - * Examination Rate - * Peak Size - * Peak time - * Prevalence - * Examination Period - * Peak Size - * Peak time - * Prevalence - * Examination Duty - * Peak Size - * Peak time - * Prevalence - * Examination Accuracy - * Peak Size - * Peak time - * Prevalence -* Multidimensional Analysis: - * Examination rate vs Examination periods - * Examination Rate vs Duty \ No newline at end of file diff --git a/research/Modelos Bassi.ipynb b/research/Modelos Bassi.ipynb deleted file mode 100644 index 0c32c6a..0000000 --- a/research/Modelos Bassi.ipynb +++ /dev/null @@ -1,1115 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Desarrollo modelo epidemiológico - A. Bassi\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samuel/anaconda3/lib/python3.8/site-packages/rpy2/robjects/pandas2ri.py:17: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.\n", - " from pandas.core.index import Index as PandasIndex\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import toml\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "from datetime import datetime\n", - "import matplotlib.dates as mdates\n", - "from matplotlib.ticker import MaxNLocator\n", - "\n", - "import epyestim\n", - "import epyestim.covid19 as covid19\n", - "from epyestim.distributions import discretise_gamma\n", - "from datetime import timedelta\n", - "\n", - "# rpy2 imports\n", - "from rpy2.robjects.packages import importr\n", - "from rpy2.robjects import pandas2ri\n", - "import rpy2.robjects as robjects\n", - "pandas2ri.activate()\n", - "# R library\n", - "global eps\n", - "eps = importr(\"EpiEstim\")\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../')\n", - "from src2.models.cv19sim import cv19sim" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "#Pop-up plots\n", - "import platform\n", - "OS = platform.system()\n", - "if OS == 'Linux' or OS == 'Darwin':\n", - " %matplotlib tk \n", - "elif OS == 'Windows':\n", - " %matplotlib qt " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Valores iniciales\n", - "$ \\beta_e = \\beta\\frac{S}{N}$\n", - "\n", - "$ I_0 = \\frac{C_0}{\\beta_e}$\n", - "\n", - "$ E_0 = \\frac{C_0}{\\delta +\\sigma}$\n", - "\n", - "$ E_0 = \\frac{C_0}{\\delta +\\sigma}$\n", - "\n", - "$\\delta = \\frac{1}{2}\\sqrt{4\\sigma\\beta + (\\sigma-\\gamma)^2} - \\sigma - \\gamma$\n", - "\n", - "Entonces:\n", - "$ E_0 = \\frac{C_0}{\\frac{1}{2}\\sqrt{4\\sigma\\beta + (\\sigma-\\gamma)^2}- \\gamma}$\n", - "\n", - "Donde $C_0$ son los infectados nuevos diarios $(I_d)$ el día 0\n", - "### Data Real\n", - "Cuando tenemos data real tenemos valores tanto de $I_0$ como de $C_0$, por lo que para lograr una partida suave es necesario usar uno para calcular el otro, o buscar un valor promedio que minimice el error entre ambos. Ese trabajo quedará para un poco más adelante" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Definición de ecuaciones " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculo de pendientes en escala logarítmica" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [], - "source": [ - "def getdelta(y,x=None,scale='linear'):\n", - " if scale == 'linear':\n", - " y = np.log(y+0.001)\n", - " #delta = np.diff(y)\n", - " delta = np.gradient(y)\n", - " return delta\n", - "\n", - "def delta_eq(beta,S,N,sigma,gamma):\n", - " delta = 1/2*(np.sqrt(4*sigma*beta*S/N+(sigma-gamma)**2)-sigma-gamma)\n", - " return delta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R efectivo" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [], - "source": [ - "def Re_delta_calc(delta,sigma,gamma):\n", - " Ti = 1/gamma\n", - " Te = 1/sigma\n", - " Ts = Ti + Te\n", - " return 1 + delta*Ts + sigma*gamma/((sigma+gamma)**2)*(delta*Ts)**2 \n", - "\n", - "def Re_delta_a_calc(delta,sigma,gamma):\n", - " Ti = 1/gamma\n", - " Te = 1/sigma\n", - " Ts = Ti + Te\n", - " return 1 + delta*Ts\n", - "\n", - "def Re_analitico_calc(beta,gamma,S,N):\n", - " Re = beta/gamma*S/N\n", - " return Re" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Beta" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [], - "source": [ - "def beta_Re(Re,S,N,gamma):\n", - " return Re*gamma*N/S\n", - "\n", - "def beta_delta(delta,S,N,gamma,sigma):\n", - " Re = Re_delta(delta,sigma,gamma)\n", - " return Re*gamma*N/S" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generación de datos sintéticos\n", - "Trabajaremos con los infectados nuevos diarios, dado que esos son los datos que se miden en la realidad. El objetivo será tratar de reconstruir los parámetros originales. Truncaremos los datos para reducir el efecto de estabilización inicial. Partiremos con datos desde que los infectados activos superen los 100 casos. \n", - "Por ahora despreciaremos los efectos del subreporte y del ruido de medición." - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [], - "source": [ - "cfgfile = '../config_files/SEIR2.toml'\n", - "cfg = toml.load(cfgfile) # no es necesario, pero es util para ver el archivo de configuracion" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.4\n", - "I0 = 10\n", - "mu = 1\n", - "tE_I = 3\n", - "tI_R = 5\n", - "sigma = 1/tE_I\n", - "gamma = 1/tI_R" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [], - "source": [ - "sims = cv19sim(cfg,beta = beta,mu=mu,I=I0,I_d=I0*beta,tE_I = tE_I,tI_R=tI_R,t_end=250)" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [], - "source": [ - "sims.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(sims.sims[0].I_d,label='Nuevos Infectados Diarios')\n", - "plt.legend(loc=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Se consideran los datos desde que hayan 100 casos" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [], - "source": [ - "# Tiempo de los datos inicial\n", - "t0 = np.where(sims.sims[0].I >= 100)[0][0]\n", - "#t0 = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [], - "source": [ - "I_d = sims.sims[0].I_d[t0:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cálculo R analítico\n", - "$R_e = \\beta \\frac{S}{N}T_{IR}$" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [], - "source": [ - "Re_analitico = Re_analitico_calc(beta,gamma,sims.sims[0].S[t0:],sims.sims[0].N)\n", - "#Re_analitico = Re_analitico[t0:] # Correccion temporal (beta,gamma,S,N)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trabajo con la data sintética" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.scatter(range(len(I_d)),I_d,label='data')\n", - "#plt.plot(t[1:],dI_d,label='dI_d')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculo de $\\delta$" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": {}, - "outputs": [], - "source": [ - "delta = getdelta(I_d)" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "axs2.set_ylabel('Delta',color='tab:red')\n", - "axs2.plot(range(len(delta)),delta,label='delta',color='tab:red')\n", - "\n", - "axs2.tick_params(axis='y', labelcolor='tab:red')\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(I_d)),I_d,color='tab:blue',label='Infectados nuevos diarios',linestyle='solid')\n", - "axs.set_yscale('log')\n", - "#axs.plot(sim.t,sim.I,color='tab:blue',label='Activos')\n", - "axs.set_ylabel('Infectados',color='tab:blue')\n", - "axs.tick_params(axis='y', labelcolor='tab:blue')\n", - "\n", - "#axs2.set_xlim(0,200)\n", - "fig.suptitle('Delta e Id')\n", - "axs.legend(loc=1)\n", - "axs2.legend(loc=3)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tiempo Serial\n", - "Uno de los mayores desafíos es tener un tiempo serial bien calculado.\n", - "Analíticamente la definición del tiempo serial es: \n", - "$ T_s = T_{EI} + T_{IR} $ \n", - "Donde $T_{EI}$ y $T_{IR}$ son las medias de las distribuciones de tiempos de incubación e infecciosidad respectivamente. \n", - "Para este análisis trabajaremos primero con los tiempos seriales de la misma simulación." - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [], - "source": [ - "Ts = tE_I + tI_R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cálculo del R efectivo a partir de $\\delta$" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [], - "source": [ - "# R delta\n", - "Re_delta = []\n", - "for i in range(len(delta)):\n", - " Re_delta.append(1 + delta[i]*Ts + sigma*gamma/(sigma + gamma)*(delta[i]*Ts)**2)\n", - "Re_delta=np.array(Re_delta)\n", - "\n", - "# R delta aprox\n", - "Re_delta_a = 1 + delta*Ts" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], - "source": [ - "# R delta\n", - "Re_delta = Re_delta_calc(delta,sigma,gamma)\n", - "# R delta aprox\n", - "Re_delta_a = Re_delta_a_calc(delta,sigma,gamma)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Estudios de Re: Comparación R analítico, R delta y R delta aprox" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(range(len(Re_analitico[1:])),Re_analitico[1:],label='Analitico')\n", - "plt.plot(range(len(Re_delta)),Re_delta,label='delta')\n", - "plt.plot(range(len(Re_delta_a)),Re_delta_a,label='delta aprox')\n", - "plt.axhline(1,color='grey',linestyle='dashed')\n", - "plt.title('Estudios Re')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "\n", - "axs2.set_ylabel('Re',color='tab:red')\n", - "axs2.plot(range(len(Re_analitico[1:])),Re_analitico[1:],label='Analitico')\n", - "axs2.plot(range(len(Re_delta)),Re_delta,label='delta')\n", - "axs2.plot(range(len(Re_delta_a)),Re_delta_a,label='delta aprox')\n", - "axs2.axhline(1,color='grey',linestyle='dashed')\n", - "axs2.tick_params(axis='y', labelcolor='tab:red')\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(I_d)),I_d,color='tab:blue',label='Diarios',linestyle='solid')\n", - "axs.set_yscale('log')\n", - "axs2.axvline(np.where(I_d==max(I_d))[0][0],color='grey',linestyle='dashed')\n", - "#axs.plot(sim.t,sim.I,color='tab:blue',label='Activos')\n", - "axs.set_ylabel('Infectados',color='tab:blue')\n", - "axs.tick_params(axis='y', labelcolor='tab:blue')\n", - "\n", - "\n", - "#axs2.set_xlim(0,200)\n", - "fig.suptitle('Re e Id')\n", - "axs.legend(loc=1)\n", - "axs2.legend(loc=3)\n", - "fig.show()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Error R" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error calculo Delta: 0.0006412931373282819\n", - "Error calculo Delta aprox: 0.015522248729671837\n" - ] - } - ], - "source": [ - "P = 1\n", - "err_delta = ((P*Re_delta - P*Re_analitico[0:])**2).mean() \n", - "err_delta_a = ((P*Re_delta_a - P*Re_analitico[0:])**2).mean() \n", - "print('Error calculo Delta: '+str(err_delta))\n", - "print('Error calculo Delta aprox: '+str(err_delta_a))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculo de beta\n", - "$ \\beta(t) = R_e \\frac{N}{S(t)} \\gamma $" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [], - "source": [ - "beta_Re_delta = gamma*Re_delta*sims.sims[0].N/sims.sims[0].S[t0:]\n", - "beta_Re_delta_a = gamma*Re_delta_a*sims.sims[0].N/sims.sims[0].S[t0:]\n", - "beta_Re_analitico = gamma*Re_analitico*sims.sims[0].N/sims.sims[0].S[t0:]" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "92" - ] - }, - "execution_count": 203, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 204, - "metadata": {}, - "outputs": [], - "source": [ - "plt.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='grey',linestyle='dashed')\n", - "plt.plot(range(len(beta_Re_delta)),beta_Re_delta,label='Delta')\n", - "plt.plot(range(len(beta_Re_delta_a)),beta_Re_delta_a,label='Delta aprox')\n", - "plt.plot(range(len(beta_Re_analitico)),beta_Re_analitico,label='beta analitico')\n", - "plt.axhline(beta,label='Real',linestyle='dashed')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs2 = plt.subplots(figsize=(10,6),linewidth=5,edgecolor='black',facecolor=\"white\")\n", - "\n", - "\n", - "\n", - "axs2.plot(range(len(Re_analitico[1:])),Re_analitico[1:],color = 'tab:red',label='Re analitico')\n", - "axs2.plot(range(len(Re_delta)),Re_delta,label='Re delta',color = 'tab:red',linestyle='dashed')\n", - "#axs2.plot(range(len(Re_delta_a)),Re_delta_a,label='delta aprox')\n", - "axs2.axhline(1,color='red',linestyle='dotted',alpha=0.5)\n", - "\n", - "\n", - "axs = axs2.twinx()\n", - "axs.plot(range(len(beta_Re_analitico)),beta_Re_analitico,label='Beta analitico',color = 'tab:blue')\n", - "axs.plot(range(len(beta_Re_delta)),beta_Re_delta,label='Beta delta',color = 'tab:blue',linestyle='dashed')\n", - "\n", - "axs2.fill_between(range(len(Re_analitico[1:])), Re_analitico[1:], Re_delta[0:-1],color='tab:grey', alpha=0.4)\n", - "\n", - "axs.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted',label='Min beta')\n", - "\n", - "axs2.tick_params(axis='y', labelcolor='tab:red',size=10)\n", - "axs.tick_params(axis='y', labelcolor='tab:blue',size=10)\n", - "\n", - "axs2.set_ylabel('Re',color='tab:red',size=12)\n", - "axs.set_ylabel('Beta',color='tab:blue',size=12)\n", - "\n", - "axs.set_ylim(0.3,0.42)\n", - "#axs2.set_ylim(0,2.5)\n", - "\n", - "fig.suptitle('Re y beta')\n", - "\n", - "lines, labels = axs.get_legend_handles_labels()\n", - "lines2, labels2 = axs2.get_legend_handles_labels()\n", - "axs2.legend(lines + lines2, labels + labels2, loc=[0.05,0.1])\n", - "#axs.legend(loc=[0.05,0.05])\n", - "#axs2.legend(loc=[0.05,0.17])\n", - "\n", - "ax_zoom = axs2.inset_axes([.67,.24,.3,.4])\n", - "\n", - "init = 80\n", - "end = 120\n", - "ax_zoom.plot(np.arange(init,end),Re_analitico[init:end],color = 'tab:red',linestyle='solid')\n", - "ax_zoom.plot(np.arange(init,end),Re_delta[init:end],color = 'tab:red',linestyle='dashed')\n", - "ax_zoom.fill_between(np.arange(init,end),Re_analitico[init:end],Re_delta[init:end],color='tab:grey', alpha=0.2)\n", - "ax_zoom.axvline(np.where(beta_Re_delta==np.min(beta_Re_delta))[0][0],color='tab:blue',alpha=0.5,linestyle='dotted')\n", - "\n", - "ax_zoom.set_title('Re difference',size=10)\n", - "#ax_zoom.set_xticks([])\n", - "#ax_zoom.set_yticks([])\n", - "axs2.indicate_inset_zoom(ax_zoom)\n", - "\n", - "fig.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9025310006997956" - ] - }, - "execution_count": 219, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(beta_Re_delta)/beta" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Análisis de sensibilidad de tiempo serial\n", - "Para hacer este estudio utilizaremos los mismos datos sintéticos y calcularemos el Re y beta utilizando distintos gammas y sigmas para ver como crece el error a medida que nos alejamos de los valores correctos" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": {}, - "outputs": [], - "source": [ - "tE_I_s = np.arange(2,6,0.1)\n", - "tI_R_s = np.arange(2,6,0.1)\n", - "Ts_s = np.array([[i+j for i in tE_I_s] for j in tI_R_s])" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "sigma_s = 1/tE_I_s\n", - "gamma_s = 1/tI_R_s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### MSE y RMSE Re vs Re_analitico\n", - "Re calculado para las distintas combinaciones de $\\sigma$ y $\\gamma$" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "metadata": {}, - "outputs": [], - "source": [ - "P = 1\n", - "# R delta\n", - "Re_delta_s_a = np.zeros(np.shape(Ts_s), dtype=object) \n", - "Re_delta_s = np.zeros(np.shape(Ts_s), dtype=object)\n", - "error_s = np.zeros(np.shape(Ts_s))\n", - "error_s_a = np.zeros(np.shape(Ts_s))\n", - "for i in range(len(sigma_s)):\n", - " for j in range(len(gamma_s)):\n", - " Re_delta_s[i,j] = Re_delta_calc(delta,sigma_s[i],gamma_s[j])\n", - " Re_delta_s_a[i,j] = Re_delta_a_calc(delta,sigma_s[i],gamma_s[j])\n", - "\n", - " error_s[i,j] = ((P*Re_delta_s[i,j] - P*Re_analitico[0:])**2).mean()\n", - " error_s_a[i,j] = ((P*Re_delta_s_a[i,j] - P*Re_analitico[0:])**2).mean() " - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err =error_s\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('MSE vs Re analítico')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err = np.sqrt(error_s)\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('RMSE vs Re analítico')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RMSE Re vs Re_delta\n", - "Re calculado para las distintas combinaciones de $\\sigma$ y $\\gamma$ vs el Re calculado con $\\delta$ para los valores con los que se hizo la simulación original" - ] - }, - { - "cell_type": "code", - "execution_count": 231, - "metadata": {}, - "outputs": [], - "source": [ - "P = 1\n", - "# R delta\n", - "Re_delta_s_a = np.zeros(np.shape(Ts_s), dtype=object) \n", - "Re_delta_s = np.zeros(np.shape(Ts_s), dtype=object)\n", - "error_s_d = np.zeros(np.shape(Ts_s))\n", - "error_s_a_d = np.zeros(np.shape(Ts_s))\n", - "for i in range(len(sigma_s)):\n", - " for j in range(len(gamma_s)):\n", - " Re_delta_s[i,j] = Re_delta_calc(delta,sigma_s[i],gamma_s[j])\n", - " Re_delta_s_a[i,j] = Re_delta_a_calc(delta,sigma_s[i],gamma_s[j]) \n", - "\n", - " error_s_d[i,j] = ((P*Re_delta_s[i,j] - P*Re_delta[:])**2).mean()\n", - " error_s_a_d[i,j] = ((P*Re_delta_s_a[i,j] - P*Re_delta[:])**2).mean()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err =error_s_d\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('MSE vs Re delta')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "err = np.sqrt(error_s_d)\n", - "err_max = np.max(err) \n", - "levels = np.arange(0,err_max*1.1,0.001)\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(tE_I_s,tI_R_s,err,levels)\n", - "\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,err_max*0.25,err_max],colors='white',linestyles='dashed')\n", - "cp2 = ax.contour(tE_I_s,tI_R_s,err,[0,np.min(err)+0.0000001,err_max],colors='white',linestyles='dotted')\n", - "\n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('RMSE vs Re delta')\n", - "ax.set_xlabel('Incubation period')\n", - "ax.set_ylabel('Infectious period')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error cálculo Re para distintos R0" - ] - }, - { - "cell_type": "code", - "execution_count": 234, - "metadata": {}, - "outputs": [], - "source": [ - "beta2 = list(np.arange(0.2,0.6,0.01))\n", - "R0 = np.array(beta2)*tI_R" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "metadata": {}, - "outputs": [], - "source": [ - "sims2 = cv19sim(cfg,beta = beta2,mu=mu,I=I0,I_d=I0*beta,tE_I = tE_I,tI_R=tI_R,t_end=250)\n", - "sims2.integrate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculo de R por delta" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [], - "source": [ - "t0=1" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "metadata": {}, - "outputs": [], - "source": [ - "Re_analitico2 = [beta2[i]*sims2.sims[i].S[t0:-1]/sims2.sims[i].N*tI_R for i in range(len(beta2))]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "metadata": {}, - "outputs": [], - "source": [ - "delta2 = [getdelta(sims2.sims[i].I_d[t0:]) for i in range(len(beta2))]" - ] - }, - { - "cell_type": "code", - "execution_count": 242, - "metadata": {}, - "outputs": [], - "source": [ - "# R delta\n", - "Re_delta2 = []\n", - "for j in delta2:\n", - " Re_delta2.append(Re_delta_calc(j,sigma,gamma))\n", - "\n", - "Re_delta2=np.array(Re_delta2)\n", - "\n", - "# R delta aprox\n", - "#Re_delta_a = 1 + delta*Ts" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [], - "source": [ - "# Error\n", - "err2 = []\n", - "for i in range(len(beta2)):\n", - " err2.append(np.sqrt((Re_analitico2[i][0:100]-Re_delta2[i][0:100]).mean()))" - ] - }, - { - "cell_type": "code", - "execution_count": 244, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 244, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(R0,err2)\n", - "plt.title('RMSE R vs R0')\n", - "plt.xlabel('R0')\n", - "plt.ylabel('RMSE')\n", - "plt.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Error cálculo Re para distintos R0 y Ts" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "beta3 = list(np.arange(0.2,0.6,0.01))\n", - "R0 = np.array(beta2)*tI_R\n", - "Ts3 = np.arange(1,6,0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "sims3 = []\n", - "for i in Ts3:\n", - " sims3.append(cv19sim(cfg,beta = beta2,mu=mu,I=I0,I_d=I0*beta,tE_I = i/2,tI_R=i/2,t_end=250))\n", - "\n", - "for i in range(len(Ts3)):\n", - " sims3[i].integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "sims = np.array([i.sims for i in sims3])" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "t0 = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "Re_analitico3 = [[sims[i,j].beta(0)*sims[i,j].S[t0:-1]/sims[i,j].N*sims[i,j].tI_R(0) for j in range(len(beta3))]for i in range(len(Ts3))]" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "delta3 = [[getdelta(sims[i,j].I_d[t0:-1]) for j in range(len(beta3))]for i in range(len(Ts3))]" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "i = 40\n", - "j = 39\n", - "aux = delta3[i][j]" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(sims[i,j].I_d)" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(range(len(aux)),aux)" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [], - "source": [ - "# R delta\n", - "Re_delta3 = []\n", - "for j in delta2:\n", - " aux = []\n", - " for i in range(len(j)):\n", - " aux.append(1 + j[i]*Ts + sigma*gamma/(sigma + gamma)*(j[i]*Ts)**2)\n", - " Re_delta2.append(aux)\n", - "Re_delta2=np.array(Re_delta2)\n", - "\n", - "# R delta aprox\n", - "#Re_delta_a = 1 + delta*Ts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# To Do:\n", - "[ ] Comprobar el orden de las variables en los arreglos sigma-gamma \n", - "[ ] Verificar el calculo de R porque no me están calzando bien los R con las magnitudes de los otros.\n", - "[ ] Grid-plot \n", - "\n", - "\n", - "[ ] Estudiar cuanto aumenta el error a medida que cambian el tiempo serial, gamma y sigma \n", - "\n", - "[x] Calcular Beta desde el R \n", - "[ ] Reconstruir las curvas con los parámetros obtenidos \n", - "[ ] Tratar de compatibilizar los cálculos de R de Felipe y de la nueva librería, y compararlos con el cálculo analítico \n", - "\n", - "[ ] Revisar calculo del error entre los cálculos de R para el contourplot. \n", - "[ ] Revisar calculo del error entre los cálculos de R. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/README.md b/research/README.md deleted file mode 100644 index cb7e102..0000000 --- a/research/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Research repository - -Here we find the scripts built for our research projects, mostly using our platform. - -**This folder will be moved to an independent repository that uses covid19geomodeller as a subrepository.** \ No newline at end of file diff --git a/research/R_calculation.ipynb b/research/R_calculation.ipynb deleted file mode 100644 index b5479d1..0000000 --- a/research/R_calculation.ipynb +++ /dev/null @@ -1,1598 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samuel/anaconda3/lib/python3.8/site-packages/rpy2/robjects/pandas2ri.py:17: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.\n", - " from pandas.core.index import Index as PandasIndex\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from scipy.interpolate import interp1d\n", - "from datetime import timedelta\n", - "import datetime as dtime\n", - "import json\n", - "import requests\n", - "import os\n", - "# rpy2 imports\n", - "from rpy2.robjects.packages import importr\n", - "from rpy2.robjects import pandas2ri\n", - "import rpy2.robjects as robjects\n", - "pandas2ri.activate()\n", - "#plot imports\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib import dates as mdates\n", - "import seaborn as sns\n", - "import matplotlib.image as mpimg\n", - "from scipy import stats as sps\n", - "# R library\n", - "global eps\n", - "eps = importr(\"EpiEstim\")\n", - "\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "from importdata import ImportData\n", - "from class_SEIRQ import SEIR\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Instantaneous Reproduction Number\n", - "https://www.rdocumentation.org/packages/EpiEstim/versions/2.2-3/topics/estimate_R\n", - "\n", - "\n", - "**Important, rpy2 2.9.4 version needed**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Real Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importing General Data\n", - "Importing Population\n", - "Importing Active infected\n", - "Importing Accumulated Infected\n", - "Importing Daily Infected\n", - "Importing ICU Beds Data from Minciencia\n", - "Importing Deaths by DEIS\n", - "Importing Active Infected by Minciencia\n", - "Done\n" - ] - } - ], - "source": [ - "initdate = datetime(2020,5,15)\n", - "RM = ImportData(tstate ='13',initdate = initdate)\n", - "RM.importdata()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame({\"dates\":list(RM.I_d_r),\"cases\":RM.I_d_r_tr})\n", - "rdf = pandas2ri.py2ri(df)\n", - "r = robjects.r\n", - "window = 5 # always 5, test\n", - "start = r.seq(2, df.values.shape[0]-window)\n", - "end = r.seq( 2+ window,df.values.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "results = eps.estimate_R(rdf[1], method=\"parametric_si\", config = eps.make_config(t_start = start,t_end = end,mean_si = 5,std_si = 2))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "results2 = dict(results.items())\n", - "rhat = pandas2ri.ri2py(results2[\"R\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " t_start t_end Mean(R) Std(R) Quantile.0.025(R) Quantile.0.05(R) \\\n", - "0 2.0 7.0 7.809289 1.664946 4.894040 5.286796 \n", - "1 3.0 8.0 5.544378 1.047789 3.684199 3.940595 \n", - "2 4.0 9.0 4.163901 0.714103 2.883622 3.062931 \n", - "3 5.0 10.0 3.292244 0.520549 2.352027 2.485293 \n", - "4 6.0 11.0 2.728476 0.402292 1.997588 2.102160 \n", - ".. ... ... ... ... ... ... \n", - "499 501.0 506.0 1.010318 0.018397 0.974579 0.980249 \n", - "500 502.0 507.0 1.010297 0.018378 0.974594 0.980259 \n", - "501 503.0 508.0 1.010276 0.018360 0.974609 0.980268 \n", - "502 504.0 509.0 1.010256 0.018341 0.974624 0.980278 \n", - "503 505.0 510.0 1.010235 0.018323 0.974640 0.980287 \n", - "\n", - " Quantile.0.25(R) Median(R) Quantile.0.75(R) Quantile.0.95(R) \\\n", - "0 6.631352 7.691290 8.858732 10.734380 \n", - "1 4.806382 5.478515 6.210636 7.372867 \n", - "2 3.662628 4.123150 4.620781 5.403896 \n", - "3 2.927813 3.264850 3.626829 4.192651 \n", - "4 2.447451 2.708730 2.987987 3.422155 \n", - ".. ... ... ... ... \n", - "499 0.997849 1.010206 1.022664 1.040767 \n", - "500 0.997841 1.010185 1.022631 1.040715 \n", - "501 0.997833 1.010165 1.022598 1.040664 \n", - "502 0.997825 1.010145 1.022565 1.040612 \n", - "503 0.997817 1.010125 1.022533 1.040561 \n", - "\n", - " Quantile.0.975(R) \n", - "0 11.394722 \n", - "1 7.778680 \n", - "2 5.675675 \n", - "3 4.388091 \n", - "4 3.571548 \n", - ".. ... \n", - "499 1.046691 \n", - "500 1.046633 \n", - "501 1.046575 \n", - "502 1.046518 \n", - "503 1.046461 \n", - "\n", - "[504 rows x 11 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rhat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sim = SEIR(tsim=500,beta=0.15)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Import scikits-odes\n" - ] - } - ], - "source": [ - "sim.integr()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame({\"dates\":sim.t,\"I\":sim.I_d})\n", - "rdf = pandas2ri.py2ri(df)\n", - "r = robjects.r\n", - "window = 5 # always 5, test\n", - "start = r.seq(2, df.values.shape[0]-window)\n", - "end = r.seq( 2+ window,df.values.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = eps.estimate_R(rdf[1], method=\"parametric_si\", config = eps.make_config(t_start = start,t_end = end,mean_si = 5,std_si = 2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results2 = dict(results.items())\n", - "rhat = pandas2ri.ri2py(results2[\"R\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rhat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import endpoint data" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = 'http://192.168.2.223:5006/getEffectiveReproductionAllComunas'" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "request = requests.get(endpoint)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "R_eff = pd.DataFrame(request.json()['data'])\n", - "R_eff_dates = pd.DataFrame(request.json()['dates'])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"R_eff_dates" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "data = R_eff" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "tstate = '13'" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - " if len(tstate) == 2:\n", - " R_eff = np.array(data.filter(regex='^'+tstate,axis=1).sum(axis=1))\n", - " elif len(tstate) > 2:\n", - " R_eff = np.array(data[tstate])" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([list([3.5368909506998163, 2.627804793936648, 2.070233986954593, 1.7188233451627748, 1.473936229216061, 1.2995565999288903, 1.1568748515004013, 1.0671986908547952, 1.0165103857196347, 0.987658123986941, 0.9816676387536566, 0.9837695969368822, 0.9977773004024986, 1.0036046371416674, 1.0161954307509653, 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"execution_count": 97, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "mean [2.7021522499025052, 2.004760155332217, 1.6658...\n", - "quantile0025 [2.5247528432498756, 1.8751974460078507, 1.561...\n", - "quantile0975 [2.885489331462001, 2.1385895186271964, 1.7736...\n", - "Name: 13, dtype: object" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datastate['13']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SEIR-RBM.ipynb b/research/SEIR-RBM.ipynb deleted file mode 100644 index f1fcdd8..0000000 --- a/research/SEIR-RBM.ipynb +++ /dev/null @@ -1,319 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compartimental Model Simulator\n", - "\n", - "**Created by:** Samuel Ropert \n", - "**Creation date:** 04/08/2020 \n", - "**Institution:** Computational Biology Lab - Fundación Ciencia y Vida, Chile \n", - "\n", - "## SEIR\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "\n", - "from class_SEIR2 import SEIR\n", - "from Quarantine import Quarantine" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epidemiological Parameters\n", - "* **beta:** Infection rate\n", - "* **mu:** Initial exposed obtained from the initial infected mu=E0/I0\n", - "* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported)\n", - "* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics\n", - "* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.2 # Contagion rate\n", - "mu = 0 # E0/I0 initial rate\n", - "sigma = 0.2\n", - "gamma = 0.1\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "# Population\n", - "population = 1000000\n", - "# Initial Active Infected \n", - "I0 = 100\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quarantines\n", - "\n", - "Quarantine object constructor:\n", - " \n", - " \n", - " ```Quarantine(rem_mov,max_mov=0.85,qp=0,iqt=0,fqt=1000,movfunct = 'once')```\n", - " \n", - " * rem_mov: Remanent mobility during Quarantine\n", - " * max_mov: Mobility during non quarantine periods\n", - " * qp: Quarantine period (for dynamic quarantines)\n", - " * iqt: Initial quarantine time\n", - " * fqt: Final quarantine time\n", - " * movfunct: Mobility function \n", - "\n", - "Mobility function types:\n", - " * once: Total quarantine between iqt and fqt\n", - " * square: Periodic quaratine with qp period" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# No quarantine\n", - "s1 = Quarantine(1)\n", - "quarantine = s1.alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation = SEIR(tsim=tsim,alpha=quarantine,beta=beta,sigma=sigma,gamma=gamma,mu=mu,I0=I0,population=population,expinfection=0)\n", - "simulation.integr_sci(0,tsim,0.01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plots\n", - "This libraries have predefined plot functions which plot the main epidemiological variables.\n", - "Each function has the following optional arguments:\n", - "* days [int] Number of days to display\n", - "* showparams [bool] Display simulation parameters \n", - "* ylim [int] Limit the vertical axis\n", - "* norm [int/float] Normalize the results\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SEIR Plot with Active infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.S,label='S',color = 'blue')\n", - "plt.plot(simulation.t,simulation.E,label='E',color = 'cyan')\n", - "plt.plot(simulation.t,simulation.I,label='I',color = 'red')\n", - "plt.plot(simulation.t,simulation.R,label='R',color = 'green')\n", - "plt.title('Epidemiological Plot')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Active Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I,label='Active infected')\n", - "plt.title('Active infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Accumulated Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_ac,label='Acc Infected')\n", - "plt.title('Accumulated Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot New Daily Infected" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(simulation.t,simulation.I_d)\n", - "plt.title('New Daily Infected')\n", - "#plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Variables:\n", - "The simulation object contains several methods and variables with its results. the following are the main epidemiological variables. Use them to compare with the RBM simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Susceptibles\n", - "simulation.S\n", - "# Exposed\n", - "simulation.E\n", - "# Infected\n", - "simulation.I\n", - "# Recovered\n", - "simulation.R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Peak Study" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Peak Value\n", - "simulation.peak" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Peak day counted from the simulation beginning\n", - "simulation.peak_t" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SEIR-Scaling.ipynb b/research/SEIR-Scaling.ipynb deleted file mode 100644 index 5370340..0000000 --- a/research/SEIR-Scaling.ipynb +++ /dev/null @@ -1,321 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIR Scaling Study\n", - "Series of simulation to study how the virus behave at different scales. We will study how it does respond to different initial conditions, and to different total population. We will study Seroprevalence, Peak size and peak date. \n", - "\n", - "## Results:\n", - "From this simulations we can conclude that the SEIR models are free of scale, their behavior depends only in the ratio of initial infected to the total population." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "\n", - "from datetime import datetime\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "import pandas as pd\n", - "from time import time\n", - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "\n", - "from class_SEIR2 import SEIR\n", - "from Quarantine import Quarantine" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epidemiological Parameters\n", - "* **beta:** Infection rate\n", - "* **mu:** Initial exposed obtained from the initial infected mu=E0/I0\n", - "* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported)\n", - "* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics\n", - "* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.15 # Contagion rate\n", - "mu = 0 # E0/I0 initial rate\n", - "sigma = 0.2\n", - "gamma = 0.1\n", - "\n", - "\n", - "# Simulation time\n", - "tsim = 1000\n", - "\n", - "# No quarantine\n", - "s1 = Quarantine(1)\n", - "quarantine = s1.alpha" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Population Conditions\n", - "population = [1000,10000,100000,1000000]\n", - "# Relative Initial Active Infected \n", - "I0 = [0.01,0.05,0.1,0.5,1,5,10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.9s\n", - "[Parallel(n_jobs=12)]: Done 2 out of 7 | elapsed: 0.9s remaining: 2.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 7 | elapsed: 0.9s remaining: 1.2s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 7 | elapsed: 0.9s remaining: 0.7s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 7 | elapsed: 0.9s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 1.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 1.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0412s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 7 | elapsed: 0.1s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 7 | elapsed: 0.6s remaining: 0.8s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 7 | elapsed: 0.6s remaining: 0.4s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 7 | elapsed: 0.6s remaining: 0.2s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 0.6s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 0.6s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0297s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 7 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 7 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 7 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 7 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 0.1s finished\n", - "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", - "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.0s\n", - "[Parallel(n_jobs=12)]: Batch computation too fast (0.0222s.) Setting batch_size=2.\n", - "[Parallel(n_jobs=12)]: Done 2 out of 7 | elapsed: 0.0s remaining: 0.1s\n", - "[Parallel(n_jobs=12)]: Done 3 out of 7 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 4 out of 7 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 5 out of 7 | elapsed: 0.0s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=12)]: Done 7 out of 7 | elapsed: 0.1s finished\n", - "ready\n" - ] - } - ], - "source": [ - "# For parallel simulation\n", - "def simulate(simulation,j,tsim):\n", - " simulation[j].integr_sci(0,tsim,0.1)\n", - " return simulation[j]\n", - "\n", - "# Define Simulations\n", - "sims = []\n", - "for i in range(len(population)):\n", - " aux = []\n", - " for j in range(len(I0)):\n", - " aux.append(SEIR(tsim=tsim,alpha=s1.alpha,beta=beta,mu=mu,I0=I0[j]*population[i],population=population[i],expinfection=0,SeroPrevFactor=1))\n", - " sims.append(aux)\n", - "# Run simulation\n", - "num_cores = multiprocessing.cpu_count()\n", - "simulation = []\n", - "for i in range(len(population)):\n", - " simulation.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sims[i],j,tsim) for j in range(len(sims[i]))))\n", - "\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate indicators\n", - "peak = []\n", - "for i in range(len(population)):\n", - " aux = []\n", - " for j in range(len(I0)):\n", - " aux.append(100*simulation[i][j].peak/population[i])\n", - " peak.append(aux)\n", - "\n", - " \n", - "peaktime = []\n", - "for i in range(len(population)):\n", - " aux = []\n", - " for j in range(len(I0)):\n", - " aux.append(simulation[i][j].peak_t)\n", - " peaktime.append(aux)\n", - "\n", - "prevalence = []\n", - "for i in range(len(population)):\n", - " aux = []\n", - " for j in range(len(I0)): \n", - " aux.append(simulation[i][j].prevalence_total[-1]) \n", - " prevalence.append(aux)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak Size Proportion" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(I0,population,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size Proportion')\n", - "ax.set_xlabel('Initial Active Infected')\n", - "ax.set_ylabel('Total Population')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Peak time" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(I0,population,peaktime) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Time')\n", - "ax.set_xlabel('Initial Active Infected')\n", - "ax.set_ylabel('Total Population')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SeroPrevalence" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(I0,population,prevalence) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Prevalence')\n", - "ax.set_xlabel('Initial Active Infected')\n", - "ax.set_ylabel('Total Population')\n", - "plt.show() " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SEIRHVD_analysis.ipynb b/research/SEIRHVD_analysis.ipynb deleted file mode 100644 index 19757f8..0000000 --- a/research/SEIRHVD_analysis.ipynb +++ /dev/null @@ -1,498 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRHVD Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Util libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime\n", - "# Adding lib paths\n", - "import sys\n", - "sys.path.insert(1, '../src2/models/')\n", - "sys.path.insert(1, '../src2/utils/')\n", - "sys.path.insert(1, '../src2/data/')\n", - "\n", - "# cv19 libraries\n", - "from SEIRHVD import SEIRHVD \n", - "from SEIR import SEIR \n", - "import cv19functions\n", - "from cv19data import ImportData\n", - "\n", - "#sys.path.insert(1, '../src2/data/')\n", - "#sys.path.insert(1, '../src2/utils/')\n", - "#import cv19functions\n", - "#import cv19data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "# For pop-up plots execute this code (optional)\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "elif OS == 'Darwin':\n", - " %matplotlib tk\n", - " print('Mac (Funciona?)')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# Input configuration file\n", - "config = 'cfg/SEIRHVD_SEIR.toml'\n", - "# Build simulation object\n", - "model = SEIRHVD(config = config)\n", - "# Simulate (solve ODE)\n", - "model.integrate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vaccines" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simple SEIR with vaccines" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Vaccine campaign\n", - "Psi = cv19functions.events(values=[0,20000,0],days=[[0,40],[40,60],[60,500]])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Input configuration file\n", - "config = 'cfg/SEIRHVD_SEIR.toml'\n", - "# Build simulation object\n", - "model_vac = SEIRHVD(config = config,Psi=Psi,v = 1.0)\n", - "# Simulate (solve ODE)\n", - "model_vac.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matplotlib\n", - "\n", - "plt.plot(model_vac.t,model_vac.I,label='I with vaccines')\n", - "plt.plot(model.t,model.I,label='I no vaccines')\n", - "plt.plot(model_vac.t,model_vac.Iv,label='Vaccinated infected')\n", - "plt.plot(np.arange(model_vac.tsim),[Psi(i) for i in np.arange(model_vac.tsim)])\n", - "plt.xlim(0,300)\n", - "plt.legend(loc=0)\n", - "plt.title('SEIRV')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vaccines effectivity" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "v = list(np.arange(0,1.1,0.1))\n", - "model_vac2 = [SEIRHVD(config = config,Psi=Psi,v = i) for i in v]\n", - "for i in model_vac2:\n", - " i.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(v)))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "for model_vac in model_vac2:\n", - " #plt.plot(model_vac.t,model_vac.I,label='I with vaccines')\n", - " \"{:#.2g}\".format(i)\n", - " plt.plot(model_vac.t,model_vac.I,label='I eff:'+\"{:#.1g}\".format(model_vac.v(0)),color=colors[i])\n", - " #plt.plot(model_vac.t,model_vac.Iv,label='Iv eff:'+\"{:#.1g}\".format(model_vac.v(0)),color=colors[i],linestyle='dashed')\n", - " i+=1\n", - "plt.plot(np.arange(model_vac.tsim),[Psi(i) for i in np.arange(model_vac.tsim)])\n", - "plt.legend(loc=0)\n", - "\n", - "plt.xlim(0,300)\n", - "plt.title('Vaccines effectivity')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hospitalization" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Input configuration file\n", - "config = 'cfg/SEIRHVD.toml'\n", - "# Build simulation object\n", - "model1 = SEIRHVD(config = config, H_cap=4000)\n", - "model2 = SEIRHVD(config = config, H_cap=3500,Psi=Psi)\n", - "# Simulate (solve ODE)\n", - "model1.integrate()\n", - "model2.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matplotlib\n", - "#plt.plot(model.t,model.S,label='S')\n", - "#plt.plot(model.t,model.E,label='E')\n", - "plt.plot(model1.t,model1.I,label='I')\n", - "plt.plot(model1.t,model1.Im,label='Im')\n", - "plt.plot(model1.t,model1.Icr,label='Icr')\n", - "#plt.plot(model.t,model.R,label='R')\n", - "plt.xlim(0,300)\n", - "plt.legend(loc=0)\n", - "plt.title('SEIR from SEIRHVD lib')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matplotlib\n", - "plt.plot(model1.t,model1.H,color='tab:red',label='H 1')\n", - "plt.plot(model1.t,model1.D,color='tab:red',linestyle='dotted',label='D 1')\n", - "plt.plot(model1.t,[model1.H_cap]*len(model.t),color='tab:red',linestyle='dashed',label='H_cap 1')\n", - "\n", - "plt.plot(model2.t,model2.H,color='tab:blue',label='H 2')\n", - "plt.plot(model2.t,model2.D,color='tab:blue',linestyle='dotted',label='D 2')\n", - "plt.plot(model2.t,[model2.H_cap]*len(model.t),color='tab:blue',linestyle='dashed',label='H_cap 2')\n", - "#plt.xlim(0,300)\n", - "plt.plot(np.arange(model_vac.tsim),[Psi(i) for i in np.arange(model_vac.tsim)])\n", - "plt.legend(loc=0)\n", - "plt.title('ICU capacity and deaths')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vaccines amount, effectivity and deaths" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [], - "source": [ - "eff = list(np.arange(0,1.1,0.1))\n", - "amount = np.linspace(0,30000,31)" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "# Vaccine campaign\n", - "Psi = [cv19functions.Events(values=[0,i,0],days=[[0,40],[40,60],[60,500]]) for i in amount]" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Input configuration file\n", - "config = 'cfg/SEIRHVD_SEIR.toml'\n", - "# Build simulation object\n", - "models = []\n", - "for i in eff:\n", - " aux = []\n", - " for j in Psi:\n", - " a = SEIRHVD(config = config,Psi=j,v = i)\n", - " a.integrate()\n", - " aux.append(a)\n", - " models.append(aux)\n", - " # Simulate (solve ODE)\n", - "#model_vac.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "a = SEIRHVD(config=config).integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matplotlib\n", - "\n", - "plt.plot(model_vac.t,model_vac.I,label='I with vaccines')\n", - "plt.plot(model.t,model.I,label='I no vaccines')\n", - "plt.plot(model_vac.t,model_vac.Iv,label='Vaccinated infected')\n", - "plt.plot(np.arange(model_vac.tsim),[Psi(i) for i in np.arange(model_vac.tsim)])\n", - "plt.xlim(0,300)\n", - "plt.legend(loc=0)\n", - "plt.title('SEIRV')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "model_vac2 = [SEIRHVD(config = config,Psi=Psi,v = i) for i in v]\n", - "for i in model_vac2:\n", - " i.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "colors = plt.cm.rainbow_r(np.linspace(0,1,len(v)))" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "for model_vac in model_vac2:\n", - " #plt.plot(model_vac.t,model_vac.I,label='I with vaccines')\n", - " \"{:#.2g}\".format(i)\n", - " plt.plot(model_vac.t,model_vac.I,label='I eff:'+\"{:#.1g}\".format(model_vac.v(0)),color=colors[i])\n", - " #plt.plot(model_vac.t,model_vac.Iv,label='Iv eff:'+\"{:#.1g}\".format(model_vac.v(0)),color=colors[i],linestyle='dashed')\n", - " i+=1\n", - "plt.plot(np.arange(model_vac.tsim),[Psi(i) for i in np.arange(model_vac.tsim)])\n", - "plt.legend(loc=0)\n", - "\n", - "plt.xlim(0,300)\n", - "plt.title('Vaccines effectivity')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hospitalization CFR variable" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "pH_R = cv19functions.Events(values=[0.7,0.6],days=[[0,30],[30,500]])\n", - "pH_D = cv19functions.Events(values=[0.3,0.4],days=[[0,30],[30,500]])" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "import scipy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scipy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pH_D = sc" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Input configuration file\n", - "config = 'cfg/SEIRHVD.toml'\n", - "# Build simulation object\n", - "\n", - "model1 = SEIRHVD(config = config, H_cap=4000, pH_R = pH_R,pH_D = pH_D)\n", - "# Simulate (solve ODE)\n", - "model1.integrate()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matplotlib\n", - "#plt.plot(model.t,model.S,label='S')\n", - "#plt.plot(model.t,model.E,label='E')\n", - "plt.plot(model1.t,model1.I,label='I')\n", - "plt.plot(model1.t,model1.Im,label='Im')\n", - "plt.plot(model1.t,model1.Icr,label='Icr')\n", - "#plt.plot(model.t,model.R,label='R')\n", - "plt.xlim(0,300)\n", - "plt.legend(loc=0)\n", - "plt.title('SEIR from SEIRHVD lib')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot matplotlib\n", - "#plt.plot(model1.t,model1.H,color='tab:red',label='H 1')\n", - "plt.plot(model1.t,model1.D_d,color='tab:red',linestyle='dotted',label='D 1')\n", - "#plt.plot(model1.t,[model1.H_cap]*len(model.t),color='tab:red',linestyle='dashed',label='H_cap 1')\n", - "plt.plot(model1.t,[pH_D(i) for i in model.t],color='tab:red',linestyle='dashed',label='pH_D')\n", - "\n", - "#plt.plot(np.arange(model_vac.tsim),[Psi(i) for i in np.arange(model_vac.tsim)])\n", - "plt.legend(loc=0)\n", - "plt.title('ICU capacity and deaths')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SEIR_Bimodal.ipynb b/research/SEIR_Bimodal.ipynb deleted file mode 100644 index 583d2b8..0000000 --- a/research/SEIR_Bimodal.ipynb +++ /dev/null @@ -1,774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIR Bimodal\n", - "This Jupyter notebook presents the SEIR Bimodal model EDOs implementation in python.\n", - "The equations are the following: \n", - "\n", - "\\begin{align}\n", - " \\dot{S_s} &=-\\frac{\\beta\\alpha_s S_s}{N}\\left(I_s + I_r\\right) \\\\\n", - " \\dot{S_r} &=-\\frac{\\beta\\alpha_s S_r}{N}\\left(I_s + I_r\\right)-\\frac{\\beta\\alpha_r S_r}{p_r N} I_r \\\\ \n", - " \\dot{E_s} &=\\frac{\\beta\\alpha_s S_s}{N}\\left(I_s + I_r\\right) - \\sigma E_s \\\\\n", - " \\dot{E_r} &=\\frac{\\beta\\alpha_s S_r}{N}\\left(I_s + I_r\\right) + \\frac{\\beta\\alpha_r S_r}{p_r N} I_r - \\sigma E_{s}\\\\ \n", - " \\dot{I_s} &=\\sigma E_s - \\gamma I_s \\\\\n", - " \\dot{I_r} &=\\sigma E_r - \\gamma I_r \\\\\n", - " \\dot{R} &=\\gamma (I_s + I_r) \\\\\n", - "\\end{align}\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Linux\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR_bimodal/')\n", - "sys.path.insert(1, 'src/SEIR_bimodal/')\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, 'src/SEIR/')\n", - "sys.path.insert(1, '../src/utils/')\n", - "sys.path.insert(1, 'src/utils/')\n", - "sys.path.insert(1, '../')\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " %matplotlib tk\n", - " print('Mac')\n", - "\n", - "from datetime import datetime\n", - "from datetime import timedelta\n", - "from joblib import Parallel, delayed\n", - "import multiprocessing\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import requests\n", - "\n", - "from class_SEIR import SEIR\n", - "from class_SEIR_bimodal import SEIRbimodal\n", - "from Quarantine import Quarantine\n", - "from SEIR_parallel import seirMetaAnalysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.4\n", - "mu = 1 \n", - "# Incubation rate\n", - "gamma = 0.2\n", - "# Recovery rate\n", - "sigma = 0.1\n", - "\n", - "# Risk population proportion\n", - "p_r = 0.05\n", - "\n", - "# Total population\n", - "population = 1000000\n", - "\n", - "# Saturation Dynamics:\n", - "k = 0\n", - "\n", - "# simulation time\n", - "tsim = 1000\n", - "\n", - "# Do exposed infect?\n", - "expinfection = 0 # NO =)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mobility functions" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Mobility\n", - "mob_s = 0.55\n", - "mob_r = 0\n", - "\n", - "alpha_s = Quarantine(mob_s).alpha\n", - "alpha_r = Quarantine(mob_r).alpha\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial conditions" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "I0 = 200\n", - "I_ac0 = 0\n", - "I_d0 = 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create model and integrate" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " message: 'The solver successfully reached the end of the integration interval.'\n", - " nfev: 181\n", - " njev: 6\n", - " nlu: 6\n", - " sol: None\n", - " status: 0\n", - " success: True\n", - " t: array([0.00000000e+00, 7.90569415e-07, 1.58113883e-06, 6.44039819e-03,\n", - " 1.28792152e-02, 1.93180323e-02, 8.37062029e-02, 1.48094373e-01,\n", - " 2.12482544e-01, 2.76870715e-01, 5.59370515e-01, 8.41870316e-01,\n", - " 1.12437012e+00, 1.40686992e+00, 1.68936972e+00, 2.14021594e+00,\n", - " 2.59106217e+00, 3.04190839e+00, 3.49275461e+00, 3.94360084e+00,\n", - " 4.49506348e+00, 5.04652612e+00, 5.59798877e+00, 6.14945141e+00,\n", - " 6.71780273e+00, 7.28615405e+00, 7.85450536e+00, 8.79109672e+00,\n", - " 9.72768808e+00, 1.06642794e+01, 1.19381903e+01, 1.32121011e+01,\n", - " 1.44860119e+01, 1.63607963e+01, 1.82355806e+01, 2.01103650e+01,\n", - " 2.10045776e+01, 2.18987902e+01, 2.27930029e+01, 2.57671734e+01,\n", - " 2.87413439e+01, 3.17155145e+01, 3.86467793e+01, 4.55780440e+01,\n", - " 5.25093088e+01, 6.52841425e+01, 7.80589763e+01, 9.08338100e+01,\n", - " 1.03608644e+02, 1.46093370e+02, 1.88578095e+02, 2.31062821e+02,\n", - " 2.73547547e+02, 3.08818275e+02, 3.44089003e+02, 3.79359730e+02,\n", - " 4.14630458e+02, 4.56275727e+02, 4.97920996e+02, 5.39566266e+02,\n", - " 5.81211535e+02, 6.22856804e+02, 6.64502073e+02, 7.06147343e+02,\n", - " 7.47792612e+02, 7.89437881e+02, 8.31083150e+02, 8.72728419e+02,\n", - " 9.21582540e+02, 9.70436661e+02, 1.00000000e+03])\n", - " t_events: None\n", - " y: array([[9.49620000e+05, 9.49620000e+05, 9.49620000e+05, 9.49619731e+05,\n", - " 9.49619462e+05, 9.49619194e+05, 9.49616517e+05, 9.49613857e+05,\n", - " 9.49611214e+05, 9.49608587e+05, 9.49597244e+05, 9.49586180e+05,\n", - " 9.49575371e+05, 9.49564794e+05, 9.49554429e+05, 9.49538279e+05,\n", - " 9.49522553e+05, 9.49507192e+05, 9.49492142e+05, 9.49477360e+05,\n", - " 9.49459585e+05, 9.49442090e+05, 9.49424821e+05, 9.49407735e+05,\n", - " 9.49390274e+05, 9.49372935e+05, 9.49355687e+05, 9.49327397e+05,\n", - " 9.49299206e+05, 9.49271045e+05, 9.49232700e+05, 9.49194230e+05,\n", - " 9.49155572e+05, 9.49098248e+05, 9.49040339e+05, 9.48981785e+05,\n", - " 9.48953619e+05, 9.48925296e+05, 9.48896815e+05, 9.48800913e+05,\n", - " 9.48703182e+05, 9.48603579e+05, 9.48363902e+05, 9.48113372e+05,\n", - " 9.47851547e+05, 9.47338018e+05, 9.46781656e+05, 9.46179039e+05,\n", - " 9.45526707e+05, 9.42950312e+05, 9.39634285e+05, 9.35418079e+05,\n", - " 9.30136781e+05, 9.24834235e+05, 9.18627352e+05, 9.11485442e+05,\n", - " 9.03427253e+05, 8.92837637e+05, 8.81311791e+05, 8.69222957e+05,\n", - " 8.57026710e+05, 8.45197091e+05, 8.34156081e+05, 8.24220516e+05,\n", - " 8.15569422e+05, 8.08248238e+05, 8.02192706e+05, 7.97274244e+05,\n", - " 7.92734644e+05, 7.89272035e+05, 7.87611199e+05],\n", - " [4.99800000e+04, 4.99800000e+04, 4.99800000e+04, 4.99799858e+04,\n", - " 4.99799717e+04, 4.99799576e+04, 4.99798167e+04, 4.99796767e+04,\n", - " 4.99795376e+04, 4.99793993e+04, 4.99788023e+04, 4.99782200e+04,\n", - " 4.99776511e+04, 4.99770944e+04, 4.99765489e+04, 4.99756989e+04,\n", - " 4.99748712e+04, 4.99740627e+04, 4.99732707e+04, 4.99724926e+04,\n", - " 4.99715571e+04, 4.99706363e+04, 4.99697274e+04, 4.99688281e+04,\n", - " 4.99679092e+04, 4.99669966e+04, 4.99660888e+04, 4.99645999e+04,\n", - " 4.99631161e+04, 4.99616339e+04, 4.99596158e+04, 4.99575911e+04,\n", - " 4.99555564e+04, 4.99525394e+04, 4.99494915e+04, 4.99464097e+04,\n", - " 4.99449273e+04, 4.99434366e+04, 4.99419376e+04, 4.99368902e+04,\n", - " 4.99317464e+04, 4.99265041e+04, 4.99138896e+04, 4.99007038e+04,\n", - " 4.98869235e+04, 4.98598957e+04, 4.98306135e+04, 4.97988968e+04,\n", - " 4.97645635e+04, 4.96289638e+04, 4.94544361e+04, 4.92325304e+04,\n", - " 4.89545674e+04, 4.86754860e+04, 4.83488080e+04, 4.79729180e+04,\n", - " 4.75488028e+04, 4.69914546e+04, 4.63848311e+04, 4.57485767e+04,\n", - " 4.51066690e+04, 4.44840574e+04, 4.39029517e+04, 4.33800271e+04,\n", - " 4.29247064e+04, 4.25393810e+04, 4.22206688e+04, 4.19618023e+04,\n", - " 4.17228760e+04, 4.15406334e+04, 4.14532210e+04],\n", - " [1.90000000e+02, 1.90000018e+02, 1.90000036e+02, 1.90146600e+02,\n", - " 1.90292896e+02, 1.90438926e+02, 1.91884666e+02, 1.93304311e+02,\n", - " 1.94698369e+02, 1.96067342e+02, 2.01789322e+02, 2.07076982e+02,\n", - " 2.11966379e+02, 2.16490637e+02, 2.20680117e+02, 2.26741019e+02,\n", - " 2.32120675e+02, 2.36907303e+02, 2.41177731e+02, 2.44998869e+02,\n", - " 2.49146000e+02, 2.52799146e+02, 2.56035902e+02, 2.58921481e+02,\n", - " 2.61590254e+02, 2.63993817e+02, 2.66175377e+02, 2.69384545e+02,\n", - " 2.72212411e+02, 2.74758381e+02, 2.77900157e+02, 2.80781883e+02,\n", - " 2.83496632e+02, 2.87310863e+02, 2.91005271e+02, 2.94656714e+02,\n", - " 2.96395568e+02, 2.98136541e+02, 2.99880174e+02, 3.05721591e+02,\n", - " 3.11641587e+02, 3.17656364e+02, 3.32093380e+02, 3.47137265e+02,\n", - " 3.62815407e+02, 3.93436693e+02, 4.26419224e+02, 4.61912329e+02,\n", - " 5.00060627e+02, 6.47819049e+02, 8.31286270e+02, 1.05373804e+03,\n", - " 1.31524059e+03, 1.55881720e+03, 1.81932721e+03, 2.08581721e+03,\n", - " 2.34330264e+03, 2.61147127e+03, 2.81251289e+03, 2.91882952e+03,\n", - " 2.91386171e+03, 2.79718226e+03, 2.58554927e+03, 2.30695849e+03,\n", - " 1.99430439e+03, 1.67798731e+03, 1.38173884e+03, 1.11831160e+03,\n", - " 8.58424553e+02, 6.50035190e+02, 5.45580889e+02],\n", - " [1.00000000e+01, 1.00000009e+01, 1.00000019e+01, 1.00077158e+01,\n", - " 1.00154156e+01, 1.00231013e+01, 1.00991929e+01, 1.01739111e+01,\n", - " 1.02472826e+01, 1.03193338e+01, 1.06204906e+01, 1.08987885e+01,\n", - " 1.11561252e+01, 1.13942441e+01, 1.16147430e+01, 1.19337378e+01,\n", - " 1.22168776e+01, 1.24688054e+01, 1.26935648e+01, 1.28946773e+01,\n", - " 1.31129474e+01, 1.33052182e+01, 1.34755738e+01, 1.36274464e+01,\n", - " 1.37679081e+01, 1.38944114e+01, 1.40092304e+01, 1.41781339e+01,\n", - " 1.43269690e+01, 1.44609674e+01, 1.46263240e+01, 1.47779938e+01,\n", - 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" 1.28065608e-02, 1.91548811e-02, 8.07026759e-02, 1.38883381e-01,\n", - " 1.93897712e-01, 2.45934789e-01, 4.43307141e-01, 5.99249853e-01,\n", - " 7.23352050e-01, 8.22994808e-01, 9.03749587e-01, 1.00367243e+00,\n", - " 1.07810928e+00, 1.13516727e+00, 1.18015710e+00, 1.21650340e+00,\n", - " 1.25253770e+00, 1.28194911e+00, 1.30657786e+00, 1.32754336e+00,\n", - " 1.34623650e+00, 1.36254398e+00, 1.37697125e+00, 1.39760159e+00,\n", - " 1.41518897e+00, 1.43058960e+00, 1.44906571e+00, 1.46551150e+00,\n", - " 1.48062136e+00, 1.50136275e+00, 1.52106328e+00, 1.54033246e+00,\n", - " 1.54954159e+00, 1.55881983e+00, 1.56805992e+00, 1.59878193e+00,\n", - " 1.62976009e+00, 1.66121794e+00, 1.73674902e+00, 1.81545418e+00,\n", - " 1.89748086e+00, 2.05770010e+00, 2.23029327e+00, 2.41604202e+00,\n", - " 2.61570434e+00, 3.38924249e+00, 4.35016851e+00, 5.51599956e+00,\n", - " 6.88760565e+00, 8.16638701e+00, 9.53560268e+00, 1.09382715e+01,\n", - " 1.22960759e+01, 1.37142803e+01, 1.47827599e+01, 1.53554564e+01,\n", - " 1.53438051e+01, 1.47431112e+01, 1.36392797e+01, 1.21793764e+01,\n", - " 1.05366854e+01, 8.87076406e+00, 7.30748848e+00, 5.91686561e+00,\n", - " 4.54385109e+00, 3.44138843e+00, 2.88878255e+00]])\n", - " y_events: None" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seirbimodal = SEIRbimodal(tsim,alpha_s,alpha_r,beta,mu,sigma = sigma,gamma = gamma,p_r=p_r,k=k,I0=I0,I_ac0=I_ac0,I_d0=I_d0,population=population,expinfection = expinfection )\n", - "seirbimodal.integr_sci(0,tsim,0.1)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis and QA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## People conservation" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(seirbimodal.t,seirbimodal.S+seirbimodal.E+seirbimodal.I+seirbimodal.R,label='Simulation total people')\n", - "plt.hlines(population,0,tsim,label='Total People') \n", - "plt.title('Population QA')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SEIR Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot SEIR\n", - "plt.plot(seirbimodal.t,seirbimodal.S,label='S')\n", - "plt.plot(seirbimodal.t,seirbimodal.E,label='E')\n", - "plt.plot(seirbimodal.t,seirbimodal.I,label='I')\n", - "plt.plot(seirbimodal.t,seirbimodal.R,label='R')\n", - "plt.title('SEIR')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bimodal Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(seirbimodal.t,seirbimodal.Is,label='Is')\n", - "plt.plot(seirbimodal.t,seirbimodal.Ir,label='Ir')\n", - "plt.title('Active Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIRbimodal vs SEIR" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "p_r = 0.05\n", - "# Mobility\n", - "mob_s = 0.8\n", - "mob_r = 1-mob_s 0.5\n", - "\n", - "alpha = Quarantine(mob_s).alpha\n", - "alpha_s = Quarantine(mob_s).alpha\n", - "alpha_r = Quarantine(mob_r).alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate SEIRbimodal" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " message: 'The solver successfully reached the end of the integration interval.'\n", - " nfev: 402\n", - " njev: 13\n", - " nlu: 13\n", - " sol: None\n", - " status: 0\n", - " success: True\n", - " t: array([0.00000000e+00, 7.90569415e-07, 1.58113883e-06, 6.77114728e-03,\n", - " 1.35407134e-02, 2.03102796e-02, 8.80059410e-02, 1.55701602e-01,\n", - " 2.23397264e-01, 2.91092925e-01, 5.86965744e-01, 8.82838563e-01,\n", - " 1.17871138e+00, 1.47458420e+00, 1.77045702e+00, 2.22256073e+00,\n", - " 2.67466443e+00, 3.12676814e+00, 3.57887185e+00, 4.03097555e+00,\n", - " 4.58471624e+00, 5.13845693e+00, 5.69219762e+00, 6.24593832e+00,\n", - " 6.80820537e+00, 7.37047241e+00, 7.93273946e+00, 9.04545560e+00,\n", - " 1.01581717e+01, 1.12708879e+01, 1.26861984e+01, 1.41015089e+01,\n", - " 1.55168194e+01, 1.71904428e+01, 1.88640662e+01, 2.05376896e+01,\n", - " 2.18468774e+01, 2.31560652e+01, 2.44652529e+01, 2.53193122e+01,\n", - " 2.61733715e+01, 2.70274308e+01, 2.79900436e+01, 2.89526565e+01,\n", - " 2.99152693e+01, 3.20499746e+01, 3.41846799e+01, 3.63193852e+01,\n", - " 3.78381835e+01, 3.93569818e+01, 4.08757801e+01, 4.14950323e+01,\n", - " 4.38483256e+01, 4.62016189e+01, 4.85549122e+01, 5.25209565e+01,\n", - " 5.64870008e+01, 6.04530451e+01, 6.44190894e+01, 7.09116053e+01,\n", - " 7.74041212e+01, 8.38966371e+01, 9.03891530e+01, 9.85389393e+01,\n", - " 1.06688726e+02, 1.14838512e+02, 1.22988298e+02, 1.31138085e+02,\n", - " 1.39287871e+02, 1.47437658e+02, 1.55587444e+02, 1.64908103e+02,\n", - " 1.74228763e+02, 1.81982384e+02, 1.89736006e+02, 1.97489628e+02,\n", - " 2.05243250e+02, 2.12996872e+02, 2.20750494e+02, 2.28504116e+02,\n", - " 2.36257738e+02, 2.44011360e+02, 2.51764982e+02, 2.59518604e+02,\n", - " 2.67272226e+02, 2.75025847e+02, 2.82779469e+02, 2.90533091e+02,\n", - " 2.98286713e+02, 3.06040335e+02, 3.15212155e+02, 3.24383976e+02,\n", - " 3.33555796e+02, 3.42727616e+02, 3.51899436e+02, 3.59361069e+02,\n", - " 3.66822702e+02, 3.74284335e+02, 3.81745968e+02, 3.89207601e+02,\n", - " 3.97798134e+02, 4.06388666e+02, 4.14979198e+02, 4.23569731e+02,\n", - " 4.30437258e+02, 4.37304786e+02, 4.44172313e+02, 4.51039841e+02,\n", - " 4.57907369e+02, 4.64774896e+02, 4.70230325e+02, 4.75685754e+02,\n", - " 4.80144861e+02, 4.84603968e+02, 4.88201118e+02, 4.91798268e+02,\n", - " 4.95395419e+02, 4.98992569e+02, 5.01825536e+02, 5.04658504e+02,\n", - " 5.07491471e+02, 5.10324439e+02, 5.13157406e+02, 5.16604488e+02,\n", - " 5.20051570e+02, 5.23498652e+02, 5.26945734e+02, 5.30392816e+02,\n", - " 5.33839898e+02, 5.39000386e+02, 5.44160874e+02, 5.49321362e+02,\n", - " 5.54481850e+02, 5.59642338e+02, 5.67959494e+02, 5.76276650e+02,\n", - " 5.84593805e+02, 5.92910961e+02, 6.01228117e+02, 6.09545272e+02,\n", - " 6.17862428e+02, 6.26179584e+02, 6.34496739e+02, 6.42813895e+02,\n", - " 6.51131051e+02, 6.59448206e+02, 6.67765362e+02, 6.76082517e+02,\n", - " 6.84399673e+02, 6.92716829e+02, 7.01033984e+02, 7.09351140e+02,\n", - " 7.17668296e+02, 7.25985451e+02, 7.34302607e+02, 7.42619763e+02,\n", - " 7.50936918e+02, 7.59254074e+02, 7.67571230e+02, 7.75888385e+02,\n", - " 7.85378883e+02, 7.94869381e+02, 8.04359879e+02, 8.13850376e+02,\n", - " 8.23340874e+02, 8.32831372e+02, 8.45575012e+02, 8.58318652e+02,\n", - " 8.71062292e+02, 8.83805932e+02, 8.96549572e+02, 9.09293212e+02,\n", - " 9.33946072e+02, 9.53700876e+02, 9.73455679e+02, 9.93210482e+02,\n", - " 1.00000000e+03])\n", - " t_events: None\n", - " y: array([[9.49620000e+05, 9.49620000e+05, 9.49620000e+05, ...,\n", - " 3.36743504e+05, 3.36743504e+05, 3.36743504e+05],\n", - " [4.99800000e+04, 4.99800000e+04, 4.99800000e+04, ...,\n", - " 1.54336717e+04, 1.54336717e+04, 1.54336717e+04],\n", - " [1.90000000e+02, 1.90000033e+02, 1.90000066e+02, ...,\n", - " 1.39442033e-06, 5.00424816e-07, 4.50622033e-07],\n", - " ...,\n", - " [0.00000000e+00, 7.90569577e-07, 1.58113932e-06, ...,\n", - " 3.45563283e+04, 3.45563283e+04, 3.45563283e+04],\n", - " [0.00000000e+00, 1.50208096e-05, 3.00416100e-05, ...,\n", - " 1.44111110e-07, 5.17178759e-08, 4.65709766e-08],\n", - " [0.00000000e+00, 7.90568952e-07, 1.58113744e-06, ...,\n", - " 7.41271940e-09, 2.66023834e-09, 2.39549603e-09]])\n", - " y_events: None" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seirbimodal = SEIRbimodal(tsim,alpha_s,alpha_r,beta,mu,sigma = sigma,gamma = gamma,p_r=p_r,k=k,I0=I0,I_ac0=I_ac0,I_d0=I_d0,population=population,expinfection = expinfection )\n", - "seirbimodal.integr_sci(0,tsim,0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate SEIR" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " message: 'The solver successfully reached the end of the integration interval.'\n", - " nfev: 380\n", - " njev: 18\n", - " nlu: 18\n", - " sol: None\n", - " status: 0\n", - " success: True\n", - " t: array([0.00000000e+00, 7.90569415e-07, 1.58113883e-06, 5.68159864e-03,\n", - " 1.13616161e-02, 1.70416336e-02, 7.38418086e-02, 1.30641984e-01,\n", - " 1.87442159e-01, 2.44242334e-01, 5.20145925e-01, 7.96049517e-01,\n", - " 1.07195311e+00, 1.34785670e+00, 1.62376029e+00, 2.07460500e+00,\n", - " 2.52544971e+00, 2.97629442e+00, 3.42713912e+00, 3.87798383e+00,\n", - " 4.42947496e+00, 4.98096609e+00, 5.53245722e+00, 6.08394834e+00,\n", - " 6.65430313e+00, 7.22465792e+00, 7.79501271e+00, 8.66120159e+00,\n", - " 9.52739048e+00, 1.03935794e+01, 1.15867122e+01, 1.27798451e+01,\n", - " 1.39729779e+01, 1.57359543e+01, 1.74989307e+01, 1.92619071e+01,\n", - " 2.15377041e+01, 2.38135012e+01, 2.60892983e+01, 2.92476697e+01,\n", - " 3.00372626e+01, 3.08268554e+01, 3.16164483e+01, 3.21338001e+01,\n", - " 3.26511519e+01, 3.31685037e+01, 3.38315188e+01, 3.44945339e+01,\n", - " 3.51575490e+01, 3.68160944e+01, 3.84746398e+01, 4.01331852e+01,\n", - " 4.76542073e+01, 4.95344629e+01, 5.32949740e+01, 5.70554851e+01,\n", - " 6.08159962e+01, 6.45765073e+01, 7.12231387e+01, 7.78697702e+01,\n", - " 8.45164017e+01, 9.11630331e+01, 9.78096646e+01, 1.07819639e+02,\n", - " 1.17829613e+02, 1.27839587e+02, 1.37849562e+02, 1.47859536e+02,\n", - " 1.57869510e+02, 1.67879484e+02, 1.76027364e+02, 1.84175244e+02,\n", - " 1.92323123e+02, 2.00471003e+02, 2.08618882e+02, 2.17688723e+02,\n", - " 2.26758564e+02, 2.28381777e+02, 2.30004990e+02, 2.31628203e+02,\n", - " 2.33251417e+02, 2.36497843e+02, 2.39744270e+02, 2.42990696e+02,\n", - " 2.48687360e+02, 2.54384024e+02, 2.60080688e+02, 2.65777351e+02,\n", - " 2.72217755e+02, 2.78658159e+02, 2.85098563e+02, 2.91538967e+02,\n", - " 3.02896866e+02, 3.11100894e+02, 3.19304922e+02, 3.27508950e+02,\n", - " 3.34166972e+02, 3.40824994e+02, 3.47483016e+02, 3.54141038e+02,\n", - " 3.61977765e+02, 3.69814493e+02, 3.77651221e+02, 3.85487948e+02,\n", - " 3.93324676e+02, 4.01161404e+02, 4.08998131e+02, 4.16834859e+02,\n", - " 4.24671586e+02, 4.32508314e+02, 4.40345042e+02, 4.48181769e+02,\n", - " 4.56018497e+02, 4.63855224e+02, 4.73682930e+02, 4.83510636e+02,\n", - " 4.93338342e+02, 5.01034309e+02, 5.08730277e+02, 5.16426244e+02,\n", - " 5.24122211e+02, 5.31818179e+02, 5.39514146e+02, 5.47210113e+02,\n", - " 5.48552888e+02, 5.49895663e+02, 5.51238438e+02, 5.52581213e+02,\n", - " 5.53923988e+02, 5.56213801e+02, 5.58503615e+02, 5.60793428e+02,\n", - " 5.63083241e+02, 5.67405618e+02, 5.71727995e+02, 5.76050372e+02,\n", - " 5.80372750e+02, 5.87488853e+02, 5.94604956e+02, 6.01721060e+02,\n", - " 6.08837163e+02, 6.15953266e+02, 6.23816453e+02, 6.31679640e+02,\n", - " 6.39542827e+02, 6.47406014e+02, 6.55269201e+02, 6.63132388e+02,\n", - " 6.70995574e+02, 6.78858761e+02, 6.87873073e+02, 6.96887385e+02,\n", - " 7.05901697e+02, 7.14916009e+02, 7.23930321e+02, 7.31518666e+02,\n", - " 7.37777971e+02, 7.44037275e+02, 7.50296580e+02, 7.56555884e+02,\n", - " 7.62815189e+02, 7.71978998e+02, 7.81142807e+02, 7.90306616e+02,\n", - " 7.99470425e+02, 8.08634234e+02, 8.18928376e+02, 8.29222518e+02,\n", - " 8.39516661e+02, 8.49810803e+02, 8.60104945e+02, 8.73077229e+02,\n", - " 8.86049512e+02, 8.99021796e+02, 9.11994080e+02, 9.24966364e+02,\n", - " 9.43472782e+02, 9.61979201e+02, 9.80485620e+02, 9.98992039e+02,\n", - " 1.00000000e+03])\n", - " t_events: None\n", - " y: array([[9.99520000e+05, 9.99520000e+05, 9.99520000e+05, ...,\n", - " 3.57824396e+05, 3.57824396e+05, 3.57824396e+05],\n", - " [2.80000000e+02, 2.80000028e+02, 2.80000057e+02, ...,\n", - " 1.45785401e-06, 7.64015152e-07, 7.39857999e-07],\n", - " [2.00000000e+02, 1.99999991e+02, 1.99999981e+02, ...,\n", - " 8.67194490e-07, 4.54468951e-07, 4.40099185e-07],\n", - " [0.00000000e+00, 3.16227751e-05, 6.32455487e-05, ...,\n", - " 6.42175604e+05, 6.42175604e+05, 6.42175604e+05],\n", - " [0.00000000e+00, 2.21359459e-05, 4.42718940e-05, ...,\n", - " 6.41975604e+05, 6.41975604e+05, 6.41975604e+05],\n", - " [0.00000000e+00, 2.21359284e-05, 4.42718415e-05, ...,\n", - " 1.50618428e-07, 7.89064071e-08, 7.64190721e-08]])\n", - " y_events: None" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seir = SEIR(tsim,alpha,beta,mu,sigma = sigma, gamma = gamma, k=k,I=I0,I_ac=I_ac0,I_d=I_d0,population=population,expinfection = expinfection)\n", - "seir.integr_sci(0,tsim,0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SEIR Bimodal Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(seirbimodal.t,seirbimodal.Is,label='Is')\n", - "plt.plot(seirbimodal.t,seirbimodal.Ir,label='Ir')\n", - "plt.title('Bimodal Dynamics Active Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Global Dynamics comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(seirbimodal.t,seirbimodal.S,label='S_bi',color='blue')\n", - "plt.plot(seirbimodal.t,seirbimodal.E,label='E_bi',color='cyan')\n", - "plt.plot(seirbimodal.t,seirbimodal.I,label='I_bi',color='red')\n", - "plt.plot(seirbimodal.t,seirbimodal.R,label='R_bi',color='green')\n", - "plt.plot(seir.t,seir.S,label='S',color='blue',linestyle = 'dashed')\n", - "plt.plot(seir.t,seir.E,label='E',color='cyan',linestyle = 'dashed')\n", - "plt.plot(seir.t,seir.I,label='I',color='red',linestyle = 'dashed')\n", - "plt.plot(seir.t,seir.R,label='R',color='green',linestyle = 'dashed')\n", - "plt.title('SEIR')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(seirbimodal.t,seirbimodal.Is_d,label='Is')\n", - "plt.plot(seirbimodal.t,seirbimodal.Ir_d,label='Ir')\n", - "plt.plot(seirbimodal.t,seirbimodal.Ir_d+seirbimodal.Is_d,label='Itot')\n", - "plt.plot(seir.t,seir.I_d,label='I',color='red',linestyle = 'dashed')\n", - "plt.title('Bimodal Dynamics Active Infected')\n", - "plt.legend(loc=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python38264bit48ae65e862f64697a29185f9fa581b02" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SEIR_Data_Fit.ipynb b/research/SEIR_Data_Fit.ipynb deleted file mode 100644 index 9bfbbfd..0000000 --- a/research/SEIR_Data_Fit.ipynb +++ /dev/null @@ -1,440 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SEIR Data Fit\n", - "Conjunto de herramientas para fitear un modelo SEIR unisectorial a los datos reales" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, 'src/SEIR/')\n", - "sys.path.insert(1, '../src/Data/')\n", - "sys.path.insert(1, 'src/Data/')\n", - "sys.path.insert(1, '../')\n", - "\n", - "from datetime import datetime\n", - "from datetime import timedelta\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux':\n", - " %matplotlib tk\n", - "if OS == 'Linux':\n", - " %matplotlib tk \n", - "# windows %matplotlib qt\n", - "from joblib import Parallel, delayed\n", - "import multiprocessing\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import requests\n", - "\n", - "from class_SEIR2 import SEIR\n", - "from Quarantine import Quarantine\n", - "from SEIR_parallel import seirMetaAnalysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data geográfica\n", - "\n", - "Selección de Comuna, región o conjunto de ellas por CUT" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Una comuna\n", - "tstate = '13101' #Santiago\n", - "# Conjunto de comunas\n", - "#tstate = ['13101','13102'] \n", - "\n", - "# Fecha de inicio \n", - "initdate = datetime(2020,5,15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import data\n", - "Infectados Activos, acumulados, diarios y población. **Es necesario estar conectado el VPN del Dlab**" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data importada\n" - ] - } - ], - "source": [ - "\n", - "# Infectados Acumulados y Diarios\n", - "endpoint = 'https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19.csv'\n", - "\n", - "aux = pd.read_csv(endpoint)\n", - "\n", - "if type(tstate) == list: \n", - " I_ac_r = aux.loc[aux['Codigo region'].isin(tstate)].iloc[:,5:-1] \n", - " I_ac_r = I_ac_r.append(aux.loc[aux['Codigo comuna'].isin(tstate)].iloc[:,5:-1])\n", - " I_ac_r = I_ac_r.sum()\n", - "else: \n", - " I_ac_r = aux.loc[aux['Codigo region']==int(tstate)].iloc[:,5:-1]\n", - " I_ac_r = I_ac_r.append(aux.loc[aux['Codigo comuna']==int(tstate)].iloc[:,5:-1])\n", - " I_ac_r = I_ac_r.sum()\n", - "\n", - "I_ac_r_dates = [datetime.strptime(I_ac_r.index[i],'%Y-%m-%d') for i in range(len(I_ac_r))]\n", - "index = np.where(np.array(I_ac_r_dates) >= initdate)[0][0] \n", - "I_ac_r = I_ac_r[index:]\n", - "I_ac_r_dates = I_ac_r_dates[index:]\n", - "I_ac_r_tr = [(I_ac_r_dates[i]-initdate).days for i in range(len(I_ac_r))] \n", - "\n", - "I_d_r = np.diff(np.interp(list(range(I_ac_r_tr[-1])),I_ac_r_tr,I_ac_r))\n", - "I_d_r_tr = list(range(len(I_d_r)))\n", - "I_d_r_dates = [initdate + timedelta(days=i) for i in range(len(I_d_r_tr))]\n", - "\n", - "\n", - "# Infectados Activos\n", - "\n", - "cutlist = []\n", - "cutlistpath = \"../Data/cutlist.csv\"\n", - "cutlist = pd.read_csv(cutlistpath, header = None,dtype=str)\n", - "\n", - "actives = []\n", - "mydict = None\n", - "if type(tstate) == list:\n", - " for i in tstate:\n", - " if len(i)==2:\n", - " for index, row in cutlist.iterrows(): \n", - " state = str(row[0])[0:2]\n", - " comuna = str(row[0])\n", - " if i == state:\n", - " endpoint = \"http://192.168.2.223:5006/getActiveNewCasesByComuna?comuna=\"+comuna\n", - " r = requests.get(endpoint) \n", - " mydict = r.json()\n", - " actives.append(mydict['actives'])\n", - " #data=pd.DataFrame(mydict)\n", - " #Ir = (np.array(actives)).sum(axis=0)\n", - " elif len(i)>2:\n", - " endpoint = \"http://192.168.2.223:5006/getActiveNewCasesByComuna?comuna=\"+i\n", - " r = requests.get(endpoint) \n", - " mydict = r.json()\n", - " actives.append(mydict['actives'])\n", - " #Ir = np.array(mydict['actives'])\n", - " Ir = (np.array(actives)).sum(axis=0)\n", - "else:\n", - " if len(tstate)==2:\n", - " for index, row in cutlist.iterrows(): \n", - " state = str(row[0])[0:2]\n", - " comuna = str(row[0])\n", - " if tstate == state:\n", - " endpoint = \"http://192.168.2.223:5006/getActiveNewCasesByComuna?comuna=\"+comuna\n", - " r = requests.get(endpoint) \n", - " mydict = r.json()\n", - " actives.append(mydict['actives'])\n", - " #data=pd.DataFrame(mydict)\n", - " Ir = (np.array(actives)).sum(axis=0)\n", - " elif len(tstate)>2:\n", - " endpoint = \"http://192.168.2.223:5006/getActiveNewCasesByComuna?comuna=\"+tstate\n", - " r = requests.get(endpoint) \n", - " mydict = r.json()\n", - " Ir = np.array(mydict['actives'])\n", - "\n", - "Ir_dates = [datetime.strptime(mydict['dates'][i][:10],'%Y-%m-%d') for i in range(len(mydict['dates']))]\n", - "index = np.where(np.array(Ir_dates) >= initdate)[0][0] \n", - "Ir=Ir[index:]\n", - "Ir_dates=Ir_dates[index:]\n", - "tr = [(Ir_dates[i]-initdate).days for i in range(len(Ir))]\n", - "\n", - "\n", - "# Population:\n", - "\n", - "endpoint = 'https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19.csv'\n", - "aux = pd.read_csv(endpoint)\n", - "\n", - "if type(tstate) == list:\n", - " population = 0\n", - " for i in tstate:\n", - " if len(i)==2:\n", - " population += int(aux.loc[aux['Codigo region']==int(i)].iloc[:,4].sum())\n", - " if len(i)>2:\n", - " population += int(aux.loc[aux['Codigo comuna']==int(i)].iloc[:,4].sum()) \n", - "else:\n", - " if len(tstate)==2:\n", - " population = aux.loc[aux['Codigo region']==int(tstate)].iloc[:,4].sum()\n", - " if len(tstate)>2:\n", - " population = int(aux.loc[aux['Codigo comuna'] == int(tstate)].iloc[:,4])\n", - "\n", - "print('Data importada')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulación" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cuarentenas\n", - "La cuarentena se construye utilizando un \"objeto de cuarentena\" utilizando la librería Quarantine. \n", - "El objeto se construye de la siguiente manera: \n", - "```\n", - " alphafunct(rem_mov,max_mov=0.85,qp=0,iqt=0,fqt=1000,movfunct = 'once')\n", - "```\n", - " El unico parámetro obligatorio es el de movilidad remanente. El resto son opcionales dependiendo del tipo de cuarentena que se quiera realizar.\n", - " El parámetro **movfunct** puede ser 'once' para una cuarentena total, o 'square' para cuarentenas dinámicas con período qp. \n", - "**iqt y fqt** son tiempo inicial y final de la cuarentena respectivamente." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from Quarantine import alphafunct" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "s1 = Quarantine(0.5)\n", - "s2 = Quarantine(0.6)\n", - "s3 = Quarantine(0.7)\n", - "quarantines = [s1.alpha,s2.alpha,s3.alpha]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "s1.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epi Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tsim = 1000\n", - "beta = 0.117\n", - "mu = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SFK" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "k = [0,5,10,15,20]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "meta = seirMetaAnalysis()\n", - "sims = meta.simulate_k2(tsim,quarantines,beta,mu,k=k, I=Ir[0],I_ac=I_ac_r[0],I_d=I_d_r[0],population=population )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Show Real Data\n", - "realdata = True\n", - "# amount of time to show\n", - "xlim = 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Actives" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ylim = max([max(sims[i][j].I[:xlim]) for j in range(np.shape(sims)[1]) for i in range(np.shape(sims)[0]) ])\n", - "fig, axs = plt.subplots(len(k), len(quarantines))\n", - "\n", - "for i in range(len(k)):\n", - " for j in range(len(quarantines)):\n", - " axs[i, j].plot(sims[i][j].t,sims[i][j].I,label=\"Infectados\")\n", - " axs[i, j].set_title(\"K: \"+str(k[i])+\" | Alpha: \"+str([0.5,0.6,0.7][j]))\n", - " if realdata == True:\n", - " axs[i, j].scatter(tr,Ir,label='Infectados Activos reales') \n", - " axs[i, j].set_ylim([0,ylim*1.05])\n", - " axs[i, j].set_xlim([0,xlim]) \n", - "\n", - "lines, labels = fig.axes[-1].get_legend_handles_labels() \n", - "fig.legend(lines, labels,loc = 'best')\n", - "fig.suptitle('Infectados Activos')\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acumulados" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ylim = max([max(sims[i][j].I_ac[:xlim]) for j in range(np.shape(sims)[1]) for i in range(np.shape(sims)[0]) ])\n", - "fig, axs = plt.subplots(len(k), len(quarantines))\n", - "\n", - "for i in range(len(k)):\n", - " for j in range(len(quarantines)):\n", - " axs[i, j].plot(sims[i][j].t,sims[i][j].I_ac,label=\"Infectados\")\n", - " axs[i, j].set_title(\"K: \"+str(k[i])+\" | Alpha: \"+str([0.5,0.6,0.7][j]))\n", - " if realdata == True:\n", - " axs[i, j].scatter(I_ac_r_tr,I_ac_r,label='Infectados Acumulados reales')\n", - " axs[i, j].set_ylim([0,ylim*1.05])\n", - " axs[i, j].set_xlim([0,xlim]) \n", - "\n", - "lines, labels = fig.axes[-1].get_legend_handles_labels() \n", - "fig.legend(lines, labels,loc = 'best')\n", - "fig.suptitle('Infectados Acumulados')\n", - "fig.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ylim = max([max(sims[i][j].I_d[:xlim]) for j in range(np.shape(sims)[1]) for i in range(np.shape(sims)[0]) ])\n", - "fig, axs = plt.subplots(len(k), len(quarantines))\n", - "\n", - "for i in range(len(k)):\n", - " for j in range(len(quarantines)):\n", - " axs[i, j].plot(sims[i][j].t,sims[i][j].I_d,label=\"Infectados\")\n", - " axs[i, j].set_title(\"K: \"+str(k[i])+\" | Alpha: \"+str([0.5,0.6,0.7][j]))\n", - " if realdata == True:\n", - " axs[i, j].scatter(I_d_r_tr,I_d_r,label='Infectados diarios reales') \n", - " axs[i, j].set_ylim([0,ylim*1.05])\n", - " axs[i, j].set_xlim([0,xlim]) \n", - "\n", - "lines, labels = fig.axes[-1].get_legend_handles_labels() \n", - "fig.legend(lines, labels,loc = 'best')\n", - "fig.suptitle('Infectados Diarios')\n", - "fig.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.2 64-bit", - "language": "python", - "name": "python38264bit48ae65e862f64697a29185f9fa581b02" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SEIR_SFK_scaling.ipynb b/research/SEIR_SFK_scaling.ipynb deleted file mode 100644 index 709e7f1..0000000 --- a/research/SEIR_SFK_scaling.ipynb +++ /dev/null @@ -1,648 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compartimental Model Simulator\n", - "\n", - "**Created by:** Samuel Ropert \n", - "**Creation date:** 04/08/2020 \n", - "**Institution:** Computational Biology Lab - Fundación Ciencia y Vida, Chile \n", - "\n", - "## SEIR\n", - "This jupyter notebook shows how to build a single SEIR simulation object with different quarantine scenarios. In this example we show 4 quarantine scenarios, 2 with total quarantines, and 2 with dynamic quarantines.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../src/SEIR/')\n", - "sys.path.insert(1, 'src/SEIR/')\n", - "from SEIRmodel import SEIRmodel\n", - "import numpy as np\n", - "from datetime import datetime\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib tk\n", - "from joblib import Parallel, delayed\n", - "import multiprocessing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Geografical Parameters\n", - "Elegir comuna o region según su código CUT" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "tstate = '13' # tstate = ['13','14']\n", - "initdate = datetime(2020,5,15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Epidemiological Parameters\n", - "* **beta:** Infection rate\n", - "* **mu:** Initial exposed obtained from the initial infected mu=E0/I0\n", - "* **Scale Factor:** Proportion of real infected compared to reported ones (1: all the infecteds are reported)\n", - "* **Sero Prevalence Factor:** Adjust the proportion of the population that enters the virus dynamics\n", - "* **Exposed Infection:** rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "beta = 0.12 # Contagion rate\n", - "mu = 0.5 # E0/I0 initial rate\n", - "\n", - "k=40 # Kinetic Saturation: 0 for mass action mixing\n", - "\n", - "ScaleFactor = 1 # Scale Factor: Number of real infected over reported \n", - "SeroPrevFactor = 1 # Sero Prevalence Factor: Adjust the proportion of the population that enters the virus dynamics\n", - "expinfection = 1 # Exposed contagion rate compared to the infected (0 the don't infect, 1 the infect in the same rate as the infected )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation time\n", - "tsim = 1000\n", - "# Population in case we don't pick a specific place\n", - "if not tstate:\n", - " population = 1000000\n", - "# Initial Active Infected \n", - "I_act0 = 100\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quarantines\n", - "\n", - "Quarantine Vector: \n", - " [Tsim, max_mob,rem_mob,quarantine period, quarantine initial time, quarantine final time, quarantine type]" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "max_mob = 0.8 # Maximum mobility\n", - "# Total quarantine\n", - "s1 = [tsim,max_mob,0.65,0,0,tsim,0]\n", - "s2 = [tsim,max_mob,0.5,0,0,tsim,0]\n", - "# Dynamic quarantine\n", - "s3 = [tsim,max_mob,0.3,14,0,tsim,1]\n", - "s4 = [tsim,max_mob,0.5,14,0,tsim,1]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "# Define one quarantine array for each different quarantine remanent mobility\n", - "quarantines = [s1,s3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create simulation Object" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n" - ] - } - ], - "source": [ - "simulation = SEIRmodel(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,tsim = tsim,tstate=tstate, k = k,initdate=initdate)\n", - "simulation.inputarray = np.array(quarantines)\n", - "simulation.addquarantine()\n", - "if not tstate:\n", - " simulation.initialvalues(I_act0,population,R=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Paralelización\n", - "1. Desacoplar objeto de datos externos, si no hay que importar los datos cada vez\n", - "2. Implementar dinámicas de paralelización para iterar con distintos parámetros (beta, k,mu, cuarentenas,seroprev, etc)\n", - "3. Agregar infectados acumulados al valor inicial\n", - "4." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n", - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n", - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n", - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n", - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n", - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n", - "Importando Datos\n", - "Fallecidos Acumulados\n", - "Fallecidos Excesivos\n", - "Infectados Activos Minciencia\n", - "Sochimi\n", - "Import Population\n", - "Infectados Activos\n", - "Infectados diarios\n", - "Infectados Acumulados\n", - "Done\n" - ] - } - ], - "source": [ - "sims = []\n", - "k = [0,5,10,15,20,30,40]\n", - "for i in k:\n", - " simulation = SEIRmodel(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,tsim = tsim,tstate=tstate, k = i,initdate=initdate)\n", - " simulation.inputarray = np.array(quarantines)\n", - " simulation.addquarantine()\n", - " if not tstate:\n", - " simulation.initialvalues(I_act0,population,R=0)\n", - " sims.append(simulation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate\n", - "The different scenarios are simulated in parallel threads " - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SEIR Model\n", - "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=8)]: Done 1 tasks | elapsed: 0.9s\n", - "[Parallel(n_jobs=8)]: Done 2 out of 2 | elapsed: 30.4s remaining: 0.0s\n", - "[Parallel(n_jobs=8)]: Done 2 out of 2 | elapsed: 30.4s finished\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "../SEIR/SEIR_vars.py:143: RuntimeWarning: invalid value encountered in double_scalars\n", - " self.SHFR = [self.totD[i]/(self.I_se_ac[i][-1]+self.I_cr_ac[i][-1]) for i in range(self.numescenarios)]\n", - "../SEIR/SEIR_vars.py:144: RuntimeWarning: invalid value encountered in true_divide\n", - " self.SHFR_d = [self.B[i]/(self.I_se_ac[i][-1]+self.I_cr_ac[i][-1]) for i in range(self.numescenarios)]\n" - ] - } - ], - "source": [ - "simulation.simulate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plots\n", - "This libraries have predefined plot functions which plot the main epidemiological variables.\n", - "Each function has the following optional arguments:\n", - "* days [int] Number of days to display\n", - "* showparams [bool] Display simulation parameters \n", - "* ylim [int] Limit the vertical axis\n", - "* norm [int/float] Normalize the results\n" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "enddate = datetime(2020,8,10)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# Accumulated infected\n", - "simulation.plotAccumulatedInfected(reales=True,days=0,enddate=enddate)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Epidemiological curves\n", - "simulation.plotseir(days = 500)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Active Infected curves\n", - "simulation.plotActiveInfected(showparams=True,days = 0, reales=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Daily infected\n", - "simulation.plotDailyInfected(days=100,reales=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Exposed\n", - "simulation.plotExposed()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Exposed\n", - "simulation.plotQuarantines(days=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[datetime.datetime(2020, 5, 15, 0, 0),\n", - " datetime.datetime(2020, 5, 18, 0, 0),\n", - " datetime.datetime(2020, 5, 22, 0, 0),\n", - " datetime.datetime(2020, 5, 25, 0, 0),\n", - " datetime.datetime(2020, 5, 29, 0, 0),\n", - " datetime.datetime(2020, 6, 1, 0, 0),\n", - " datetime.datetime(2020, 6, 5, 0, 0),\n", - " datetime.datetime(2020, 6, 8, 0, 0),\n", - " datetime.datetime(2020, 6, 12, 0, 0),\n", - " datetime.datetime(2020, 6, 15, 0, 0),\n", - " datetime.datetime(2020, 6, 19, 0, 0),\n", - " datetime.datetime(2020, 6, 23, 0, 0),\n", - " datetime.datetime(2020, 6, 28, 0, 0),\n", - " datetime.datetime(2020, 7, 1, 0, 0),\n", - " datetime.datetime(2020, 7, 5, 0, 0),\n", - " datetime.datetime(2020, 7, 10, 0, 0),\n", - " datetime.datetime(2020, 7, 13, 0, 0),\n", - " datetime.datetime(2020, 7, 17, 0, 0),\n", - " datetime.datetime(2020, 7, 20, 0, 0),\n", - " datetime.datetime(2020, 7, 24, 0, 0),\n", - " datetime.datetime(2020, 7, 27, 0, 0),\n", - " datetime.datetime(2020, 7, 31, 0, 0),\n", - " datetime.datetime(2020, 8, 3, 0, 0)]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulation.Ir_dates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Saturation Kinetics Dynamics:\n", - "The simulation object contains several methods and variables with its results" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Parallel Simulation function definition\n", - "num_cores = multiprocessing.cpu_count() \n", - "def ParallelSimulation(quarantines,k=0): \n", - " #quarantines = [[tsim, 0.85, alpha, qp, iqt, fqt, qt]] \n", - " simulation = SEIRmodel(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,tsim = tsim,tstate=tstate, k = k,initdate=initdate)\n", - " simulation.inputarray = np.array(quarantines)\n", - " simulation.addquarantine()\n", - " if not tstate:\n", - " simulation.initialvalues(I_act0,population,R=0)\n", - " simulation.simulate() \n", - " return simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "k = [0,5,10,15,20,25,30,35,40]#list(np.arange(0,kmax+step,step))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=8)]: Done 1 tasks | elapsed: 2.4s\n", - "[Parallel(n_jobs=8)]: Done 2 out of 9 | elapsed: 2.4s remaining: 8.3s\n", - "[Parallel(n_jobs=8)]: Done 3 out of 9 | elapsed: 2.4s remaining: 4.7s\n", - "[Parallel(n_jobs=8)]: Done 4 out of 9 | elapsed: 2.4s remaining: 3.0s\n", - "[Parallel(n_jobs=8)]: Done 5 out of 9 | elapsed: 2.4s remaining: 1.9s\n", - "[Parallel(n_jobs=8)]: Done 6 out of 9 | elapsed: 2.4s remaining: 1.2s\n", - "[Parallel(n_jobs=8)]: Done 7 out of 9 | elapsed: 2.4s remaining: 0.7s\n", - "[Parallel(n_jobs=8)]: Done 9 out of 9 | elapsed: 2.4s remaining: 0.0s\n" - ] - }, - { - "ename": "IndexError", - "evalue": "too many indices for array", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", - "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/parallel.py\", line 808, in dispatch_one_batch\n tasks = self._ready_batches.get(block=False)\n File \"/usr/lib/python3.8/queue.py\", line 167, in get\n raise Empty\n_queue.Empty\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 431, in _process_worker\n r = call_item()\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py\", line 285, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py\", line 593, in __call__\n return self.func(*args, **kwargs)\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/parallel.py\", line 252, in __call__\n return [func(*args, **kwargs)\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/parallel.py\", line 252, in \n return [func(*args, **kwargs)\n File \"\", line 10, in ParallelSimulation\n File \"../SEIR/SEIRmodel.py\", line 81, in simulate\n self.sims = model.simulate()\n File \"../SEIR/class_SEIR.py\", line 159, in simulate\n self.sims=Parallel(n_jobs=num_cores, verbose=50)(delayed(self.sim_run)(self.inputarray[i,0],self.inputarray[i,1],self.inputarray[i,2],self.inputarray[i,3],self.inputarray[i,4],self.inputarray[i,5],self.inputarray[i,6]) for i in range(self.inputarray.shape[0]))\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/parallel.py\", line 1029, in __call__\n if self.dispatch_one_batch(iterator):\n File \"/home/samuel/.local/lib/python3.8/site-packages/joblib/parallel.py\", line 819, in dispatch_one_batch\n islice = list(itertools.islice(iterator, big_batch_size))\n File \"../SEIR/class_SEIR.py\", line 159, in \n self.sims=Parallel(n_jobs=num_cores, verbose=50)(delayed(self.sim_run)(self.inputarray[i,0],self.inputarray[i,1],self.inputarray[i,2],self.inputarray[i,3],self.inputarray[i,4],self.inputarray[i,5],self.inputarray[i,6]) for i in range(self.inputarray.shape[0]))\nIndexError: too many indices for array\n\"\"\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Run Parallel Simulation s\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_cores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParallelSimulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquarantines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/.local/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1041\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1043\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1044\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 920\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 922\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 923\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 538\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 539\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 541\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 440\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 389\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: too many indices for array" - ] - } - ], - "source": [ - "# Run Parallel Simulation s\n", - "sims = [] \n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(quarantines=s1,k=i) for i in k)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Peak Values per each scenario\n", - "simulation.peak" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Peak time\n", - "simulation.peak_t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "simulation.showscenarios()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'13101'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tstate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/SerialTime/Serial Time Effects.ipynb b/research/SerialTime/Serial Time Effects.ipynb deleted file mode 100644 index 1916b26..0000000 --- a/research/SerialTime/Serial Time Effects.ipynb +++ /dev/null @@ -1,860 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Serial time dinamics\n", - "Estudiar efectos del tiempo serial en la dinámica:\n", - "1. Manteniendo el tiempo serial y el R0 (variando el beta), variar sigma y gama para ver cómo responde la dinámica del virus. \n", - "2. Manteniendo tiempo serial, gamma y beta, ver el efecto de variar sigma (1/tiempo de incubación) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import multiprocessing\n", - "from joblib import Parallel, delayed\n", - "import sys\n", - "from pathlib import Path\n", - "sys.path.insert(1, '../../src/SEIR/')\n", - "sys.path.insert(1, '../../src/SEIRHVD/')\n", - "sys.path.insert(1, '../../src/utils/')\n", - "\n", - "import numpy as np\n", - "from numpy import linalg as LA\n", - "\n", - "import platform\n", - "OS = platform.system()\n", - "\n", - "import matplotlib.pyplot as plt\n", - "if OS == 'Linux': \n", - " %matplotlib tk\n", - " print('Linux')\n", - "elif OS == 'Windows':\n", - " %matplotlib qt\n", - " print('Windows')\n", - "else:\n", - " print('OS not detected :-|')\n", - "\n", - "from class_SEIR2 import SEIR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parámetros\n", - "### Tiempo de incubación \n", - "$T_{inc} = \\frac{1}{\\sigma}$\n", - "### Tiempo de infecciosidad \n", - "$T_{inf} = \\frac{1}{\\gamma}$\n", - "### Tiempo Serial \n", - "$T_s = T_{inc} + \\frac{T_{inf}}{2}$\n", - "\n", - "### Numero reproductivo básico \n", - "$R_0 = \\frac{\\beta}{\\gamma}$\n", - "\n", - "# Ecuaciones\n", - "$\\dot{S} = -\\alpha\\beta \\frac{SI}{N} $ \n", - "$\\dot{E} = \\alpha\\beta \\frac{SI}{N} - \\sigma E$ \n", - "$\\dot{I} = \\sigma E -\\gamma I$ \n", - "$\\dot{R} = \\gamma I$ " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estudio efectos del tiempo de incubación\n", - "En estas simulaciones se simula para distintos tiempos de incubación manteniendo todas las otras variables fijas. Al variar el tiempo de incubación y mantener el tiempo de infecciosidad constante, el tiempo serial también variará" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation time\n", - "tsim = 1000\n", - "\n", - "# Parameters\n", - "R0 = 1.2\n", - "\n", - "\n", - "step = 0.5\n", - "\n", - "T_inf = 6\n", - "T_inc = np.array(np.arange(1,6,0.5))\n", - "T_s = T_inc + T_inf/2\n", - "gamma = 1/T_inf\n", - "sigma = 1/T_inc\n", - "\n", - "beta = R0*gamma " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Initial Conditions\n", - "mu = R0\n", - "population = 100000\n", - "I = 1\n", - "E = I*mu\n", - "R = 0\n", - "S = population - I - E - R" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def alpha(t):\n", - " return 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "sim = [SEIR(tsim=tsim,alpha=alpha,beta=beta,mu=mu,sigma=sigma[i],gamma=gamma,I0=I,I_ac0=I,I_d0=0,population=population) for i in range(len(T_inc))]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr()\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sim,i,tsim) for i in range(len(sim)))\n", - "sim = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(T_inc)):\n", - " #plt.plot(sim[i].t,sim[i].S,label='(S) T_inc: '+str(T_inc[i]))\n", - " #plt.plot(sim[i].t,sim[i].E,label='(E) T_inc: '+str(T_inc[i]))\n", - " plt.plot(sim[i].t,sim[i].I,label='(I) T_inc: '+str(T_inc[i]))\n", - " #plt.plot(sim[i].t,sim[i].R,label='(R) T_inf: '+str(T_inc[i]))\n", - "plt.title('Incubation time variation')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak = [sim[i].peak for i in range(len(T_inc))]\n", - "peak_t = [sim[i].peak_t for i in range(len(T_inc))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(T_inc,peak,label='Peak size vs Infectious period')\n", - "plt.title('Peak size vs Incubation time')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(T_inc,peak_t,label='Peak day')\n", - "plt.title('Peak day vs Incubation time')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_final = [sim[i].R[-1] for i in range(len(T_inc))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(T_inc,R_final,label='Infectados totales')\n", - "plt.title('R al final vs Incubation time')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tiempo serial y R fijo - Variacion sigma y gamma\n", - "Para mantener el R fijo, al variar gamama variamos beta proporcionalmente." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation time\n", - "tsim = 1000\n", - "\n", - "# Parameters\n", - "R0 = 1.2\n", - "T_s = 8\n", - "\n", - "step = 0.5\n", - "# gamma\n", - "T_inf = np.array(np.arange(1,7+step,step))\n", - "# sigma\n", - "T_inc = T_s-T_inf/1\n", - "gamma = 1/T_inf\n", - "sigma = 1/T_inc\n", - "\n", - "beta = R0*gamma " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Initial Conditions\n", - "mu = 0#R0\n", - "population = 100000\n", - "I = 1\n", - "E = I*mu\n", - "R = 0\n", - "S = population - I - E - R" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def alpha(t):\n", - " return 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "sim = [SEIR(tsim=tsim,alpha=alpha,beta=beta[i],mu=mu,sigma=sigma[i],gamma=gamma[i],I0=I,I_ac0=I,I_d0=0,population=population) for i in range(len(T_inf))]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr()\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sim,i,tsim) for i in range(len(sim)))\n", - "sim = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak = [sim[i].peak for i in range(len(T_inf))]\n", - "peak_t = [sim[i].peak_t for i in range(len(T_inf))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(T_inf)):\n", - " #plt.plot(sim[i].t,sim[i].S,label='(S) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].E,label='(E) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,np.log2(sim[i].I),label='(I) T_inf: '+str(T_inf[i]))\n", - " plt.plot(sim[i].t,sim[i].I_d,label='(I_d) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].I,label='(I) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].I_ac,label='(I_ac) T_inf: '+str(T_inf[i]),linestyle='dashed')\n", - " #plt.plot(sim[i].t,sim[i].R,label='(R) T_inf: '+str(T_inf[i]))\n", - "plt.title('I vs t: Sigma and Gamma variation')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.cumsum(sim[0].I)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Iacu = [np.cumsum(sim[i].I) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(T_inf,peak,label='Peak size vs Infectious period')\n", - "plt.title('Peak size vs Infectious period')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(T_inf,peak_t,label='Peak day')\n", - "plt.title('Peak day vs Infectious period')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "R_final = [sim[i].R[-1] for i in range(len(T_inf))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(T_inf,R_final,label='R final')\n", - "plt.title('Peak day vs Infectious period')\n", - "plt.legend(loc=0)\n", - "plt.plot(1,1)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estudio tiempo serial para R fijo\n", - "Manteniendo el R fijo, variar sigma y gamma. \n", - "Hacer plots de contorno de tamaño y fecha del peak para estas dimensiones" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation time\n", - "tsim = 1000\n", - "\n", - "# Parameters\n", - "R0 = 1.2\n", - "T_s = 8\n", - "\n", - "step = 0.5\n", - "T_inf = np.array(np.arange(1,10+step,step))\n", - "T_inc = np.array(np.arange(1,8+step,step))\n", - "gamma = 1/T_inf\n", - "sigma = 1/T_inc\n", - "beta = R0*gamma " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Initial Conditions\n", - "mu = R0\n", - "population = 100000\n", - "I = 100\n", - "E = I*mu\n", - "R = 0\n", - "S = population - I - E - R" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def alpha(t):\n", - " return 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "sim = [[SEIR(tsim=tsim,alpha=alpha,beta=beta[i],mu=mu,sigma=sigma[j],gamma=gamma[i],I0=I,I_ac0=I,I_d0=0,population=population) for j in range(len(T_inc))] for i in range(len(T_inf))]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr()\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "aux = []\n", - "for j in range(len(T_inc)):\n", - " aux.append(Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sim[i],j,tsim) for i in range(len(T_inf))))\n", - "#sim = aux\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sim = aux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak = [[sim[i][j].peak for i in range(len(T_inc))] for j in range(len(T_inf))]\n", - "peak_t = [[sim[i][j].peak_t for i in range(len(T_inc))] for j in range(len(T_inf))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.shape(peak)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Variando tiempo de infeccion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "j = 0\n", - "for i in range(len(T_inf)):\n", - " #plt.plot(sim[i].t,sim[i].S,label='(S) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].E,label='(E) T_inf: '+str(T_inf[i]))\n", - " plt.plot(sim[j][i].t,sim[j][i].I,label='(I) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].R,label='(R) T_inf: '+str(T_inf[i]))\n", - "plt.title('I vs t: T_inc = '+str(T_inc[j]))\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Variando tiempo de incubacion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "for j in range(len(T_inc)):\n", - " #plt.plot(sim[i].t,sim[i].S,label='(S) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].E,label='(E) T_inf: '+str(T_inf[i]))\n", - " plt.plot(sim[j][i].t,sim[j][i].I,label='(I) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].R,label='(R) T_inf: '+str(T_inf[i]))\n", - "plt.title('I vs t: T_inf = '+str(T_inf[i]))\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contour plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(T_inc,T_inf,peak) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak Size')\n", - "ax.set_xlabel('Incubation Time')\n", - "ax.set_ylabel('Infection Time')\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Contour Plot\n", - "fig,ax=plt.subplots(1,1)\n", - "cp = ax.contourf(T_inc,T_inf,peak_t) \n", - "fig.colorbar(cp) # Add a colorbar to a plot\n", - "ax.set_title('Peak day')\n", - "ax.set_xlabel('Incubation Time')\n", - "ax.set_ylabel('Infection Time')\n", - "plt.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Serial Time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "T_s = [[i + j/2 for i in T_inc] for j in T_inf]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "T_s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Estados iniciales" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation time\n", - "tsim = 1000\n", - "\n", - "# Parameters\n", - "R0 = 1.2\n", - "T_s = 8\n", - "\n", - "step = 0.5\n", - "# gamma\n", - "T_inf = 6\n", - "# sigma\n", - "T_inc = 3\n", - "gamma = 1/T_inf\n", - "sigma = 1/T_inc\n", - "\n", - "beta = R0*gamma " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Initial Conditions\n", - "mu = R0\n", - "population = 100000\n", - "I = np.array(np.arange(1,100,10))\n", - "E = I*mu\n", - "R = 0\n", - "S = population - I - E - R" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def alpha(t):\n", - " return 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "sim = [SEIR(tsim=tsim,alpha=alpha,beta=beta,mu=mu,sigma=sigma,gamma=gamma,I0=I[i],I_ac0=I[i],I_d0=0,population=population) for i in range(len(I))]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate(simulation,i,tsim):\n", - " simulation[i].integr()\n", - " return simulation[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_cores = multiprocessing.cpu_count()\n", - "sims = Parallel(n_jobs=num_cores, verbose=50)(delayed(simulate)(sim,i,tsim) for i in range(len(sim)))\n", - "sim = sims\n", - "print('ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peak = [sim[i].peak for i in range(len(I))]\n", - "peak_t = [sim[i].peak_t for i in range(len(I))]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(len(I)):\n", - " #plt.plot(sim[i].t,sim[i].S,label='(S) T_inf: '+str(T_inf[i]))\n", - " #plt.plot(sim[i].t,sim[i].E,label='(E) T_inf: '+str(T_inf[i]))\n", - " plt.plot(sim[i].t,np.log2(sim[i].I),label='(I) I0: '+str(I[i]))\n", - " #plt.plot(sim[i].t,sim[i].I,label='(I) I0: '+str(I[i]))\n", - " #plt.plot(sim[i].t,sim[i].R,label='(R) T_inf: '+str(T_inf[i]))\n", - "plt.title('I vs t: Sigma and Gamma variation')\n", - "plt.legend(loc=0)\n", - "plt.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ideas Bassi\n", - "$I_d(t) = R_e(t)^{\\frac{1}{T_S}}$ " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/cfg/SEIRHVD.toml b/research/cfg/SEIRHVD.toml deleted file mode 100644 index 7975da0..0000000 --- a/research/cfg/SEIRHVD.toml +++ /dev/null @@ -1,139 +0,0 @@ -# Pandemic Suite Configuration File - -title = 'Example of a SEIRHVD Configuration File' -date = '2021-06-01' -user = '' - -[model] - name = "SEIRHVD" - compartments = ['S','S_v','E','E_v','Im','Icr','Iv','R','H','D'] # Nombres de los estados - -[data] - # Real data will set initial conditions and will be available to be plotted and to calculate errors - datafile = false # File path for importing data from file - importdata = false # Import data from external source - - # parameters to use when importing from external sources - initdate = '' - country = '' - state = '' - county = ''#'13101' - loc_name= '' # optional - -[parameters] - [parameters.static] - t_init = 0 # Initial day - t_end = 500 # Final day - timestep = 0.01 # Timestep for solver - - # Saturation dynamic - k_I = 0 - k_R = 0 - - popfraction = 1 # Fraction of the total population that take part on the dynamic at the beginning - - mu = 0.8 # E(0) = mu*I(0) - - - [parameters.dynamic] - - # Contagion and movility - alpha = 1 - beta = 0.3 - beta_v = 0.05 - - # Vaccination - Psi = 0 # Vaccines per day - v = 0.7 # Vaccine effectivity - - # External flux rates over time - S_f = 0 - Sv_f = 0 - E_f = 0 - Ev_f = 0 - Iv_f = 0 - Im_f = 0 - Icr_f = 0 - H_f = 0 - D_f = 0 - R_f = 0 - - - # -------------------------- # - # Transition Parameters # - # -------------------------- # - # Exposed - pE_Im = 0.95 - tE_Im = 4.0 - pE_Icr = 0.05 - tE_Icr = 4.0 - - # Vaccinated exposed - tEv_Iv = 3.0 - - # Infectious (asymptomatic + mild + severe) - tIm_R = 5.0 - - # Infectious (critical) - tIcr_H = 5.0 - - # Infectious (vaccinated) - pIv_R = 0.99 - tIv_R = 12.0 - - pIv_H = 0.01 - tIv_H = 7.0 - - # Hospitalized (IMV) - pH_R = 0.7 - tH_R = 11 - - pH_D = 0.3 - tH_D = 11 - - # Immunity loss - pR_S = 0 # probability - tR_S = 90 # time - - # Subreport: Detected infected are calculated from the real infected given in the IC - pI_det = 1 - pIcr_det = 1 - pIm_det = 1 - pIv_det = 1 - - -[initialconditions] - - population = 1000000 - Sv = 0 - - # Non vaccinated infected - I = 6000 # Active Infected - I_d = 3000 # New daily Infected - I_ac = 15000 # Accumulated Infected - - # Vaccinated infected: If false they will be calculated using the vaccinated proportion of susceptibles - Iv = false - Iv_d = false - Iv_ac = false - - # Hospitalization - H_cap = 4000 # Capacity - H = 0 # Use - H_d = 0 # Use - - # Deaths - D = 0 - D_d = 0 - - # Recovered - R = 5000 - - # Leave E and E_d as false for using mu - E = false - E_d = false - Ev_ = false - Ev_d = false - - - diff --git a/research/plotspaper.py b/research/plotspaper.py deleted file mode 100644 index 0005cc4..0000000 --- a/research/plotspaper.py +++ /dev/null @@ -1,1032 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -# -------------------- # -# # -# SEIRHDV Paper # -# # -# -------------------- # -import sys -from pathlib import Path -sys.path.insert(1, '..src/SEIRHVD/') -sys.path.insert(1, 'src/SEIRHVD/') -from SEIRHVD_local import SEIRHVD_local -import numpy as np -from datetime import datetime -import matplotlib.pyplot as plt -import multiprocessing -from joblib import Parallel, delayed -from numpy import linalg as LA - -tstate = '' -initdate = datetime(2020,5,15) - - -num_cores = multiprocessing.cpu_count() -def ParallelSimulation(Htot,Vtot,alpha,k=0, qp = 0, qt = 0,iqt = 0,fqt = 500): - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop, k = k) - quarantines = [[tsim, 0.85, alpha, qp, iqt, fqt, qt]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot,Vtot,R=0,D=0,H_cr = 0) - simulation.simulate(v=3) - return simulation #, simulation.SHFR[0] - - - - -""" - # ------------------------------------------------- # - # # - # Análisis para total de Camas Disponibles # - # # - # ------------------------------------------------- # - - Datos: Fallecimientos relacionados a escaséz de camas - ejes: Camas H totales vs infectados severos vs - -""" - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0.6 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - - -# Simular -tsim = 1000 # Tiempo de simulacion -population = 1000000 -nm = int(population/100000) - -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 - -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - -# Hospital capacity -step = 6 -Htot_max = 30 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per1000 = list(range(0,30+step,step)) - -# VMI Capacity -Vtot = 10*nm - -#Movilty -# From 0 to 1 in steps of: -step = 0.05 -alpha = list(np.arange(0.5,0.7+step,step)) - -Nalpha = len(alpha) -NHtot = len(Htot) - - -# -------------------------------------------------- # -# Plot 1: Cuarentena total permanente # -# -------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = 500#tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0],label="Hospitalized") - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('MassAction - Total Quarantine') -fig.show() - -# ---------------------------------------------------- # -# Plot 2: Cuarentena dinamica 2 semanas # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0],label="Hospitalized") - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('MassAction - Dynamic Quarantine (2W)') -fig.show() - -# ------------------------------------------------- # -# Plot 3: Cuarentena Total SKF 30 # -# ------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0],label="Hospitalized") - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Saturated Kynetics -Total Quarantine') -fig.show() - - - -# ----------------------------------------------------------- # -# Plot 4: Cuarentena dinamica 2 semanas SKF 30 # -# ----------------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0],label="Hospitalized") - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Saturated Kynetics - Dynamic Quarantine (2W)') -fig.show() - - - - - - -""" - # ------------------------------------------------- # - # # - # Análisis para total de VMI Disponibles # - # # - # ------------------------------------------------- # - - Datos: Fallecimientos relacionados a escaséz de VMI - ejes: VMI totales vs infectados severos - -""" - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0.6 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 1000 # Tiempo de simulacion - -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 -population = 1000000 -nm = int(population/100000) - -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - - -Htot = 30#list(range(0*nm,30*nm+step,step)) - -# VMI Capacity -Vtot_max = 20 #per 100.000 persons -step = 6*nm -Vtot = list(range(0*nm,Vtot_max*nm+step,step)) - -#Mobilty - -# From 0 to 1 in steps of: -step = 0.25 -alpha = list(np.arange(0.5,0.7+step,step)) -Nalpha = len(alpha) -NVtot = len(Vtot) - - -# -------------------------------------------------- # -# Plot 1: Cuarentena total permanente # -# -------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims[i][j].I_cr[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('MassAction - Total Quarantine') -fig.show() - - -# ---------------------------------------------------- # -# Plot 2: Cuarentena dinamica 2 semanas # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('MassAction - Total Quarantine') -fig.show() - -# ------------------------------------------------- # -# Plot 3: Cuarentena Total SKF 30 # -# ------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('MassAction - Total Quarantine') -fig.show() - - -# ----------------------------------------------------------- # -# Plot 4: Cuarentena dinamica 2 semanas SKF 30 # -# ----------------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('MassAction - Total Quarantine') -fig.show() - - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis SHFR: Should habe been Hospitalized Fatality Rate # - # # - # ---------------------------------------------------------------------- # - - Datos: SHFR - ejes: VMI totales vs infectados severos - - - Contourplot de SHFR=Muertos acumulados/((Ise+Icr) acumulados) considerando movilidad (alpha) en el eje X, - y numero de camas en el eje Y - -""" - -tstate = '' -initdate = datetime(2020,5,15) - - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 1000 # Tiempo de simulacion - - -# Simular -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - - - -# Muertos iniciales -dead0 = 0 -population = 100000 -nm = int(population/100000) -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 -# UCI/UTI capacity per 100000 persons -Htot_max = 60 - -step = 2 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per100M = list(range(0,Htot_max+step,step)) -# VMI Capacity -Vtot = [i/2 for i in Htot] - -# Mobility -step = 0.05 -alpha = list(np.arange(0.05,1+step,step)) - - -# ----------------------------------------------------------------- # -# Plot 1: SHFR Mass Action Dynamics - Total Quarantine # -# ----------------------------------------------------------------- # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Mass Action Dynamics - Total Quarantine') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - -# ------------------------------------------------------------------------ # -# Plot 2: SHFR Mass Action Dynamics - Dynamic Quarantine (2W) # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Mass Action Dynamics - Dynamic Quarantine (2W) ') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - -# ------------------------------------------------------------------------ # -# Plot 3: SHFR Saturated Kynetics Dynamics - Total Quarantine # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Saturated Kynetics Dynamics - Total Quarantine') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - -# ------------------------------------------------------------------------ # -# Plot 4: SHFR Saturated Kynetics - Dynamic Quarantine (2W) # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Saturated Kynetics - Dynamic Quarantine (2W)') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis Mass Action vs Saturated Kynetics # - # # - # ---------------------------------------------------------------------- # - -# Comparacion dinamica Mass Action con Saturated Kinetics -#Grafico se calcula para un valor T de tiempo. Debe ser contourplor con ejes x= alpha, y=kappa, -# y color dado por funcion distancia euclideana entre Infectados kappa =0 y el distinto valor de kappa. -#Genera contourplots para 4 valores distintos de T: Entre inicio y peak, al peak, entre peak y el fin, y al fin de la dinamica. - -""" - -tstate = '' -initdate = datetime(2020,5,15) - - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 2000 # Tiempo de simulacion - -# Simular -# Activos Iniciales -I_act0 = 10 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 -population = 100000 -nm = int(population/100000) -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - -# UCI/UTI capacity -Htot = 50*nm -# VMI Capacity -Vtot = Htot/2 - -# Mobility -step = 0.05 -alpha = list(np.arange(0.5,0.75+step,step)) - -# Saturation Kinetics Factor -step = 2 -kmax = 10 -k = [0,5,10,20,30,40]#list(np.arange(0,kmax+step,step)) - - -# ------------------------------------------------- # -# Plot 1: MAD vs SKD -Total Quarantine # -# ------------------------------------------------- # - -qt = 0 -qp = 14 -iqt = 0 -fqt = tsim -sims = [] - -# Run Simulation -sims = [] -for i in k: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,Vtot,alpha=j,k=i,qp = qp, qt = qt, iqt = iqt, fqt =fqt) for j in alpha) - sims.append(aux) - - -# Euclidian Distance for each alpha for diferent T - -#ED = [] -#for i in range(len(k)): -# aux = [] -# for j in range(len(alpha)): -# aux.append(LA.norm(sims[i][j].I[0]-sims[0][j].I[0])) -# ED.append(aux) - - -# Peak size proportion - -peak = [] -for i in range(len(k)): - aux = [] - for j in range(len(alpha)): - aux.append(sims[i][j].peak[0]/sims[0][j].peak[0]) - peak.append(aux) - - -# Contour Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,k,peak) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('Peak Size Proportion') -ax.set_xlabel('Mobility') -ax.set_ylabel('Saturation Dynamics Factor') -plt.show() - - -# Grid plot -fig, axs = plt.subplots(len(k), len(alpha)) -for i in range(len(k)): - for j in range(len(alpha)): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I[0],label="Infected") - axs[i, j].set_title("K: "+str(k[i])+" | Alpha: "+str(alpha[j])) -fig.suptitle('Axes values are scaled individually by default') -#fig.tight_layout() -lines, labels = fig.axes[-1].get_legend_handles_labels() - -#fig.legend(lines, labels, loc = 'upper center') -fig.legend(lines, labels,loc = 'best') -fig.show() - - - -# SHFR - -SHFR = np.zeros((len(k),len(alpha))) -for i in range(len(k)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,k,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR') -ax.set_xlabel('Mobility') -ax.set_ylabel('Saturation Dynamics Factor') -plt.show() - - -# --------------------------------------------------- # -# Plot 2: MAD vs SKD -Dynamic Quarantine # -# --------------------------------------------------- # - - -# Run Simulation -qt = 1 -qp = 14 -iqt = 0 -fqt = tsim -sims = [] - -for i in k: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,Vtot,alpha=j,k=i,qp = qp, qt = qt, iqt = iqt, fqt =fqt) for j in alpha) - sims.append(aux) - - -# Euclidian Distance for each alpha for diferent T - -ED = [] -for i in range(len(k)): - aux = [] - for j in range(len(alpha)): - aux.append(LA.norm(sims[i][j].I[0]-sims[0][j].I[0])) - ED.append(aux) - - -# Peak size proportion - -peak = [] -for i in range(len(k)): - aux = [] - for j in range(len(alpha)): - aux.append(sims[i][j].peak[0]/sims[0][j].peak[0]) - peak.append(aux) - - -# Contour Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,k,peak) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('Peak Size Proportion') -ax.set_xlabel('Mobility') -ax.set_ylabel('Saturation Dynamics Factor') -plt.show() - - -# Grid plot -fig, axs = plt.subplots(len(k), len(alpha)) -for i in range(len(k)): - for j in range(len(alpha)): - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I[0],label="Infected") - axs[i, j].set_title("K: "+str(k[i])+" | Alpha: "+str(alpha[j])) -fig.suptitle('Axes values are scaled individually by default') -#fig.tight_layout() -lines, labels = fig.axes[-1].get_legend_handles_labels() - -#fig.legend(lines, labels, loc = 'upper center') -fig.legend(lines, labels,loc = 'best') -fig.show() - - - -# SHFR - -SHFR = np.zeros((len(k),len(alpha))) -for i in range(len(k)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,k,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR') -ax.set_xlabel('Mobility') -ax.set_ylabel('Saturation Dynamics Factor') -plt.show() - - - - -# Comparaciones de todos los graficos anteriores para mass action y saturated kinetics también con distintas cuarentenas -# Fittear datos chilenos - - - - - - - - - -""" -Codigo para posible reciclaje - - - -sims = [] -for i in Htot: - aux = [] - for j in alpha: - print("alpha") - # Creación del objeto de simulación - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop) - quarantines = [[tsim, 0.85, j, 0.0, 0.0, tsim, 0.0]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,i,Vtot,R=0,D=0,H_cr = 0) - simulation.simulate(v=3) - if i > 0: - aux.append(simulation) - else: - aux.append(simulation) - sims.append(aux) - -# Non parallel Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims = [] -for i in range(len(Htot)): - aux = [] - for j in range(len(alpha)): - # Creación del objeto de simulación - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate) - quarantines = [[tsim, 0.85, alpha[j], 0.0, 0.0, tsim, 0.0]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot[i],Vtot,R=0,D=0,H_cr = 0,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop) - simulation.simulate(v=3) - SHFR[i,j] = simulation.SHFR[0] - aux.append(simulation) - sims.append(aux) - - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_crD_d[0],label="H_cr to D") - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].VD_d[0],label="V to D") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_seD_d[0],label="I_seD") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) -fig.suptitle('Axes values are scaled individually by default') -#fig.tight_layout() -lines, labels = fig.axes[-1].get_legend_handles_labels() - -#fig.legend(lines, labels, loc = 'upper center') -fig.legend(lines, labels,loc = 'best') -fig.show() - - - - - - -sims = [] -for i in Vtot: - aux = [] - for j in alpha: - print("alpha") - # Creación del objeto de simulación - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop) - quarantines = [[tsim, 0.85, j, 0.0, 0.0, tsim, 0.0]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot,i,R=0,D=0,H_cr = 0) - simulation.simulate(v=3) - if i > 0: - aux.append(simulation) - else: - aux.append(simulation) - sims.append(aux) - - - -num_cores = multiprocessing.cpu_count() -def ParallelSimulation(Htot,Vtot,alpha): - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop) - quarantines = [[tsim, 0.85, alpha, 0.0, 0.0, tsim, 0.0]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot,Vtot,R=0,D=0,H_cr = 0) - simulation.simulate(v=3) - return simulation #, simulation.SHFR[0] - -""" \ No newline at end of file diff --git a/research/plotspaper2.py b/research/plotspaper2.py deleted file mode 100644 index 5d986aa..0000000 --- a/research/plotspaper2.py +++ /dev/null @@ -1,966 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -# -------------------- # -# # -# SEIRHDV Paper # -# # -# -------------------- # -import sys -from pathlib import Path -sys.path.insert(1, '..src/SEIRHVD/') -sys.path.insert(1, 'src/SEIRHVD/') -from SEIRHVD_local import SEIRHVD_local -import numpy as np -from datetime import datetime -import matplotlib.pyplot as plt -import multiprocessing -from joblib import Parallel, delayed -from numpy import linalg as LA - -tstate = '' -initdate = datetime(2020,5,15) - - -num_cores = multiprocessing.cpu_count() -def ParallelSimulation(Htot,Vtot,alpha,k=0, qp = 0, qt = 0,iqt = 0,fqt = 500): - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop, k = k) - quarantines = [[tsim, 0.85, alpha, qp, iqt, fqt, qt]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot,Vtot,R=0,D=0,H_cr = 0) - simulation.simulate(v=3) - return simulation #, simulation.SHFR[0] - - - - - -""" - # ------------------------------------------------- # - # # - # Busqueda de Tipping point de movilidad # - # # - # ------------------------------------------------- # - - - -""" - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -# Simular -tsim = 2000 # Tiempo de simulacion -population = 1000000 -nm = int(population/100000) - -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 - -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - -# Hospital capacity -Htot = 50*nm - -# VMI Capacity -Vtot = 30*nm - -#Movilty -# From 0 to 1 in steps of: -step = 0.01 -alpha = list(np.arange(0.3,0.35+step,step)) - -Nalpha = len(alpha) - -# Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -quarantines = [[tsim, 0.85, i, qp, iqt, fqt, qt] for i in alpha] -simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop, k = k) -simulation.inputarray = np.array(quarantines) -simulation.addquarantine() -simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot,Vtot,R=0,D=0,H_cr = 0) -simulation.simulate(v=3) - -simulation.plotinfectadosactivos() - - -print('El limite de movilidad para la fijacion de la cuarentena con beta = 0.2 es de alpha=0.33') - - - - -""" - # ------------------------------------------------- # - # # - # Busqueda de movilidad basal para 5 camas # - # # - # ------------------------------------------------- # - - -""" -tstate = '' -initdate = datetime(2020,5,15) - - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 1000 # Tiempo de simulacion - - -# Simular -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - - - -# Muertos iniciales -dead0 = 0 -population = 100000 -nm = int(population/100000) -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 -# UCI/UTI capacity per 100000 persons -Htot_max = 60 - -step = 2 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per100M = list(range(0,Htot_max+step,step)) -# VMI Capacity -Vtot = [i/2 for i in Htot] - -# Mobility -step = 0.05 -alpha = list(np.arange(0.05,1+step,step)) - - # Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Mass Action Dynamics - Total Quarantine') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - -print('The Basal movility for 5 beds is 0.3') - - - -""" - # ------------------------------------------------- # - # # - # Análisis para total de Camas Disponibles # - # # - # ------------------------------------------------- # - - Datos: Fallecimientos relacionados a escaséz de camas - ejes: Camas H totales vs infectados severos vs - -""" - -# Parametros del modelo -beta = 0.117 # Tasa de contagio -mu = 0.6 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - - -# Simular -tsim = 1000 # Tiempo de simulacion -population = 1000000 -nm = int(population/100000) - -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 - -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - -# Hospital capacity -step = 6 -Htot_max = 30 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per1000 = list(range(0,30+step,step)) -NHtot = len(Htot) -# VMI Capacity -Vtot = 10*nm - - -#Movilty -# From 0 to 1 in steps of: -alpha = [0.65,0.75,0.85] -Nalpha = len(alpha) - - - -# ---------------------------------------------------- # -# Plot 1: Cuarentena dinamica 7 dias # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 7 -qt = 1 -iqt = 0 -fqt = tsim - - -# Simulation - -sims = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims[i][j].I_se[0]+sims[i][j].H_bed[0]+sims[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims[i][j].t[0],sims[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Dynamic Quarantine 7 days') -fig.show() - - -# ---------------------------------------------------- # -# Plot 2: Cuarentena dinamica 14 dias # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - - -# Simulation - -sims2 = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims2.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims2[i][j].I_se[0]+sims2[i][j].H_bed[0]+sims2[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims[i][j].t[0],sims[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Dynamic Quarantine 14 days') -fig.show() - -# ---------------------------------------------------- # -# Plot 3: Cuarentena dinamica 21 dias # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 21 -qt = 1 -iqt = 0 -fqt = tsim - - -# Simulation -sims3 = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims3.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims3[i][j].I_se[0]+sims3[i][j].H_bed[0]+sims3[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = tsim - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].H_bed[0]+sims[i][j].I_se[0]+sims[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].H_bed[0],label="Hospitalized") - #axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Dynamic Quarantine 21 days') -fig.show() - - - - - - -""" - # ------------------------------------------------- # - # # - # Análisis para total de VMI Disponibles # - # # - # ------------------------------------------------- # - - Datos: Fallecimientos relacionados a escaséz de VMI - ejes: VMI totales vs infectados severos - -""" -# Parametros del modelo -beta = 0.117 # Tasa de contagio -mu = 0.6 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 1000 # Tiempo de simulacion - -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 -population = 1000000 -nm = int(population/100000) - -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - - -Htot = 30#list(range(0*nm,30*nm+step,step)) - -# VMI Capacity -Vtot_max = 20 #per 100.000 persons -step = 6*nm -Vtot = list(range(0*nm,Vtot_max*nm+step,step)) -NVtot = len(Vtot) - -#Movilty -# From 0 to 1 in steps of: -alpha = [0.65,0.75,0.85] -Nalpha = len(alpha) - - - -# ---------------------------------------------------- # -# Plot 1: Cuarentena Total # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation - -sims4 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims4.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims4[i][j].I_se[0]+sims4[i][j].H_bed[0]+sims4[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Total Quarantine') -fig.show() - - - -# ---------------------------------------------------- # -# Plot 2: Cuarentena dinamica 7 dias # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 7 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims5 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims5.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims5[i][j].I_se[0]+sims5[i][j].H_bed[0]+sims5[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Dyanmic Quarantine 7 days') -fig.show() - - -# ---------------------------------------------------- # -# Plot 3: Cuarentena dinamica 14 dias # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims6 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims6.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims6[i][j].I_se[0]+sims6[i][j].H_bed[0]+sims6[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Dyanmic Quarantine 14 days') -fig.show() - - -# ---------------------------------------------------- # -# Plot 4: Cuarentena dinamica 21 dias # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 7 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation - -sims7 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims7.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims7[i][j].I_se[0]+sims7[i][j].H_bed[0]+sims7[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(alpha[j])) - #axs[i, j].set_ylim([0,maxval*1.05]) - #axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Dyanmic Quarantine 21 days') -fig.show() - - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis SHFR(t) # - # # - # ---------------------------------------------------------------------- # - - """ - -tstate = '' -initdate = datetime(2020,5,15) - - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 1000 # Tiempo de simulacion - - -# Simular -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - - - -# Muertos iniciales -dead0 = 0 -population = 100000 -nm = int(population/100000) -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 -# UCI/UTI capacity per 100000 persons -Htot_max = 60 - -step = 2 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per100M = list(range(0,Htot_max+step,step)) -# VMI Capacity -Vtot = [i/2 for i in Htot] - -NHtot = len(Htot) - -#Movilty -# From 0 to 1 in steps of: -alpha = [0.65,0.75,0.85] -Nalpha = len(alpha) - - - -# ------------------------------------------------------------------------ # -# Plot 1: SHFR(t) Dynamic Quarantine 7 days # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 7 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims9 = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - sims9.append(aux) - -maxval = max([max([max(sims9[i][j].I_se[0]+sims9[i][j].H_bed[0]+sims9[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(len(alpha)): - axs[i, j].plot(sims9[i][j].t[0],sims9[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR(t) - Dynamic Quarantine 7 days') -fig.show() - - - - -# ------------------------------------------------------------------------ # -# Plot 2: SHFR Dynamic Quarantine 14 days # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims10 = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - sims10.append(aux) - -maxval = max([max([max(sims10[i][j].I_se[0]+sims10[i][j].H_bed[0]+sims10[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(len(alpha)): - axs[i, j].plot(sims10[i][j].t[0],sims10[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR - Dynamic Quarantine 7 days') -fig.show() - - - -# ------------------------------------------------------------------------ # -# Plot 3: SHFR Dynamic Quarantine 21 days # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -SHFR = np.zeros((len(Htot),len(alpha))) -sims11 = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - sims11.append(aux) - -maxval = max([max([max(sims11[i][j].I_se[0]+sims11[i][j].H_bed[0]+sims11[i][j].I_seD_d[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(len(alpha)): - axs[i, j].plot(sims11[i][j].t[0],sims11[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(alpha[j])) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR - Dynamic Quarantine 7 days') -fig.show() - - - - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis SHFR final simulacion # - # # - # ---------------------------------------------------------------------- # - - Datos: SHFR - ejes: VMI totales vs infectados severos - - - Contourplot de SHFR=Muertos acumulados/((Ise+Icr) acumulados) considerando movilidad (alpha) en el eje X, - y numero de camas en el eje Y - -""" - -tstate = '' -initdate = datetime(2020,5,15) - - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -tsim = 1000 # Tiempo de simulacion - - -# Simular -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - - - -# Muertos iniciales -dead0 = 0 -population = 100000 -nm = int(population/100000) -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 -# UCI/UTI capacity per 100000 persons -Htot_max = 60 - -step = 2 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per100M = list(range(0,Htot_max+step,step)) -# VMI Capacity -Vtot = [i/2 for i in Htot] - -# Mobility -step = 0.05 -alpha = list(np.arange(0.05,1+step,step)) - - - -# ------------------------------------------------------------------------ # -# Plot 1: SHFR Dynamic Quarantine 7 days # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 7 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation - -sims12 = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims12.append(aux) - -SHFR = np.zeros((len(Htot),len(alpha))) -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims12[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Mass Action Dynamics - Dynamic Quarantine 7 days ') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - - -# ------------------------------------------------------------------------ # -# Plot 2: SHFR Dynamic Quarantine 14 days # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation - -sims13 = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims13.append(aux) - -SHFR = np.zeros((len(Htot),len(alpha))) -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims13[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Mass Action Dynamics - Dynamic Quarantine 14 days) ') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - -# ------------------------------------------------------------------------ # -# Plot 3: SHFR Dynamic Quarantine 21 days # -# ------------------------------------------------------------------------ # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation - -sims14 = [] -for i in range(len(Htot)): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],alpha[j],k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in range(len(alpha))) - #aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in alpha) - sims14.append(aux) - - -SHFR = np.zeros((len(Htot),len(alpha))) -for i in range(len(Htot)): - for j in range(len(alpha)): - SHFR[i][j] = sims14[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(alpha,Htot_per100M,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR Mass Action Dynamics - Dynamic Quarantine 21 days) ') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() diff --git a/research/plotspaper3.py b/research/plotspaper3.py deleted file mode 100644 index 7d892d5..0000000 --- a/research/plotspaper3.py +++ /dev/null @@ -1,1118 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -# -------------------- # -# # -# SEIRHDV Paper # -# # -# -------------------- # - -Generación de Plots Finales -- beta: 0.2 -- init: infectados 100 todos asintomaticos -- mu: 0 -- - -- Movilidad basal 0.3 -- Movilidad superior: 0.65,0.70, 0.75 -- Cuarentena total, cuarentena dinamica 2 semanas -- mass action, saturated kinetics con SKF 30 - -Grillas -- Camas -- Ventiladores -- SHFR(t) - -Contour: -- SHFR -- MassAction vs SK - -""" -import sys -from pathlib import Path -sys.path.insert(1, '../src/SEIRHVD/') -sys.path.insert(1, 'src/SEIRHVD/') -from SEIRHVD_local import SEIRHVD_local -import numpy as np -from datetime import datetime -import matplotlib.pyplot as plt -import multiprocessing -from joblib import Parallel, delayed -from numpy import linalg as LA -import os - -tstate = '' -initdate = datetime(2020,5,15) - -beep = lambda x: os.system("echo -n '\a';sleep 0.2;" * x) - -num_cores = multiprocessing.cpu_count() -def ParallelSimulation(Htot,Vtot,max_mov = 0.85,rem_mov = 0.65,k=0, qp = 0, qt = 0,iqt = 0,fqt = 500): - simulation = SEIRHVD_local(beta = beta,mu = mu,ScaleFactor=ScaleFactor,SeroPrevFactor=SeroPrevFactor,expinfection=expinfection,initdate = initdate, tsim = tsim,tstate=tstate,I_as_prop = I_as_prop, I_mi_prop = I_mi_prop,I_se_prop = I_se_prop,I_cr_prop = I_cr_prop, k = k) - quarantines = [[tsim, max_mov, rem_mov, qp, iqt, fqt, qt]] - simulation.inputarray = np.array(quarantines) - simulation.addquarantine() - simulation.initialvalues(I_act0,dead0,population,H0,V0,Htot,Vtot,R=0,D=0,H_cr = 0) - simulation.simulate(v=3) - return simulation #, simulation.SHFR[0] - - -# ------------------------------------- # -# # -# Parametros Epi Generales Paper # -# # -# ------------------------------------- # - -# Parametros del modelo -beta = 0.2 # Tasa de contagio -mu = 0 # Razon E0/I0 -ScaleFactor = 1 # Factor de Escala: Numero de infectados por sobre los reportados -SeroPrevFactor = 1 # Sero Prevalence Factor. Permite ajustar la cantidad de gente que entra en la dinamica -expinfection = 1 # Proporcion en la que contagian los expuestos - -# Activos Iniciales -I_act0 = 100 -I_as_prop = 1 -I_mi_prop = 0 -I_se_prop = 0 -I_cr_prop = 0 - -# Muertos iniciales -dead0 = 0 -population = 1000000 -nm = int(population/100000) - -# Initial Hospitalized -H0 = 0 -# Initial VMI -V0 = 0 - -# Tiempo de simulacion -tsim = 1000 - -# Rem Mov: -rem_mov = 0.3 - -# Max Mov: -max_mov = [0.65,0.7,0.75] - - - -""" - # ------------------------------------------------- # - # # - # Análisis para total de Camas Disponibles # - # # - # ------------------------------------------------- # - - Datos: Fallecimientos relacionados a escaséz de camas - ejes: Camas H totales vs infectados severos vs - -""" - -# Hospital capacity -step = 8 -Htot_max = 40 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per1000 = list(range(0,30+step,step)) -NHtot = len(Htot) - -# VMI Capacity -Vtot = 10*nm - -#Movilty -# From 0 to 1 in steps of: -max_mov = [0.3,0.65,0.75,0.85] -rem_mov = 0.3 -Nalpha = len(max_mov) - - -# ---------------------------------------------------- # -# Cuarentena Total - Mass Action # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - - -# Simulation - -sims1 = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims1.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims1[i][j].I_se[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = 300 - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims1[i][j].t[0],sims1[i][j].H_bed[0]+sims1[i][j].I_se[0]+sims1[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims1[i][j].t[0],sims1[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims1[i][j].t[0],sims1[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims1[i][j].t[0],sims1[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot per 100k : "+str(int(Htot[i]/10))+" | Mov: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Total Quarantine') -fig.show() - -# Analysis -I_se_peak1 = np.zeros((NHtot,Nalpha)) -I_seD_peak1 = np.zeros((NHtot,Nalpha)) -peak_day1 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_se_peak1[i][j] = max(sims1[i][j].I_se[0]) - I_seD_peak1[i][j] = max(sims1[i][j].I_seD_d[0]) - peak_day1[i][j] = sims1[i][j].peak_t[0] - - - -# ---------------------------------------------------- # -# Cuarentena Total - Saturated kinetics # -# ---------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - - -# Simulation - -sims2 = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims2.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims2[i][j].I_se[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = 300 - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].H_bed[0]+sims2[i][j].I_se[0]+sims2[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims2[i][j].t[0],sims2[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot per 100k : "+str(int(Htot[i]/10))+" | Mov: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Total Quarantine - SKF=30') -fig.show() - -# Analysis -I_se_peak2 = np.zeros((NHtot,Nalpha)) -I_seD_peak2 = np.zeros((NHtot,Nalpha)) -peak_day2 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_se_peak2[i][j] = max(sims2[i][j].I_se[0]) - I_seD_peak2[i][j] = max(sims2[i][j].I_seD_d[0]) - peak_day2[i][j] = sims2[i][j].peak_t[0] - - - -# --------------------------------------------------------------- # -# Cuarentena Dinamica 14 dias - Mass Action # -# --------------------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - - -# Simulation - -sims3 = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,max_mov = j, rem_mov = rem_mov,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims3.append(aux) - - -# Plots Generation: -maxval = max([max([max(sims3[i][j].I_se[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = 500 - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].H_bed[0]+sims3[i][j].I_se[0]+sims3[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims3[i][j].t[0],sims3[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot per 100k : "+str(int(Htot[i]/10))+" | Mov: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Total Quarantine') -fig.show() - -# Analysis -I_se_peak3 = np.zeros((NHtot,Nalpha)) -I_seD_peak3 = np.zeros((NHtot,Nalpha)) -peak_day3 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_se_peak3[i][j] = max(sims3[i][j].I_se[0]) - I_seD_peak3[i][j] = max(sims3[i][j].I_seD_d[0]) - peak_day3[i][j] = sims3[i][j].peak_t[0] - - - - - -# --------------------------------------------------------------- # -# Cuarentena Dinamica 14 dias - Saturated kinetics # -# --------------------------------------------------------------- # - -# Saturated kinetics Factor -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - - -# Simulation - -sims4 = [] -for i in Htot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(i,Vtot,max_mov = j, rem_mov = rem_mov,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims4.append(aux) - - - - - -# Plots Generation: -maxval = max([max([max(sims4[i][j].I_se[0]) for j in range(Nalpha)]) for i in range(NHtot)]) -xlim = 1000 - - -fig, axs = plt.subplots(NHtot, Nalpha) -for i in range(NHtot): - for j in range(Nalpha): - #axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].H_bed[0]+sims4[i][j].I_se[0]+sims4[i][j].I_seD_d[0],label="Total Severe") # No me convence este grafico - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].H_bed[0],label="Hospitalized") - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].I_se[0],label="I_se") - axs[i, j].plot(sims4[i][j].t[0],sims4[i][j].I_seD_d[0],label="Severe to Death") - axs[i, j].set_title("Htot per 100k : "+str(int(Htot[i]/10))+" | Mov: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('Bed Analysis - Total Quarantine - SKF = 30') -fig.show() - -# Analysis -I_se_peak4 = np.zeros((NHtot,Nalpha)) -I_seD_peak4 = np.zeros((NHtot,Nalpha)) -peak_day4 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_se_peak4[i][j] = max(sims4[i][j].I_se[0]) - I_seD_peak4[i][j] = max(sims4[i][j].I_seD_d[0]) - peak_day4[i][j] = sims4[i][j].peak_t[0] - - - - - - - -""" - # ------------------------------------------------- # - # # - # Análisis para total de VMI Disponibles # - # # - # ------------------------------------------------- # - - Datos: Fallecimientos relacionados a escaséz de VMI - ejes: VMI totales vs infectados severos - -""" -Htot = 30#list(range(0*nm,30*nm+step,step)) - -# VMI Capacity -Vtot_max = 20 #per 100.000 persons -step = 4*nm -Vtot = list(range(0*nm,Vtot_max*nm+step,step)) -NVtot = len(Vtot) - -#Mobilty -# From 0 to 1 in steps of: -max_mov = [0.3,0.65,0.75,0.85] -rem_mov = 0.3 -Nalpha = len(max_mov) - - -# ---------------------------------------------------- # -# Cuarentena Total - Mass Action # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation -sims5 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims5.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims5[i][j].I_cr[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = 350 - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims5[i][j].t[0],sims5[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Total Quarantine') -fig.show() - - -# Analysis -I_cr_peak5 = np.zeros((NHtot,Nalpha)) -I_crD_peak5 = np.zeros((NHtot,Nalpha)) -peak_day5 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_cr_peak5[i][j] = max(sims5[i][j].I_cr[0]) - I_crD_peak5[i][j] = max(sims5[i][j].I_crD_d[0]) - peak_day5[i][j] = sims5[i][j].peak_t[0] - - -# ---------------------------------------------------- # -# Cuarentena Total - Saturated kinetics # -# ---------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Simulation -sims6 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims6.append(aux) - -# Plots Generation: -maxval = max([max([max(sims6[i][j].I_cr[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims6[i][j].t[0],sims6[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Total Quarantine - SKF = 30') -fig.show() - -# Analysis -I_cr_peak6 = np.zeros((NHtot,Nalpha)) -I_crD_peak6 = np.zeros((NHtot,Nalpha)) -peak_day6 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_cr_peak6[i][j] = max(sims6[i][j].I_cr[0]) - I_crD_peak6[i][j] = max(sims6[i][j].I_crD_d[0]) - peak_day6[i][j] = sims6[i][j].peak_t[0] - - -# ---------------------------------------------------- # -# Dynamic Quarantine 14 days - Mass Action # -# ---------------------------------------------------- # - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation -sims7 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,max_mov = j, rem_mov = rem_mov,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims7.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims7[i][j].I_cr[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = 500 - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims7[i][j].t[0],sims7[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Dynamic Quarantine 14 days') -fig.show() - - -# Analysis -I_cr_peak7 = np.zeros((NHtot,Nalpha)) -I_crD_peak7 = np.zeros((NHtot,Nalpha)) -peak_day7 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_cr_peak7[i][j] = max(sims7[i][j].I_cr[0]) - I_crD_peak7[i][j] = max(sims7[i][j].I_crD_d[0]) - peak_day7[i][j] = sims7[i][j].peak_t[0] - - - - - - -# ---------------------------------------------------------------- # -# Dynamic Quarantine 14 days - Saturated kinetics # -# ---------------------------------------------------------------- # - -# Mass Action -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Simulation -sims8 = [] -for i in Vtot: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,i,max_mov = j, rem_mov = rem_mov,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims8.append(aux) - -# Plots Generation: - -maxval = max([max([max(sims8[i][j].I_cr[0]) for j in range(Nalpha)]) for i in range(NVtot)]) -xlim = tsim - -fig, axs = plt.subplots(NVtot, Nalpha) - -for i in range(NVtot): - for j in range(Nalpha): - axs[i, j].plot(sims8[i][j].t[0],sims8[i][j].I_cr[0],label="Critical Infected") - axs[i, j].plot(sims8[i][j].t[0],sims8[i][j].VD_d[0],label="Daily VMI to Death") - axs[i, j].plot(sims8[i][j].t[0],sims8[i][j].I_crD_d[0],label="Daily Critical Infected to Death") - axs[i, j].plot(sims8[i][j].t[0],sims8[i][j].H_crin[0],label="Critical Hospitalized") - axs[i, j].plot(sims8[i][j].t[0],sims8[i][j].V[0],label="VMI") - axs[i, j].set_title("Vtot: "+str(Vtot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,maxval*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('VMI Analysis - Dynamic Quarantine 14 days - SKF = 30') -fig.show() - -# Analysis -I_cr_peak6 = np.zeros((NHtot,Nalpha)) -I_crD_peak6 = np.zeros((NHtot,Nalpha)) -peak_day6 = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - I_cr_peak6[i][j] = max(sims6[i][j].I_cr[0]) - I_crD_peak6[i][j] = max(sims6[i][j].I_crD_d[0]) - peak_day6[i][j] = sims6[i][j].peak_t[0] - - - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis SHFR(t) # - # # - # ---------------------------------------------------------------------- # - - """ - -# Hospital capacity -step = 8 -Htot_max = 40 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per1000 = list(range(0,30+step,step)) -NHtot = len(Htot) - - -# VMI Capacity -Vtot = [i/2 for i in Htot] - - - -#Mobilty -# From 0 to 1 in steps of: -max_mov = [0.3,0.65,0.75,0.85] -rem_mov = 0.3 -Nalpha = len(max_mov) - -# ---------------------------------------------------------------------------- # -# SHFR(t) - Total Quarantine - SKF = 0 # -# ---------------------------------------------------------------------------- # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -sims9 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims9.append(aux) - -beep(10) - -xlim = 400 -ylim = 1 - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(Nalpha): - axs[i, j].plot(sims9[i][j].t[0],sims9[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,ylim*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR(t) - Total Quarantine - SFK = 0') -fig.show() - - -# ---------------------------------------------------------------------------- # -# SHFR(t) - Total Quarantine - SKF = 30 # -# ---------------------------------------------------------------------------- # - - # Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -sims10 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims10.append(aux) - -beep(10) - -xlim = 400 -ylim = 1 - - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(Nalpha): - axs[i, j].plot(sims10[i][j].t[0],sims10[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,ylim*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR(t) - Total Quarantine - SFK = 30') -fig.show() - - - -# ------------------------------------------------------------------------ # -# SHFR(t) Dynamic Quarantine 14 days - SKF = 0 # -# ------------------------------------------------------------------------ # - - -# Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -sims11 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = rem_mov,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims11.append(aux) - -beep(10) - -xlim = 400 -ylim = 1 - - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(Nalpha): - axs[i, j].plot(sims11[i][j].t[0],sims11[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,ylim*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR(t) - Dynamic Quarantine - SFK = 0') -fig.show() - - - -# ------------------------------------------------------------------------ # -# SHFR(t) Dynamic Quarantine 14 days - SKF = 30 # -# ------------------------------------------------------------------------ # - - -# Mass Action -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -sims12 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = rem_mov,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims12.append(aux) - -beep(10) - -xlim = 400 -ylim = 1 - - -fig, axs = plt.subplots(NHtot, Nalpha) - -for i in range(len(Htot)): - for j in range(Nalpha): - axs[i, j].plot(sims12[i][j].t[0],sims12[i][j].SHFR_d[0],label="SHFR") - axs[i, j].set_title("Htot: "+str(Htot[i])+" | Alpha: "+str(max_mov[j])) - axs[i, j].set_ylim([0,ylim*1.05]) - axs[i, j].set_xlim([0,xlim]) - -lines, labels = fig.axes[-1].get_legend_handles_labels() -fig.legend(lines, labels,loc = 'best') -fig.suptitle('SHFR(t) - Dynamic Quarantine - SFK = 30') -fig.show() - - - - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis SHFR final simulacion # - # # - # ---------------------------------------------------------------------- # - - Datos: SHFR - ejes: VMI totales vs infectados severos - - - Contourplot de SHFR=Muertos acumulados/((Ise+Icr) acumulados) considerando movilidad (alpha) en el eje X, - y numero de camas en el eje Y - -""" - -tsim = 2000 -# Hospital capacity -step = 5 -Htot_max = 50 -Htot = list(range(0*nm,Htot_max*nm+step*nm,step*nm)) -Htot_per100k = [int(Htot[i]/nm) for i in range(len(Htot))] -NHtot = len(Htot) - - -# VMI Capacity -Vtot = [i/2 for i in Htot] - - -#Mobilty -# From 0 to 1 in steps of: -max_mov = [0.3,0.65,0.75,0.85] -step =0.05 -max_mov= list(np.arange(0.05,1+step,step)) -rem_mov = 0.3 -Nalpha = len(max_mov) - - -# ---------------------------------------------------------------------------- # -# SHFR - Total Quarantine - SKF = 0 # -# ---------------------------------------------------------------------------- # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -sims13 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims13.append(aux) - -beep(10) - -SHFR = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - SHFR[i][j] = sims13[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(max_mov,Htot_per100k,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR - Total Quarantine - SKF = 0') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - -# ---------------------------------------------------------------------------- # -# SHFR - Total Quarantine - SKF = 30 # -# ---------------------------------------------------------------------------- # - - # Mass Action -k = 30 -# Total constant quarantine -qp = 0 -qt = 0 -iqt = 0 -fqt = tsim - -# Run Simulation -sims14 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims14.append(aux) - -beep(10) - -SHFR = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - SHFR[i][j] = sims14[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(max_mov,Htot_per100k,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR - Total Quarantine - SKF = 30 ') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - - - -# ---------------------------------------------------------------------------- # -# SHFR - Dynamic Quarantine 14 days - SKF = 0 # -# ---------------------------------------------------------------------------- # - - # Mass Action -k = 0 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -sims15 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims15.append(aux) - -beep(10) - -SHFR = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - SHFR[i][j] = sims15[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(max_mov,Htot_per100k,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR - Dynamic Quarantine 14 days - SKF = 0') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - - - -# ---------------------------------------------------------------------------- # -# SHFR - Dynamic Quarantine 14 days - SKF = 30 # -# ---------------------------------------------------------------------------- # - - # Mass Action -k = 30 -# Total constant quarantine -qp = 14 -qt = 1 -iqt = 0 -fqt = tsim - -# Run Simulation -sims16 = [] -for i in range(NHtot): - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot[i],Vtot[i],max_mov = j, rem_mov = j,k=k,qp = qp, qt = qt, iqt = iqt, fqt = fqt) for j in max_mov) - sims16.append(aux) - -beep(10) - -SHFR = np.zeros((NHtot,Nalpha)) -for i in range(NHtot): - for j in range(Nalpha): - SHFR[i][j] = sims16[i][j].SHFR[0] - - - -# Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(max_mov,Htot_per100k,SHFR) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('SHFR - Dynamic Quarantine 14 days - SKF = 30') -ax.set_xlabel('Mobility') -ax.set_ylabel('Beds per 100.000') -plt.show() - - - - - - - - - - -""" - # ---------------------------------------------------------------------- # - # # - # Análisis Mass Action vs Saturated Kynetics # - # # - # ---------------------------------------------------------------------- # - -# Comparacion dinamica Mass Action con Saturated Kinetics -# Grafico se calcula para un valor T de tiempo. Debe ser contourplor con ejes x= alpha, y=kappa, -# y color dado por funcion distancia entre Peak de Infectados para los valores de kappa respecto al de kappa =0 - - -""" -tsim = 2000 -# UCI/UTI capacity -Htot = 50*nm -# VMI Capacity -Vtot = Htot/2 - -# Movility -step = 0.05 -max_mov = list(np.arange(0.3,0.85+step,step)) - -# Saturation Kinetics Factor -k = [0,5,10,20,30,40]#list(np.arange(0,kmax+step,step)) - - -# ------------------------------------------------- # -# Plot 1: MAD vs SKD -Total Quarantine # -# ------------------------------------------------- # - -qt = 0 -qp = 0 -iqt = 0 -fqt = tsim -sims17 = [] - -# Run Simulation -sims17 = [] -for i in k: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,Vtot,max_mov=j,rem_mov = j,k=i,qp = qp, qt = qt, iqt = iqt, fqt =fqt) for j in max_mov) - sims17.append(aux) - -beep(10) - -# Peak size proportion -peak = [] -for i in range(len(k)): - aux = [] - for j in range(len(max_mov)): - aux.append(sims17[i][j].peak[0]/sims17[0][j].peak[0]) - peak.append(aux) - - -# Contour Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(max_mov,k,peak) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('MAD vs SKD - Total Quarantine') -ax.set_xlabel('Mobility') -ax.set_ylabel('Saturation Dynamics Factor ') -plt.show() - - -# Grid plot -fig, axs = plt.subplots(len(k), len(max_mov)) -for i in range(len(k)): - for j in range(len(max_mov)): - axs[i, j].plot(sims17[i][j].t[0],sims17[i][j].I[0],label="Infected") - axs[i, j].set_title("K: "+str(k[i])+" | Alpha: "+str(round(max_mov[j],2))) -fig.suptitle('MAD vs SKD | Htot = 50 | Total Quarantine') -#fig.tight_layout() -lines, labels = fig.axes[-1].get_legend_handles_labels() - -#fig.legend(lines, labels, loc = 'upper center') -fig.legend(lines, labels,loc = 'best') -fig.show() - - - -# --------------------------------------------------------- # -# Plot 2: MAD vs SKD -Dynamic Quarantine 14days # -# --------------------------------------------------------- # - -qt = 1 -qp = 14 -iqt = 0 -fqt = tsim -sims18 = [] - -# Run Simulation -sims18 = [] -for i in k: - aux = Parallel(n_jobs=num_cores, verbose=50)(delayed(ParallelSimulation)(Htot,Vtot,max_mov=j,rem_mov = rem_mov,k=i,qp = qp, qt = qt, iqt = iqt, fqt =fqt) for j in max_mov) - sims18.append(aux) - -beep(10) - -# Peak size proportion -peak = [] -for i in range(len(k)): - aux = [] - for j in range(len(max_mov)): - aux.append(sims18[i][j].peak[0]/sims18[0][j].peak[0]) - peak.append(aux) - - -# Contour Plot -fig,ax=plt.subplots(1,1) -cp = ax.contourf(max_mov,k,peak) -fig.colorbar(cp) # Add a colorbar to a plot -ax.set_title('Peak Size Proportion - Dynamic Quarantine 14 Days') -ax.set_xlabel('Mobility') -ax.set_ylabel('Saturation Dynamics Factor') -plt.show() - - -# Grid plot -fig, axs = plt.subplots(len(k), len(max_mov)) -for i in range(len(k)): - for j in range(len(max_mov)): - axs[i, j].plot(sims18[i][j].t[0],sims18[i][j].I[0],label="Infected") - axs[i, j].set_title("K: "+str(k[i])+" | Alpha: "+str(max_mov[j])) -fig.suptitle('MAD vs SKD | Htot = 50 | Dynamic Quarantine 14 Days') -#fig.tight_layout() -lines, labels = fig.axes[-1].get_legend_handles_labels() - -#fig.legend(lines, labels, loc = 'upper center') -fig.legend(lines, labels,loc = 'best') -fig.show() -