extract_tail_angle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "from pathlib import Path\n",
    "import argparse\n",
    "from scipy.spatial import distance\n",
    "from math import factorial, atan2, degrees, acos, sqrt, pi\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.convolution import convolve, Gaussian1DKernel, Box1DKernel\n",
    "from scipy.signal  import find_peaks\n",
    "%matplotlib inline\n",
    "from Skeleton_functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "skeleton = [['body', 'tail_1'], ['tail_1', 'tail_2'], ['tail_2', 'tail_3'], ['tail_3', 'tail_4'], ['tail_4','tail_5'], ['tail_5', 'tail_6'], ['tail_6','tail_tip']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_folder = \"../head_fixed_training_data/Processed_data/\"\n",
    "Prefix_list = [\"190712_DC_WT_GR_7dpf_F2\", \"190712_DC_WT_GR_7dpf_F3\", \"190712_DC_WT_NR_7dpf_F1\", \"190719_DC_WT_NR_7dpf_F1\"]\n",
    "Prefix = Prefix_list [3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dataframe = pd.read_hdf(Data_folder + Prefix + \"/\" + Prefix + \"_stimDLC_resnet50_runningSep18shuffle1_250000.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.signal import butter, lfilter, freqz\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "def butter_lowpass(cutoff, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    normal_cutoff = cutoff / nyq\n",
    "    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
    "    return b, a\n",
    "\n",
    "\n",
    "def butter_lowpass_filter(data, cutoff, fs, order=5):\n",
    "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
    "    y = lfilter(b, a, data)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "angle = get_tail_angle(Dataframe = Dataframe, skel =skeleton)\n",
    "np.save(arr=angle, file=Data_folder + Prefix + \"/\" + Prefix + \"_tail_angle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def segement_responses(trace):\n",
    "    return(np.array(convolve( (trace **4), Gaussian1DKernel(10))> 2000))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def segement_responses(trace):\n",
    "#    return(np.array(convolve(np.diff(trace)**2, Gaussian1DKernel(10))> 2000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs_mask = segement_responses(np.array(angle))\n",
    "#segs_mask = erode(segs_mask, size = 10, thresh = 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trace_to_segments(segs_mask, angle):\n",
    "    segs = np.where(np.diff(segs_mask != 0))[0]+1\n",
    "    mask_segments = np.split(ary = np.array(segs_mask), indices_or_sections=segs)\n",
    "    response = np.array([np.max(x) for x in mask_segments])\n",
    "    angle_segments = np.array(np.split(ary = angle, indices_or_sections=segs))[response]\n",
    "    return(angle_segments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def angle_in_segment(a):\n",
    "    peaks, _ = find_peaks(a, distance=5, height=5, width=2, prominence=4)\n",
    "    mins, _ = find_peaks(a*-1, distance=5, height=5, width=2, prominence =4)\n",
    "    if len(peaks)!= 0 and len(mins) == 0:\n",
    "        return(np.array(a.iloc [peaks].index) [0], np.array(a.iloc [peaks])[0])\n",
    "\n",
    "    if len(peaks) == 0 and len(mins) != 0:\n",
    "        return(np.array(a.iloc [mins].index) [0], np.array(a.iloc [mins])[0])\n",
    "\n",
    "    if len(peaks) > 0 and len(mins) > 0:\n",
    "        if np.array(a.iloc [peaks].index) [0] < np.array(a.iloc [mins].index) [0]:\n",
    "            return(np.array(a.iloc [peaks].index) [0], np.array(a.iloc [peaks])[0])\n",
    "        else:\n",
    "            return(np.array(a.iloc [mins].index) [0], np.array(a.iloc [mins])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mm_segment(segment):\n",
    "    a = segment\n",
    "    plt.plot(a)\n",
    "    a_s = angle_in_segment(a)\n",
    "    plt.plot(a_s [0], a_s [1], \"o\",markersize = 10, alpha = 0.6)\n",
    "    print(a_s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_by_first_bout_angle(angle=angle,segs_mask= segs_mask):\n",
    "    a = trace_to_segments(angle=angle,segs_mask= segs_mask)\n",
    "    appended_data = []\n",
    "    for segs in a:\n",
    "        appended_data.append(angle_in_segment(segs))\n",
    "    df = pd.DataFrame(appended_data, columns= [\"Time_frame\", \"First_tail_angle\"])\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_by_angle = time_by_first_bout_angle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.zeros(len(angle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     56\n",
       "1    184\n",
       "2    301\n",
       "3    735\n",
       "Name: Time_frame, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_by_angle [\"Time_frame\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#t [time_by_angle [\"Time_frame\"]] = time_by_angle [\"First_tail_angle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fef251789e8>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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xmz5nMTg6e8dlre8XY79GAAcAcK7HpcbEjSUDAMAxgiEgRR7sHOnrL9/V6m4jvCCuGJ037u3qBx9sjPswAABTLm7gQacbQcawFgAAuEUwBKTIw51jSdLGQSMY8ikZGhnfWlX95vNZvzfLaQYAOMalBgCQRgRDQIqEIZAXKSUnGBqd8FS+u7qnP/3hbVWqjW17nGUAQFIG6jFk4iuMAAAYFYIhIEXCYpZoMERiMXpvPdiVJB239HMCAMClbvmOlWKnkhkZlgIAAKcIhoAUCbc5eZGVoc9qcGSsbMdFOTdjAQCu1aeScc0BAKQIwRCQIn4tBcp4bCVzwdrO08eYSgYASCVDkAQAcItgCEiRuK1kBEPu0LMBAJCk8LITdzPC2qCfUCsG2AMAXCMYAlKkWlsxNrUYIrsYGavO55PzDABILy5SAAB3CIaAFPHrPYaoGHLB2i49hpI9FADAFLJtHwAAMH4EQ0CKVGM6TdN8enQ4lQCAcQq3MHe6HpmYjWPGGKpaAQBOEQwBKeLHLBipGBoh23kxTr8hAEAaGXFjAwDgFsEQkCL1YCgSUhBYjFbr+TS1bXucZgCAa9ycAACkEcEQkCJVP/izuWJoLIcykRhJDwAYq3AqWYfLUexUMsbVAwAcIxgCUqTamGNbx1ay0bFdtpIBAJCU1hsV3S71cX2HAAAYJYIh9LS+X9TbD3bHfRhTodGUsrFC9P1xHc3ksVYdkyHyNwCAayetXKXiFQDgEsEQevr2m6t65fb2uA9jKoRTyaIhRcX39e03H2pjvzimo5ocLKsBAOPETQgAQBoRDKGrcrVRrkJjRPfixtWv7RW1vl/Syx9ujeGIJk/0rqvt8DgAAC4NsqSixxAAwDWCIXT1aK9RpVKhC7JzNqYpZbEShHO5DP9ch2Wt7bi4ZtENAHAtppVg7OdRjKsHALjGO010tblfqn8cV82C0arWeww1HBQrkgiGRiE8r4yoBwCkUdxUMgAAXOOdJrraPGwEQ1QMudeoGGqc63I1+NjjX+vQuk0l49UNAHAtvNa0bs/vul3fsJ0fAOAWbzXR1dZBSWGhSqXKeKykxC3/woAIw7HNjYXqQ4BZdAMAXDvJtYZx9QAA1wiG0FHVtzosVXVuLi+JiqFx8CJrwVKFYG544dS3cMser2kAQPK4FwEASBOCIXQU9hQq5DJNn8O9cMHoRZoNlKnYGlq3hTivbgCAa72uNVQHAQDGgWAIHfm1d9H52l4yKoaSFJzraBNKgqHhtU2BsfEfAwCQpK5TyRhXDwBwjGAIHYUVQvls8DKp0uPGqWgPp3ABmInsJSuylQwAgFOtV8ATN5UsGFfPGgwA4A7BEDoKK4YK2bBiiGDCpcNytf5xuPyLbiWrVK18qraG0pj6Vvuz+asJHw0AYPqEve7GfBgAAEQQDKGjMAfK1baS0WPIraNSJBhqOdUzueB3UGI72VDCO65xd15ZpAMAxqXrtHpjuEYBAJwiGEJHVdu8lYxx6W4dRoKhUBjGzdYagNNnaDitC2tG1AMAktSoWO3/+hO3vQwAgFEiGEJHfkswRMWQW4elSv3jcMEY/g7Cqi1+A8MJz1/cVjLOLQDAtZ5TyTqEQNzHAAC4RDCEjsJ+NhljlPHoMeTaYcxWsvDPbIbbhaMUt8Bm0Q0ASEpbBSu3JwAAY0QwhI7CrWSeJ2U8j4ohxw5L1Xp1Vnim2yqG+BUMpXXrmLWU6AMAktNzKpnaL0rBVDIAANwhGEJHYQ6UMUa5jKHHkGPlil+fABcGGOHvIBuOredXMBL1LWWRE8rdWgCAa40hCP0zhp54AAC3CIbQUX0rmWeU8QwVQ45VfNsIgFrk6pVE/A6G0diix1QyAEB6cA0CAIwTwRA6CoMgY4yynqHHkGNV3yqbif8nmfP4pwoAwGnX7QZFZ4bbQgAAp3i3iY7C/jYZzyjreaqwlcypiu/XK4Za14th82nuKA6nrYQ/cj45twCAcYvre0cvPACAawRD6KgeDBmjbIaKIdeCiqFaANRybzBXfxzDsI3mQtE/ah9zdgEAbrVchgb/RgAAHCAYQkfVWg5kTDAVi+bTblV8q0yniiGvuSk1TobTBwAYp17X8bjiIAqGAACuEQyho6of3UpGxZBrVd92HEufpWJopOpbythKBgAYg0GvOVS1AgBcIhhCR21byagYcsb3raxVvWKolUeDgZGol/DXt5JZejcAAMauW1BkjOHmBQDAKYIhdBQGQ55nlMt4qjCu3pnw3NabT9cev3p2RlKj8SQLw+GwFQ8AkA5cjwAA6ZEd9wEgvaq+VTg9PeMFd6sqVb/jSHWcXLUeDDX3EvriUyvyrdX6fil4nIXkUFqbfrKVDACQpMa4+vivx04lEzESAMAt3uGjI9/a+hamsPcNVUNuhP2bWqePeZ5RNuM1Foqc/uF0WZATugEAXDvJtYYtzwAA1wiG0JFvG71twi1O5SoNqF2INvqW1DEAIroYLSsqhQAA49crMOJaBQBwiWAIHVUj49PrFUM0oHai0rKVrBU3C0cjbuHdq6wfAIBRaQw/6KT9im9YBQAAHCMYQke+b+XVgqFwXHqZkfVO1CuG6lvJWpaMNJ8eidbzF21GzakFALjW6WZEP9d3BigAAFwhGEJHwVay4OOwkqVKjyEnwoqhXDiVzDb3FAjvFtIHZzitZ89aAiEAQLrRYwgA4BrBEDqqWqtMvfl0rWKowttoF6q1LXrhxLfWs8y4eve4EwsAcK3XlaZbCMRlCgDgCsEQOmreShZOJWMrmQvhec1EK4ZinseacDix08hqD3JuAQCuNa45XHUAAOlBMISOouPqw6lkjKsfvVLFV7lWMRRWZrWiihwAgMnR1mOoy3NN94GlAAAMLTvuA0B6VX1bbzodBkQ+dcwj9/WX79Y/ro+rb2FMWEnE+T+puHMX7THEqQUAuNZzK1m377W2xzMAADiZkQRDxphbkvYkVSVVrLXPG2OWJf17STck3ZL0NWvt1ih+HpLh20YgQY+bZOQ6jKsPcf5PruO5s20fAACQqG43fhhXDwBwbZRbyX7VWvsZa+3ztc//SNIL1tqnJL1Q+xyniB9pPk3FkHtZzzRPImuaSoZhxb1y6fEAAEhUh3H1A3wrAAAj57LH0Fck/Unt4z+R9HsOfxYciPYYCoMJciF3Mp6pV2i1YlTt8DpvJbP1jwEAcKl+zekQ88StA1gDAABcG1UwZCV92xjzsjHmD2uPXbLWPqh9/FDSpRH9LCTEt1LY8saLTMuCG9mWxtPR0vHwY86/O5xaAECasQYAALgyqubTv2ytvWeMuSjpO8aYn0a/aK21xpjYy1ktSPpDSXr88cdHdDgYBWsb4+ql4I4VW8ncyWW65LT1iSSc/5OK30rGQhsAkBzbYStZP1PJAABwZSQVQ9bae7U/H0n6hqQvSFo1xlyRpNqfjzp87x9ba5+31j6/srIyisPBiFT9xlYyKage4j20O1mvudF3HEKMk+t17ji3AIBxi1sC1KuGWYUBABwZOhgyxswbYxbDjyX9pqQ3JP21pD+oPe0PJP3VsD8LyYpuJZOChQkVQ+6EFUOxi0LuFg4tbkFtrY2Mq+e1DQBwq+OAzD4uQVymAACujGIr2SVJ36g1y8tK+lNr7d8ZY16U9OfGmH8n6UNJXxvBz0KC/JitZLx5dqe1x5BM+4ec/ZPr9NLlJQ0ASMpJrjncHAIAuDZ0MGSt/UDSp2Me35D0pWH/foyP37aVzPAm2qGs17mAL5xSQjA3WrbDxwAAuMTlHACQJi7H1eOUa9tKZoLH4EZ4ruPuDFIx5EZ0Yc4iHQDgUj83d7qtAQAAcIVgCLH8WgLUXjHEu+dRijufYZPJuIUgp//k4s8dJxQAkLy2vnf0GAIAjBHBEGKFTaajwRAVQ+NDfwE3miqGCIkAAA71E+yYmNtCrAEAAK4RDCFWGABF294YKoZGrt/TadhMNrTYqWQdPwEAwJ3W63/3mxOMqwcAuEUwhFhhxVCmaSsZFUOJqPcaaq7WkigjH0bcueN8AgCSMuwlh2sWAMAVgiHECoOhpnBChrtVIxZ3NrtVjHP2Ty7u3PmRVTbnFgDgkj3hNYetZAAA1wiGEKtabz7deIyKodGLLhI/ujLfx/NdHs1k67UNknMLABi3biEQlykAgCsEQ4hV7zHUtJ2JHkOufOb6WV1cnGl6LLo25G6hGz6vZwBAQqJXnNb1VLfLEUsAAIBrBEOIFS5YMl5znxveR49WeDqjwY+JSYEMjSeHFnfmmEoGAEjKsGsobs4BAFwhGEKssGLING0lM1RYjFjc6ezaY4jTf2K9zh3nFgCQlMF6DFEzBABwi2AIsRo9hpqnkvHm2Y24NV9zFVHwJ+d/CEwlAwCMUbfK1N7D6ukxBABwh2AIsTptJaNiaLT63b7EvcLhxZ3r6GO8sgEALjVtXz7BRYclGADAFYIhxIrbSmaM4c2zI0bNAVzb1w09hlzwmxbpnFsAQFLirzm9KogBAHCBYAixqrZ9K5kRb55HLbbHkGn+U4qUkXP6T4xzBwBIq77WV1zHAACOEAwhlh/bY8g0VVhgdLgb6F7cSze6NZKXNgBg3LpNJgUAwBWCIcQK3y9nWoIhqi7c6LXkIzhyI/p6frB9rLW94vgOBgAw0YbuMcQtDACAIwRDiBVuJTORVwjNp0cvflx9zN3CWjK0d1xRsVJ1fVgTKb5Mv/mxH3ywkczBAACmTreBB12nknFzCADgGMEQYoUBULRiiGDInbjR9HEB0c31A33r9QcJHdVkiY2FWh5cnMkmciwAAAyKJRgAwBWCIcSysc2nuWU1ao27h73PbfirOCr57g5ogsUtqFsfOjObS+RYAADTp59gp9tqgFwIAOAKwRBiVWvZQ7SSxaNiaOS6nc7W0nFiueHE9WZoPf8e9foAgAS0Xn8GWQ8AADBqBEOI5cdUDHmekU+xihP9LPpYGI5eWBn3+PLcmI8EADDpotlPp/H03a71fY20BwDgBAiGEKveY8iLTiWjjHnU4jaSxY2qDZ5DMjSUmBevX3vs8tKMsp6hIg4A4MxJg51O6wIAAEaFYAixwrWL17QW4Y3zqHH3LznxM8lq0/eMJENjTwDAePQzip5LFADAFYIhxKr6VsY036XyjFiVONLX3cDIUypV9vQNqtu0+louJF7gAABXbIePe6FeCADgGsEQYvnWtlQLBeEFFUOjFXc2+1kAlgiGBhbbfDrysTGGiiEAgDPRa0yn603ctvHw3hHXKACAKwRDiBUEQ82Lk2Aq2ZgOaMI19xgK/+wcEZUqBEOjEC6yjQmW4ry8AQBp9Z23VuWzEAMAOEAwhFi+bR/dHX5OX5zRGehURp5LMDS4uHMdrSIy9BgCALgUrRhqvRXRbVx95PYRldsAABcIhhDL9628Dq8O1iSj10+LoegiskgwNLDYFkPRHkOG0BMA4E4/DaYZQAYAGAeCIcTqVjHE3aoRqgcTjXPdaSx99LQflCouj2pqRF/LnjFsJQMAJKP/gqGmsIjrFADABYIhxIrrMRR+yvb20Ym7e1jvMdT23Ib944r+8f113Vw/0M5R2dnxTZK4aqDwERp7AgBcG8U1husUAMCF7LgPAOkU33w6HOjNqmTU+tpKFjnte8cVPdg51q31Q0nS156/pmyGnLeb7uPqTW0qGa9tAIB7na42ccsBdpcBAFzjnSRi+VZqzRk8qipGrtu57BYW7R43VwnRc+hkmppPixJ9AIA7J77GNG0l40oFABg9giHE8n3bNi69sZWMRcmoxJ3JXncG5wsZHRSrTY8RDPUWO5WsPq6eqWQAALeiVamt15t+rz9cpwAALhAMIVZ8j6FwXP04jmiyRU91r21lc/n2HaDFSjXmmegl+lo2MtyJBQCMV8waoNNQCgAARoVgCLGCqWTNj5n613jzPCrh3cPW6qxucpn25x6Wqnp3dU8+ncE7igt9oo9QMQQAcMk2fWxbvtb5AsS6CwDgGs2nEcu3tq2ZcWNc/TiOaDLFn0pT+7/xYVE+psn0izc35VtpJpfR9eW50R3gBIlbV0cX2/QYAgCkUYnt4gAAx6gYQizfb99KlqmVEFVJhkZukCLxuOlj4a+Em4qdxQ4la+sxxAkEALgRvcR0upAxCrQAACAASURBVNzE3RQqVxvBEJcpAIALBEOIFbeVrJALXi70sxmduAVer11lcVvJQvxuOosPfaJb+QwVQwAAZ07ax+7imZkRHwkAAM0IhhCraq0yLQlFIZORJJUrvH0etbgwqFNAlIupGAoxnWwwTc2n2UsGAEhIeLmx1uq/vrOmR7vFjs9dKGT1hY+eq30fFyoAwOjRYwixrG0fV5/PUjE0anELvF7byjoFQ9mM4XfTRed6oYBnDA0+AQDuxFxiSlVfd7eOdHfrSFK3qmEmkwEA3KFiCLGCcfXNjzWCIapSRm2QUbStW8munJ3Rf/fpK5rJZVQs87vpJC7zqfcYEktuAIBbTVPJwqmkA159uH8BAHCBYAixfL/RbDqU8YyynlGpSvgwMpHmx6GwUqvTUrG1YmihkNXiTE6FrEdoN6CwQihsPk1fdQBAGvXqPwgAwDAIhhCrGrOVTAqqhhibOjonySHigiEp+N2wlayb9rMdBkOeMUwlAwA41TSVbMDvDVdkXKUAAC4QDCGWjdlKJhEMuWI6fBwnG9lK9gsfW9YzlxcliYqhHuIyn6ofCYaYSgYAcKipr6CNeUy91wDcwAAAuEAwhFjVmK1kkpTPEAyNUn19N8BUsui0uOvn5uqVXYUsPYa6iVtKN4IhSYbeDQCAZPV73Ymr4gYAYFQIhtDGRrbXtMpnPXoMjVDsVLIea7/o7yUbCe8WZ7Kq+FaHpcrIjm+SdGs+nfHC9p8kQwAAN/oJgXoFQFylAAAuEAyhTVhFEbc2KWQ9HZXoYzNqA00liTzViwRDS7M5SdLuEcFQvyr117qRMYaKIQCAc8bE3xjq+j2OjgUAAIlgCDHCyUxxFUNLczkVK76Oy4RDoxAXRDROe/wyMK73k9QIhnaOysMf2AQKF+Fx588zwdkmFwIAuBLdPR5e/we9IcENDACACwRDaBNOaorrMXRuLi9J2josJXpMk26Q1gFxgZ0kzeQyymc9gqEOwsV03OnLeOFUsmSPCQAwPbpt1e+FFkMAAJcIhtDG1iuG2r8WBkObBwRDoxDXe7rXtrJui8Ol2RzBUAf1YCjm/DamkpEMAQAci1yG+p1KVr92cZkCADhAMIQ21R7Np+cLGW0fEj6MQrexs50CoG53Gs/MZLVLMBSrvpUsJvH0jJFnGtsoAQAYtcYNiiG2kpEMAQAcIBhCG79HqfO5uTwVQyPWNIWkj3Lxjn2G6AHVUybmv3qNcfUsuAEAbhljOsY7ne79sJUMAOBSdtwHgPTx/e7B0PJ8Xne3jlSu+srFvctG306SQ3jG6F9+7rHY721MJitrJpcZ8ugmi+3SVD0YV8+qGwDgXvRqM+gygPsXAAAXeFePNn6XJr2SdHYuCB/YTjY6psPHsc81UiGbiQ1+mEzWW2swZEw4rp4FNwDAnbghCFSqAgDSgGAIbbpNJZOk8/MFSdLq7nFixzTp4kK4TgFRtx5Dc/msCllP6/vBVr93VvfYVtai9XUdfhqMq2eBDgBwywyxdZmrFADABYIhtKn63YOh2XxGFxcLurl+kORhTaS4daHp0UigV0XRhcWC1veL2jkq66VbW/r+e+snP8AJ0mnaXhi0GWOoGAIAOFMfghDpMdR62em0rZkeQwAAlwiG0KZc9SVJ2U4djiXduDCvveOK1veLSR3WRItbCHYKiOKmakVdWMhr77hSn062usvvSIpWA7VWDIXBEFvJAADuxF1j+r3uhGsCtp4BAFwgGEKbSjVYdOSynV8ejy/PKeNJt6gaGko9rBhsKFlXl87MSFJTRVeVOeyyNgh/WnO1sDIu2ErW3WGpondW95wcHwBgOnStUO2xCOBqDgBwgWAIbcKKoZzX+eWRz3q6sjSre9tHSR3WRHJx4+/8fF6zeU93txq/m63D0uh/0CljFay3Wyuxwk/76fnwn376SC/d2qJvEwBgYLF1q/1WDI34WAAAiCIYQptyWDGU6b4MOb+Q10GxqmKFN8nDimYVw/YRMMbo2rm5pse2CYYk9agYivR8iLLWavc42Ja3e1SRJBUrvsvDBABMoPDmQ3CdDz4edOgBO8kAAC4QDKFNxfdljJTNdH95nJvLS5J2GFt/Yt3Wd8PkQ+HvRgoCvi1+R7LWysi0TXVr+tw2nvtoL5i690/vb+ibrz3QYalSf1qJYAgAcEKdGkx3/R5KhgAADhEMoU256ndtPB0KwwdCh+GZpo+HX/2dm8tFPs5r84CKoXoJf9tUsvBPI792K/atB7v67luPdGfzULc2DiVJ33lrtf49pSrBEABgMNHrkK3fiGh+Tq8AaNAKIwAA+kEwhDblqlW+S+Pp0Gw+o/lCRjfX9+XT3PhEuvW0Gebu4NJsIxi6sFjQ1kFp6qtcbK3JUGuPofpUMjUW7WEV3Ie1UEiSDoqNLZNFegwBAE6oj3tvbUZx0wgAgE4IhtAmqBjq76XxmetntXlQ1v0dmlAPIxpWjKJcPNwGeGVpRlfPzsi30sOd4+H/4lPNyqh9QR43rt6rPel+S3P1X3nqgiQqhgAAg2vcC2r0tGu9PdRpCVBfG3AfDgDgQHbcB4D0qVStsj0aT4cuLwWj0feOKz2eiTixE0pG5N98/no97MhljB7sHOnx83O9v3GCGWPa7ro2xtU3Hj+qVQRVfKtC1qs3m75Se71Pe/UVAOAkIs2nIz3tBv8bAAAYLYIhtClVfeV7NJ4OFbIZ5TJG+0WCoZOIWw92Col+61OXdTDAefa8RiXMhYXC1PcZslaxFUPRcfWS5PtWh5FtY2dmc3piZV77xYqyGU/5rEcwBAA4MaPBp4uxkQwA4BLBENpUqlZz+f6XIIszWT3YOdb2YUlnI9Ow0N3OYVk/urkpKX77WOtjy/N5Lc+f7Pwuz+f19oNdVap+z2lzkyDutdho+hlfMRR93kGxojOzWWU9TzfOz+ljKwv1r+ezng5L9BgCAAwmDINMpGRo0AogxtUDAFyY/HeIGFjF95UbIDyYy2e1f1zRt15/SCXFAL7//nr946btTQ5uCy7P5+VbaeOgpPKE98e5tX6gb73+UHc2D9u+ZkzcVLJGZZUUbBOr+FZPrCzotz51WU9dWmx6/nG5qrtbR3r7wa6T4wcATLYgFopPeFpvXjR9EwAAjhAMoU2p4ivXZ48hSU1Bw7uP9lwc0lQa5QSSc7VKoxfefqSvv3x3ZH9vGoX9rrYOm7fOWRuEP17HqWTBn2Fj6U7h6MXFgiRpba84uoMGAEy86Lj6+mMDVgAxrh4A4ALBENpUfatMn1PJJOlTjy3p8lJBs3lP27Ux3xiMaSoYGv1twfl8RtnalqlJL0MPG6dX/Ob/R8MGn+1TyYI/w99BGHRmO8wT/sUnLmgm57UFTAAAdFOffGlM/eN+gx7G1QMAXCIYQhvfSpkB3vReOjOjX3vmks7O5bV7RDCURsYYzeQz9c+Py5PbIycMdKqtwZCChXXbVrLa88Ogpx4Mdaiay2c9Lc7kVKxM7jkEAIxHr3H1k35zBwAwHgRDaOL7kVGqAzozk9PecWXg0avTqtfib9Q3B6O/l93jyQ3wwmbSrb2Uwq1krf0bWnsMVarBecp2qZpjMhkAYFA2Mq7eNh4c8O8AAGD0CIbQxK9vtxk8lTgzk1XFt0xsOoG40z1I1VY/VhYK9Y/3j/sfe3/ahK/d1oqh1q+HZnLBfwbDR3tVDElSPuOpSDAEABhA81Sy2mN9fi8byQAALhEMoUn4Xrp1hHc/lmZzkqRttpMNJTzznXrcnNQXPrqsLz17UZ6R1vdLvb/hlAoX2WHlT+NxK2PauzR87EIwir7RYyisGOp8/gu5YGT9+2v7ozhkAMAUMVLH6upe94SoygYAuEAwhCZ+hwa9/ViezyvjSau7xyM+qsnUq+H0ScK5brIZT5fOzOijF+b1/tq+dmoBXqXq62iCqrzC13Br82lFmn5K0sJMVp99/Kxm672XmregdTv/+drEsh9+sEmvIQBAXxoVQ+2P9dJxjD0AACNAMIQm4ZvqkyxAshlPK4sF/fTBnt57RCXFSYXnvttWpmE8c/mMrJW2DoKqoe+9u65vvHLPyc8ah3CRXfVbegwpiH7Cl/ZHluf07JUz9a+3TiXrNK5eamw/k6S/ee2B3l3d073to6GPHQAwmb737ppevbMtqflm0KDj5ykYAgC4QDCEJn69quJk3/+R8/OSpJc/3Gxr/ovOYnsMdWl+PIy5QlAhE/aCerATVHhNSuVLWGZfbt1KVm8+HXzut6yuw19BWGnUrWIo2pi6VPH14q0t/f3P1urN2wEAiLqz2bh50G2N1WksPfVCAACXCIbQJGzYe5Lm05L0xMqCfvOTl1T1pQ/WDkZ5aBMterbDYGPUPYZCuYynbMboqFzR2w92649PynayMJqJaz5tZOqv7davhpVa5VpT6W7nv9O/j0me9gYAGJ3w3kT/W8ncHQsAAARDaGKHmEoWurBQ0KUzBb1xb4eR3l3Fn+Oq7V2xMqy5fEZre0W9cnu7/tikTJPr1GMoaD7deG23NvCsTyXzrbKe6bqd8vryrH7xifO6cnZGV5Zm9OufuCgpqL6iMSgAoBtjTH0L2aBXDC4xAAAXCIbQpL6VbMhXxqevn1Wx4uv25uHwBzUFoiFE2BrHVcWQJM3ns9o8aK5umZRgKFw0V1q2MrY2/WwtKAoDo3tbRz1DOWOMblyY168+fVG/+sxFXZgvSJJeub2tD9aplAMAdNbt3lunr3XaYgYAwCgQDKGJP4KKISmoGjozm9Ut3iR31DyVrKHi956KNayZXKbtsePyZARD4Ws4rt2PUaO3Q7e7rtUBb8l6ntHFxSAcWtsr1h+vVP2J6d0EABiNuO3j/Rq0WTUAAP0gGEKTUQVDknTt3JzW94s05B1Q2Bsn66j5tCTN1xpQRwdvTVrFkNTcZ8gqDOOC13Zb8+nIS75SHfw1+2vPXNTyfE6HpUr9sW/+5IH+4uXJmfgGABieMWbgHkMUDAEAXMqO+wCQLuE2plFkEmdmsvKttF+q6MxMTkelqjKeUT5LHtkqGkqEYUbG0bh6SXr68qLOLxR0dWlGh6WqvvfumvaLk9E4ObrIrvi+Ml6m9ngYegZfaw2GhuV5RkuzeT3cPVK56usf3lufmLANADA6xpy8VxA9hgAALvAOHU1GWTG0OJOTJO0dBxUU3317Va/c3hr6751ETT2GHE8lk6RCNqPHzs7KGKP5QlbnFwpa3y9NRHVXtMw+WvkTfNSYStZajX8QqfQ5qTOzWR2VfH3nrVU92D4e+u8DAEyWky6vmEoGAHCJYAhNRhsMBQVpr97e1vp+UXvHFa1G+q8gXjhNy2WPoVYXFwuqVK22j05/1VA022raEmaDhfX5hbwk6fryXNP3nZ3ND/2zL5+ZkSRtHzafR6bzAQCk9h1hfY+rD58/yoMBAKCGrWRoUt9KNoJMImxwvHNU1rffXJUk7R9XdFyuxjY/njadTnGjx1BywdCFhaBx8vp+Ucvzwwck4xTdIlb2mwMZo6CS7fd/4fG277u8NKOvPn9Nu0flE/d3Or9Q0Jd/7rK+9frDpseLlSpbKAEAkoJr0UkDnkGbVQMA0A/eqaBJ+KbajKhm+enLC3rs3GzTY5sHpZH83ZOqXrWVYDA0l8/IGOnDjUP98IMN3dk8TOxnj1rn5tO25+s6l/F0fqGgpbnciX/+2bm85vIZ5SI9oopUDAEAFFSuBj2GgutT65SxjuPq2UsGAHCIYAhN/JYGvcP6+Y8s659/fKXpsY19gqFuwu1PSVYMGWNUyHpa2yvq/bUDfe/ddd3dOq3hUKRiqNoIZJK8yfp7n31MX33+un7zk5ckEQwBAEaDeiEAgAsEQ2gSFliMur/NhVpfl6XZnNYP6DMkdb77Vx1DjyGpsfXv/EJeGU96dEr7QXXqMWRt8tN+C7XtY8Uy08kAAJKpXYnCq1PrTQvT4UpFvRAAwCV6DKGJHWHz6agvPXtJVd/qx7e3dG/raKR/96QJg42T9rk5qTDEWChkZa3V9uHprOyKTlarNG0lG10lXL8K2SBs++nDPT2+PKdshiweAKaaqf8fSYNXANFiCADgAu9S0CR8Hz3qrewZzyif9XRhIa9ixdfe8emffuXKQm2a27gqhmZyGZ2dy+vhTvFU9hqykrK1/j6V1ubTCQdDYcPp7cOybm2cvnMJABg9c4Lu07QYAgC4RDCEJvVtTI5WIOfng+lX9Bnq7DeevaRffWal9xNHLKwSm8tndL42mex7764nfhzD8q1VvlaZ07yVzHYs0Xfp+nLQfH1j/3RuzQMAjE77uPr+EiLDwHoAgEMEQ2jiO9pKFlqazSnrGa3vF/Xeoz1VqtPblLfTGZ7NZ3RlabbDV90JF6eFrKePrTSmyR2WKokfy1BsMNEt47VvJRuHX3lqRVeWZvT+2oHeXd3ThxsHTdvdAADTJSgYir8O9Fp+sZUMAOACwRCahAsOV6PSPc/o3Hxe76zu60c3t3Rz/cDJzzkN0ra2C7MKzxhlPKNnryxKkrYOg21/WwcllU7BdC3fBr2Esp7XHjyOqRT/8tKMJOnFW1v6/nsbevvh7ngOBACQKmlbCwAAphPBEJpUrXXeoPdK7U2yJN3fOXb7w1LMT9ltv49fXpAkXTwTbPc7OxtsJ3uwfaQ37+/ob994qH/6YGNsx9cvKyvPGGUzRuUxTyULPX1pUTcuzNU/fzjFr3sAmHbGmPqNuPapZJ2+x+khAQCmHMEQmvjWOttGFrq+3HiDvLpzXO9rNG1Slgvp4uKMfv8XHtdcPmh+nc96Wp7P6Z3Vfb12Z0eStHYKRtj7tQAo63ktzafHd8I9z+hqZHvgxn6p774SAACEuHIAAFwgGEITa63zu1JLszk9dWlBT6zMq+JbPdqb1uqJ9C/vPnl1qelz37ep748TvoZzGaNyZCuZtcFd2nFZrE2bk4LeR8fl9G/LAwCMllXYYyj6yADfn+5LMADglMr2fgqmSdCfxf2b58/fWFal6uvWxoHubx+PpdnyuKU8X5EUVHf9xicu6fx8Xrc3D/WP729o97iss3P5cR9aR2EANJPLaL/YaJwdLsbHZXEm1/T5frGi2XxmTEcDAEiDfoMetpIBAFyiYghNfN8q47rJUE024+ni4oxWd6ezYui03PVbWSzI84zOLwRhUNq3k4U9hgpZT8VKtelr41xY57OePveRs/qlJ89LOoXT3gAAI2FM5zVAp8rWcFx9p2lmAAAMg2AITaoJbCWLOr+Q185ReSrH1p+2xd3iTE5z+YxWd9uDoVLF14OdI33zJ/e1vj/e4Mj3g8qgmVxGx2W/3ssnaD493luuz1w+o6tng+q477+3oVfvbOvm+oG2Dkp64e3Vpq1vAIAJZLt+2vvbT9fSAQBwSrCVDE1sQlvJQufm8rJW2j4q68JCIbGfmwanYStZq4uLBT3cDRqGh5Vlvm/1wtur9bH297ePxvq7tJIyXhAMWSsVK37wcUqCuFymkce/dT8YW39+Ia+N/ZI29kv10fYAgMkUvUnR71QyAABcIhhCkySmkkWF25M2D0pTFwydxqlU15fndGvjUP/+xTtamMnqwnxeV8/O1kMhafyBl2+tsvI0kwsCmHowZNPboyHcVhZWDN1aP9Bxparr5+b0YOdYT15cGOfhAQAcGGQdkNbrFwBgMrCVDE2qvlVCLYYkSXP5rGZynl66taW/evVe03Egfa6dm9X5hbw8I+0fV3Rr41CP9orKekb/9gvXNV/I6KhU7f0XORROJZvJBY2di+XgeMbdfDrqtz91WfOFRuPpo1IQCB3VjvUf39/Qjz/c1gs/faQf3dxUqcIWMwCYBFaNLfvW9r+tPLx+ncJ7SgCAU4BgCE2qvlU2k+zLYnk+qBo6KFbl+1b/5WeP9Dev3U/0GMbBP4WrO2OMvvTMRX3t+ev6/I1zkoKtY8vz+foksOPyuIOh4DgL2eB13DQWPiXJ0Ln5vC6fad8ydlQK/g2E9o+DSqJ720faOy63PR8AcLoNuhRIy7ZoAMBkIRhCk3LVVy6T7Lvn6Ojzdx7t6f72sQ5L1YlvxHsKcyFJwTQ5zzP1ipzDUlXLtS2Bs7mMjspVlSr+2LbKWUlepGLouDaZLG1b9+KO5rBU1V6xfVrZP72/ob957YF2DgmHAOA063Up6rRlrNO0MgAARoFgCE3KVdvUHDcJizONVlc//nC7/nFYLTGpUpZTDGw239gKtVwL92bzGW0flvX1l+/q9Xs7Yzku31oZRSuGGhVM455KFnUuEohKwZuB43JVa3udp7q99OGm/uObD7W6e+z68AAAjlmdoGLolK8dAADpRDCEJkHFULIvi49dmNeN83P1z5+6FDTavb9zpJdubTZtralUfb1+d2ciqolO41ayqNlcIxhams1JUn1SmRQ0FB8H3wYVQ+F2suhWsjTdcP34pQX986dX6p9fXCxo+6ikV+9sd/ye1d2iNvZL+nDjMIlDBAA40OgxZAfuMQQAgAsEQ2hSqVplE95KZozRjQvz9c/DSorX7uzondV9bR+Vtbp7rO++taq3Huzq9Xs7en9tP9FjdOF0x0LNwVBY9bUcqYJJcrpdlLW2voKeyWV0UKzoqFRV5OFUMMbosbOz9c8vnZnRUclXqeLrix+/0Pb8cItnIetp86BzVdGf/vC2Xry1OfoDBgAMrde1ny1jAIBxIBhCne9bVXyrfMIVQ5KawqiFQrbpa/vHFf3Du+t6tFfUB2sHTV/zfVsf9X3qnPJkyItUB4UNy29cmNe/+txjurCQV8UfX1VXGErN5Dw92DnWN165l/qGndfONUKiaGAU+tKzl/TFj1/QExcXtH1Yjp3cV6lV0r27evqDUwCYVNFtzf0WD5MXAQBcIhhCXan2pjLprWSSmsKoXMbo2SuLevbKoiRpv1hRsTau+7A2Cn3vuKJy1de331rVX75yP3WNhftx2reShVqblc/kMsplPJWr4/n/L+gx1DiWUDitLG2eWJnXc9eWdHYur4uLBT13bUnGGD1/45x+85OX6s87N5fTtXNzWlksyLfSP7y33jYB7jgy1v7bbz5s2oYJABi/4FpU+3iA7wuvXxOydAAApEy291MwLSq1N5FJTyWTGhUnkpTPevrs48Eo9PfXDnQQUxH07up+U1VEseI3hQCnwSSs7f7V5x6LvYuZy3ixv7ck+H6jmmkm13hdRRfjafILHztf//jXP9EIgj5+abHpeeGbgsfOzurnP3JOr9ze0vffW9elMzPaPizrl548r6NSIyha3y9p/aCoi4szjv8/AACcxElCnrRXvwIATieCIdSVK+OrGMpGtiVFf/5CIdPXBKaDYiU2GFrdPdbKQqFp21NaTMJdv05hXC5jxtYgPBxXLzVXCFV9m6oeQ8N4+vKijJFeurWl1d2g31DhQ08XFwtNz1vdaQ+Gtg9Lymc9zeX5zz8AjNskrAUAAKef8wTAGPNbxpifGWPeM8b8keufh5Mrj3ErWfRnRreVzRey2j3qXXlyWKq2Pba+X9QLbz/S3a2j0RzkCJ3GrW+DyI55K1nYZroc2VpV8W0qK4Z6+Z3nrujLP3e57fEnVxb0qcfO6Jkri7qyNKN3V/fbJpo9jISq6/tF+b7Vt15/qG++9sD5cQMA4jW2kg12nTSGIAkA4IbTW8bGmIyk/13Sb0i6K+lFY8xfW2vfcvlzcTLlMW4li445j1b3LM3mdEfNwY5ngpHkknRlaUYPdo61dVjS9mFZs3lPizM5XTozo0e1Sor9YvqaU0cXduM4367lM54qVaujUlWz+YS3+EW2jD1+fk7vtzQsP22WZnOxj3ue0XPXztY//y8/e6T7240gKONJG/tFlau+3lnd02t3dvT05QVJjW2jkvTu6p5evLWlf/3z15TP0nYOAFxraj49QDg0easFAEBauH4X8AVJ71lrP7DWliT9maSvOP6ZOKGwuiI7hoqhTs5Fxp+HvvjxFa0sFvS7n76iL358RdmM0Rv3gjH2P7q5pRfefiRJerQXvEk+KqcvGAobT3/6+pK++vz1MR/N6IVT5r7xyr3Et5T51tankl1ZmtUvPnE+8tXJXVZ/4soZScE4+199ZkVf/PiKfCs92ivqndU9SWoKyfaLFd3ZPNSb93clSdtHpb5/1qRXvAFAEsL/lA5Szcp/fQEALrhuMvGYpDuRz+9K+gXHPxMnFL6BH8e4+k6W5xvB0OJMVnvHFV09O6urkXHe8/msdo7K9c/zWU/WWq3vB290D4rt28zGLVzYeadxb1MfolsDD4tVLc15OihWZIyc97aJ9hiSmqvRJvR0S5Iunpmp/3u5sjSrqm+Vz3r6+5+t1Z9TiWzv+/5769rYb4RBWwflno2qj8tVfbB2oFfvbOujF+b13zSFbgCAQVkb3LLoJ/CZ5GsYAGC8xp4AGGP+0BjzkjHmpbW1td7fAGca4+rTs/KYL2Rrf2b025+6rK8+f63tOdHwSAqCoZ2jskq1Cqjd47J+9nBPu8fltu8dl7BiaFIXedFwcb82neyvXr2vv3zlvh7tHevN+ztOfq61trbIbpzYbOT1PKGnu+5fPL2iX37qgqQgEPu1Zy7Wv3Z+ofnfSTQUkqSNg2Dr5d++/kB/+3p8D6IffLBR72N0c/10b9EDgHGJXvsHrQCiYhMA4ILrYOiepOg+mWu1x+qstX9srX3eWvv8ysqK48NBN5Vq0Jx3XFvJzs7ldOlMoe3x33nuiv7bT15WNuPFNsa+sjRT//6nLy/quFzV2l7wJvfSmYJ2jyp6+cMtvXHPTRhxEvXy8QmNKqJhzN//bE3vPdqrf/7dtx7ptTs7qkS2mB2Xq3rv0f7QPzdsneNFXiZZb+z5d2JmchktFBoVWcvz+frEv4+cn5Mkffbxs7Hfe2v9UO+s7mnrsKytw/gQ9SimyfswipX0VfMBgGutV/5+bxIZGbaSAQCccL2V7EVJTxljPqogEPo3kn7fu9R6bwAAIABJREFU8c/ECZWr/lgmkoW+/HNXYh/v1Hw3dOlMEAxdPTurXMaoUrW6u32kuXymqfHx5kH/PVSS4k1mLqSq37x0/dHNrbbn3N481NZhSZ++dlav3dnW+2sH+tHNTf2zjy3r6tlZWauBG1eHPze6RS/btJVsQk94F7/z3BVtHZZ0dWlWy3N5rSwW9Oqd7aYG6FfOzqhStXr19nbnv0ij7W3xYOdI//mna/q1Zy7q8lL3LWwAMImspQIIAJAOTlMAa21F0v8k6T9KelvSn1tr33T5M3Fy5apN1Tayfs3mM/rKZ67quceWVMgGQcKD7WNdX57V05cW9ZHzc/rE1TPaParUt5eF1vaK+vrLd7WVcGg06VvJVhYLPQO9H3ywqZ893NeDneOmyXHvPdrX//fje/rLV+91+e544XmN9hXKTNFWsjjzhayunZuT5xldPDMjY4wKteljYbXdExcW9LnHzzZNK/N9q0rV14u3Nuvbxo7LjQqf6GvX960OS/FN3ouValN1WGi1NjVwfb843P+DAHDKtG4l67t6mHH1AABHnJeHWGu/Za39uLX2CWvt/+L65+Hkxl0xNIz5QlaeZzSTaxz/jfPzOr9Q0C89eaG+Re3O1qGstXq0dyxrre5sHapUCd78hkoVX77vduXVWNhNZlQxk8vod5670jT+/MmLC7HPvbd9pM2Dks7NBUHSUS18sFaxgUI3YTDUuWJooL9uYoX/zlcWC/rq89f0+Pm5ej+v0FG5qh/f3ta7q/v6p/c3dHvjUMflxu8jvNP9xr0d/eWr9/SXr9yPDVj/4uV7+rs3H0oKehTdqoVMkx6OAkAv9VH1/edCAAA44XorGU6R0xwMhWZyQcXQmdmszi80+hVdWpzRymJBL9/a0vZh0Iw6ausweENb9a2+/vJd3Tg/p1988oKz42xMJXP2I1Lhd5+7ImuDCh4r29RHaHk+r8WZrD6ojVB/5soZ3dk81N2to/pzXv5wS5+/sSyvzxPV2ErWeCwz6Sf5BMLgbCbX6NsV/tsJHRQrurV+oBsX5nR/+1j/8N66jAl6Fd1aP5Qk/fj2ln72sPE7/fHtLX3p2Uv1z8MtErtHFR0UK/pg7UAfrB1o87BU/121bjsEgMnXuC4NXgHEfzMBAKN3ulMAjFQQDJ3uN9GztTe3N87PNz3ueUafv3FOFd+2hUKSVPWlP/3hbf3wgw1J0q2NQ5UHrFYZRKNa4nSf715mckGfp3zWUyGbqVfvPH/jnH7zE5f0iStn6s99fHmu3lMoPC3vrx3o9uahjkrVvvow+LVfWTQMijafntRm34MKT08+07mH03fffqSKb3Xj/LyevrSoXMboM9fP6hefuKBfroWmYSj0metn9cyVRa3uFuvbzT7cONBf/LixHfCnD3cbHz/Y08OdY0nScdlv+t2G1Xrf/Mn9gRrGu67yA4BRC/7TZ/u+Mk36mgEAMD4EQ6grV23TmPHTaL6Q1a89c1HPRgKH0Nm5vB5fntOZ2c6Fcrc2Dusfv7MaBEgHxfjeKcMI38RmpmyRd6bWd2g2l5HnGZ2bz+tXnrqgL//cZWU8Uw/2zs/n9YWPnpMk3dw40Ddeuacf3tzs+PeG4raSZdhK1iY8D4Vc73/vl87M6OeuLemrz1+v/7vKZRsn8tefvahPXD2jj10Iwth3Vvd0b/tI339vo6mn181aldGN2nS0vePg39V+saz/9+W7+unDXd3eONSfvXhHL3+4pd2jin5yd0ebByW9u7pXbx7/d288qPc8Ch2Xq/qzF+80Tb8DgLRovbExzLWIHkMAABfYSoa6ctUf26j6Ueo24eiXnwoqHe5sHurO5qFWFguayWX0vXfX68+5vjyrqm/15v1drSwU9N23H+nK2Rl99vpZnZ3Lj+QY61ueTv/pHsiTFxf0o5ubTf1sri/P1T8Oe0Tls56evLiotb1SPQT4YO1An7l+tm3LU1Q1DIZato9lvKAqDIHwrnO3NyfXzs3q8zeWY7finZvL68xsVl/46LIuLgb/3sLg9Y17QWVQIeupWAuGshlTD4m+8NFlPdg5rn/t4U7QfPrHHzYmokWr+v7ujaA/UT7r6Xefu6LNg7L+6f0NLc/n6w3Od47KkqTX7uzoyYuLOi5XtXNUrk8sDL/eqyG6FIS2vrUT8d9CAON3XK423ayIsgqCnuC/yb0Tn/6eBQDA4Fj5om4StpL16/py0EPoqUuLuhDpRSQFbx6furSoStXq3VpPnAfbx/rRzU29v7avF95e1XG5qh98sKH/9NPVgRskS40AIztlydCTFxf0lc9c1fJ8fMAWLp7Dvjc3Lsw1fX2zx/S4TpVYXh9ByDT55NWg8qc1KAmbhX/lM1f1K09dqG/tazWTy+h3n7taD4VCP/+Rc/WPP/XYUv3ja2dnJUmzeU/ZjFefhtaqWxZTqvi6u9Wo6PsPPwkqh/7DTx7Ut6UVK74qVV9/9eo9vfD2I/3/7N33mxvneS/87xT0XrZXNrGLpChZimzHvcRJ7Dg5OVfiPzDvOSeJfRKX2D6yZUe2ZTVS7J1cbi9Y9Dr1/WEws4OyBbvYJbn7/fjyJRLEAlhgADxzP3cp1lXUVR0/v7mEXzcbYLvphtmxk/+rO8v492vzmz8QIqIe/Pb+Km7M51suc38VmWBTaSIievGYMUQArBNq3cAr33x6N/xt5TSxgAfpsBW4eL5ehSwKmEgGMZut4KOnVjnTT64vOOnctxYKuDKZQC+OasYQgI7pV252qdloM5AwHG0NINjlR5vZyBhqvVyWBKg691lt44kgfvT2ZMfl3zo7hPl8dcvXaCvuQJL9HgKsxuKKbuDEgDWZ7i9OpHBlMoFPZrKYz9VwfjSKU0NhqLqJj59lsVZqQOgylvnjZ7mWv3/4xOoJZmcEAcAfHmec7LDlQt3pedT++hfrKn57bxUBr4Rj6RDqqo7Xx+PIVa3beu/uCt46lmwJnmn7mFVpB7Hag229KtVVeCRxy8w6Ijo4xZq66WfqTnrnuXX7XCQiIuqHI3haSt0ozTOpoxgYam/mGPLJ8MmSc0IY8ctIhrzOyWbAK8I0rf+OxPxYamYr9MIODB21jKHtpMM+/N2VURxr9qsRBAF//foI/vbSCGRJQKmubvnzdv/h9rR9+3lm8+mtxYIenB+NbX/FLaSaAaFE0OtkICVDXnz19KBTNigIAgJeCa8NRTCdCuLCWAxBr4xYwANf82cGI1YmX8gndWSOdZMMeTAS92Mxb70fPZKApUIdC80pd+6PtnJDw+35AqqKjvWygk9ncri7WGyZkLZaauCPjzdKTBXNwP/5dB7XZ1uDU1ux+5O5ey3lKgo+eLQG3TDxZK0M3TBRrKv42Y0lvHd3dc9N7396Ywn/8fnC9lc8IKW6ivI+9GkjehXUVR2GCehG6/vave6wSsl2dnvMeiUiov3CjCECAGjNE6KjUkrW7nsXhwEAmXLDKS2bSgVxc74Aw2wtuXlzKokPHmUQ8EgYiPhwc74ARTOck+CdcKZncZXXIeht/Viyn/uoX942Y2izUjI74Mmne/997fQgaqoOURTwN6+PoKFuHugYjvk7eoLZDcSPD4QRD3pwdiSKSkPHTGajjCwe9ODqVAK/ubeKkE+CohkYjgWQCnmx1AwMjcYDmM9VoRsb/Y4+eLSGdNiH67NWWcdYIoBcRUFVsU7eVkutQd58VUVD06HqJh43y0rvLZVwbjQKn9yakaPqBkxzoxzv6VoZf36axVdOD+D3D9ZwaSKG86MxfPh0HfmqijuBAm4vFFGqa0i6epetl5Ut+6S10w2zow/UXvtp1VUdsij0JTvqpzeWAKBrhhrRYWdnLGqbZKxaPYbMnr6bes0yIiIi2gkGhggA0GguXnoJbhwmdlNpd3PpM8MRLORreG0o4kwyk0UBo/EAJpNBnBuNOpkAq6U6xhPbZzXYtGZkiAlDOxfxe5ApN5qL6O6r6I0SvdZ/9x3R4/pF8Mqi8zni90g9lzTZgcGgV8KxdNK57K9fH8HPb1pBhu9dHAEA/M2lEYS9svN62/2+vLKIgFdyAiSTqSAerZQxl61hLltz7isZ9KJc1wBYn3/u4JPt0UoZ95dLLVk/2YqCkZgVVCrUVCzka3i+XoVHEvCPb04AgNM0/XnzvzfmCqg0dCdnraZY95mvKs40PsAKTu80MLRSrOM391bx7fNDSId9zkkoAOd9slSo4cZcHl95bXDTnlFuumHix9cWMJEM4MunBnb0ODZjGHs/gf18Lo97S0X88xd2H1gyTROGia6N1In2U80ODLW9F+wj0R3jGU8EMJ+rYSvMeiUiov3CwBABgJPqH95lb5HDSJZEfOf8sPP3C2NRTCaDkETBmW6m6QZCPgnXZ/MYjQU6AhKbMY5o8+m9GIsH8Hy9iqeZitOrpp39vLafADJj6NVxZTKOZMjrTBSzdZsoFvW3XiZLIr52ZgAhn4zF/MYJVjrsw6OVcsfPxwIejMb9KNRUSCIwm7WCOG9OJ5AKefGrOyu4OV/o+LlcRcVILID3H6yi7sqIUnUTpbqKqqI7vYrmXA2zH6+WkQxZj9n+d1U3UVU0iAIQ9svIlBvO9e1soE9mskiGvB3H/VzWuu2VYh3psA9VZSMwVG5oiPg9eJapIFtR8clMFn/52vaBHvt52+4EdSfyrt5PVUVzgn6Vhoaaqnc0/u/m7qI15a6m6DsKbHXzwaMM5nM1Zi3RgbMDwO2BITer+bSAL59K76h/EPOFiIhoPzAKQAAYGNqJ18fjHZfJkohzI1F8MpNDVdV3/PxpR7j59G5Np0O4s1jEzE4CQ20RIDuDhbutLz+PJOLkYPfX94dXxrb9+ZGY1bg879kISrhLtb5/eRQ1RceD5RJG4n6MJwKYSoVwb6mI5+tWoGUyGdw0yyzolfD5XB6xoKelJ5HNLp2ytZd12e99e8JeQ9NRU62gRzLoxVozMLRarOP9B6t4azrpBLXs4z5bUbBWajhBqUYzm6mqbJRaFutWYKjasE5Mc9XOiX6KZuCnNxbx5nQCUymrr5cdEIr4ZdxeKEDVDae5frmhIeCROgKvlYaGJ2tlXBiNtQTH3feZr6pOYOgXt5ag6uaOAjWiYPUOy1UVBLyBba/fjf071VV9T025b80XMBL37yigRQRsZAx19hja+LPdY0gQhG03L9h8moiI9gtPSwkAUK5r8HvEfZu4c5jZvUZ6GVvP5tO7kw57sVJs4D9vLGJ2vdpRqmK/BO2La7t3lmbssfkKvVABr7TjrBG/d+O95f6ZsE/GQMSHL51KwyOJEEUByZDXaZod8FoTvbqVK54aCjuBj98/sBpIj8b9+Pb5IZwZiXQcd5cmOht5F2utfbLKdQ3luhVwiQU9qDR0KJqBj2ey0A3go2dZ57q6YaLS0PDL28v47HkOs82MoWIzM6fmyhj60+MMPnyyjtWSFWiqqzpU3UC+GaxRNAOrpToamoFrszncbI7TLjYbvBdrGm7OF3BvqYRSc2Laf36+iGuzOeiG2fLeu7dUxO2FIm4vtmZXrRY3sp9yVQVP1spYKtScCXH2fdUUHe8/WO3aXN5+7X73YA25igJFM/De3RXkKq2BrocrJWc6nbvsz22t1Oh6+U7UFB23Fgr4/YO1Xd8GHT2bTUV0MMhDREQvCaaHEABrJ5jZQrvjka2zwV7GoduBIfa86E0y5MWTtQrKdQ1/eJzBa0NhvDmddP59s1IyO2OII+uPDncTc68s4sxIBJPJzfuA2Vkg7my0v7owjM/n8lgq1PH28SRODISxkK/h+mwOxZoGw7QylNJhH9JhH04OhuGTRdQVAxCsnmQ35qxgyZdPpfHBo0zH/RomkCkrmEgGnB5nS4UaijUNsYDHCXYAwP/+ZM4pqZtOBfE8W4VpAoWaimJdxScz1sS0gYgPa6WG0+co7JdRrmv43YM1rJUauDqVwGfPcxiOWb9zTTFwe6GIVNjnTFJz+69by06m06OVMmYyFUT8Mr56ehB+j+Q8xkcrZVwci0EQBBiGicV8DdPpINbLCp6vV1GoqYi7SgKzZQVRvwf3lotYytcxE6ri4vhGMK2m6C3v2XtLRUymglgtNfDfj9bw5VMDmMtWcWoojE9ncvDJIr53cQQ/ub6A6XQQ755It2R1XZ/LYz5Xw1vTiY5NkK16lwEbQSXZNaBBN0yourGnLCQ63GqKFaRszy60s1fN5v960ev1iYiIdoKRAAJgBYYGmB6/K3bWTy/ZKFbvkP16RIdXLGidVMqiAEkUnGwIm77JVDJv88neLJOADp9g28n6G81yqM2kwz58+/wQUqGNsrNEyIsvnkzj/nIR081Sq7F4ACNRP/7XJ3MAgJBv437snkfuiWWnhsKI+OWugfeRuB8wgaVCHZIgOEGfPz5etx7zVBzv37cyVIJeCVVFd4Iw50ajuDKZwGy2is+e5/CbeysArDK4U0NhfPQsi6tTCayXGwh4JHwyk3OCG589twJIy4XW98/thQLqqoF40IN8VUXIJ0EUhI5pgKpuIltRcWexgCsTCayXFXibk98KNRXxoBd3l4poaAbG40GIgoCna1aQyu6tBADrlQZG4n5n4ttCvoZSXcV0OoTBiA8/ub4AADgxEEJdMzCzXsVMs9yv0tDxy9vLAAC9GRBuaAZm1q37mclUcX5UdTI20mEvshUFz+oafB4Rl8bjqKs6NMPEr+8sw++R8MZUAr9/sIa/uTQCVTPw+VweX3ltAJmy4kyscwccf/dgFSvFBnsX0absUrLtyr+2Ckru5npERES9YmCIAFjpzrtt7HnU2WVKqrbzXTzDNCGxjKxnqZAP44kALozFsJCr4VazB4rdXNrOGGpvAm7/u7rXOd70yhBFAaeHwxiN77wvTbfeMV5Z7OgvJooCQj4Jlcb2fcXeama0ucu87OlDum7iS6fS+PDJOiZTQYR9Mk4NhfFopYyQT8KwqwH3Dy6P4tpsDg+WrSCKR7Imr702FMZCvorlQgNhv+w0xv/+pVEAViBrpVjf9PENRX1YKTZaJiIlQ17kqyqSIS90w+wIDMWDHsQDHjxdq2A8EYRmmHhjIoZrz/NYLtaRrSi4OV/AdDqIiWQAmmHg6VrFuS/AymJazNfhkyVouomJZABz2RqyFQUz69WWPlPJkBfpsA8LmzTEdjfKvr9chCwK0AwTc9mq0zz88kQcg1E//vQkg8erZdRVHTOZKi5PxKHqJlRdw0dPrYDcz1x9oq7N5p3AFWB9Vz5YLiHil53fhWgzNdekQLeW+E6vCUBMGCIion3AwBBB1Q3oRusuN+2cXZKg9pAxpOnMGNoNSRScyUp2JkCm3HAaDm+WiWWXkjFj6Gi5OpXc/kq79LUzg3iwXOqYjLYZv2fjwHzneArPMhUMRHzweyR87cyg829vTSdxaTwOsdmM9tJEDIWqCkEQnAAn4J60J+BYOtyR/dN635t/tl8cj+EbET9WS3UnwHJ8IASfLOLsSBR3mn2DRuN+VBUd+aoKnyxiMhXEzHoVD1dKAICpZAgPV8q49tzqVZQKe/HOsRQEQXAaW4/EAvjJ9QUcHwghFfLik5kcbs4XMBLz463pJOayC87jcgdjfLKERMiLv39jDD++tnEdW9kVuKopBr5zfgi/vrviTDQDgGgzG+tYOoSZTBUzGSvzyC63A9AyYa7b4wCAUl3DZ89zcMee7elxRO3qSvfAkM007alkRERELxZPTcmZaOM+caGdk5snBMWauuMG1DozhvZsIOKDTxZxa74As5kpZJjdU+2TzfKgY+nQgT5GOryifg/emk52ZKdtxn1cemURp4cjznHZzitvDAI4PxrDuyetLCB38N7j6nUz1syKOjcS7Xp7gS6BoWTIA58sIh2ysqQGI36cGYkgGfIgEfTiymQCfo/k3GfE73GCYH6PhEjzzwu5GsJ+GQGvhKGIdVshn4Rvnh1ynhtJFHB8IIyAV8I/XB3DO8dTGE9s9Hs6NxqF3yPhW+eGnL5Hrc8dnPv90sk0vnluI5A2Eve3XPfSRAypsA8hnwzNMJEMefBPb004wbGhiL8leFyoqUiHN16HzV6Tdu6WMWxqT90omgHNMDedcAhsJP/stEJMABOGiIhofzBjiJzMCx8baO6K3b/m3lIJa6UGvn1+eNuf0Q2zow8O9cYjibg8GcdHT7N4lqng+EDYKtHr8rz6PRL7gNAL942zg3sq2fW6TjDbA01bHd9eWYQkbkztA4ArkwnEAp6WwFa3Pkz2Sa3fIzqlmn6P6JTQGSac/nSpsA9P1ipIhXybZtDYgaaAV8IPr4whW1Uw1CyZG4j48IVjKfzn54vO9UdifozENoI/k6nWBuJnhiNYylulct85P4RU87HYQfoTA+GW31EUBfhkq1+TLRHyIhrwQBIFvDWdhKIZ+OOTjHO7Nrt8cCDig0eyepxpuglNN8HZDdTOLiML+2U0ysqm1+tl/DzH1RMR0X5hygI5GUNb7WrR5twnHZktFn9ubD7dH8fTISRDVqNbgCUd9HIbivp3XHrWjXcPn9Getg8cv0fa0TStEwNhXJmM48xw1Hlv+WQJkigg2AxyDTQzhSaTQUylgrgyGd/09twCXsnJdrKFfTK+e2EjuP7Fk+mOCWJuI7EAQj4JgxGfExQCrMDXZDLY0qvI5n4eByM+HEuH8M7xlNMPyiuL+NrpQbx7IoXzo1EEvNb17ebgAY+Er54exBea19cMnqlTJ3vTLdIlati+fyGwmIyIiF4w7nERGnbGEANDB8YKYPD53itBEDCRDODGXAG3Fwp4ulZxTlaJDhvvHqLJsiQCrh46O/28F0UBZ9tK1MTmWW3YJ6Oq6E5gyCuL+GKz7G0v3KVvmwXDvnQyjbpmfXf97eujHSfax9KhTUtH7edxOObD188Mbfo4pps//zRj9RmyJ5JF/NZ/7UBZ+yhyerWZptmX6V92YCjYNTAkOPdlwuyhlEzguHoiItoXDAyRK2OIJ9QHRTdMyBJ3CPvBPim1pw9Vt2n2SfSq2lPGUFsm3W6CTHYvL/skNh70oNzQnEyaftlJ0MpdUrbTPk82+3lsz6LaTMgro6YoODsSQdAr4cxwBACcz3D2GDo8Hq+WcWshj7+7PLbn4JA9BXOrzQoTvZeSERER7QcGhggNzYAo7O2kgzYomrHtc2mNq2cgrh9SIR9EobUZLNFhtJeszvZyrF6DKQCcMji7v9CliTjOj8Z2/Zg2s5vH1oteA0NfPjWAhXwVEb8HF8Y2fl+5mfXJjKHDI1dVUFMMqLoJr7y341DRmj25drDp1ss9sccQERHtBwaGCA1Vh48TyfqmqmiQRQ+eZso4ng53PcnR2Hy6byRRgM8joqZw154Ot72Uknn6kKF4cjCMaMDjNIv2SCJexZkFvQaGAl4JJwcjHZfbEyk1nWfqh0WtmXGq6ttv8GxHMwwIArqur+x3oz2unoiI6EVjYOglZI04NZx+BvutquhdxxnT7lQUHaulBj6dyUEzTJwZ7hwhrRsm2GKofzySiBoYGKLDzQ4yp8I7G6nuttMgyFYEQXCCQvvtG2cHnV5G/WYH2PbaqF5ySsl4an9Y2JPE+hHsU3UDsih0PY7dF1k9jXZ2mxxXT0RE+4WBoQNSaWjQDNPpxWAvGARBQL6qIOL3OIvUn99aRE0xDmy8dqmhIRXq/USDNnhlEUqzV1ND1dFoNnm1L2un6WZfTtTIYp/oRQMyvnl282ayRK+6718e3VVJmewKgpwb7QxWv2z2MwDVr3iTxyklY1D6sLAbRqt9eE0VzYRXFlvee+1Mp5X0zg5Ka1w9Q0NERNR/PDPdJ4pm4OFKyfkC/8WtJfz85hIA60v9Xz+dx0fPslA0A7+4tYwPHq2h0tAAYNOSmKqioVhXOy6vKTreu7uCTLnR8+M0DBOVhuZMWaHd+ZvXR/C9i9aIZUU3tp0aohvmlotF6o2d8h/2yTsawU30qgr75F0Fle0eQ+dGo7g8sbNx8ofdXk+w7c0cZgwdDqZptpSS7ZWqG/BIYtdycns8/fXZPJby9T3fFxER0V4xMLRPbi3k8elMDosF6wtfbaYl5yoKlOaC4+laxVmELObr+I/PF7e8zT8+XsfPbixhLlttufzDpxmslhqYz9Vwa76A+Vy168+vFOt4slZuuazU0GCaG81EaXf8HgnxoBeCACdbCNhY/LmZpgnNYMZQP9kZQ3vpwUJ0mNmBaAakNz6X9xrOYY+hw6WhGc4QA1Xb+2uqGVZm+FYli+tlxbnuzggsJSMion3BaMA+qTeDA8WaitHYRkr8XM6abGKrKFrLz+UqivPnRysl1FQdF0ZjEARgrWRlBC0V6phIBqFoBq7N5rBcsC5vqDqerFUAAF8+lUahpuLcSBQPVkpYLyuYbQaUjqVCzg5WsWZlILkfE+2eVxKh6IZT6tGtZMEOEu61vwVtsDOG2icvEZHF/ixiFQowGvfj8zlgPBHY0+2IogBRYMbQi2KaJm7OF3ByMIxQHza37I06oH+lZD6P2HXQRPtF/chQIiKi/rLLi49KNQIDQ/vgl7eXkK1YAZdcRXGaGQLA7YViy3WrbYGhDx5nnD9/MpNzfuaia0SuXXL2aLWEp81AUMgnIVPeCCp98Mi6Hc0wcXex7T5VHWGfDN0wcW02B58sIh5kYKgf7F5DW2Wu2KON+zEliCwbgSE+p0Td2A1wDUaGEA96+9bDTxKFA+0xtFKswyuJSLAvIFaKDdxZLKJQU/GXrw3s+fbcazV3Flhd1eGTRQg9NqfSDAMRSd5RTyt3pvFWBHafJqIjoK7qUHVjXxIXTNPEh0/XcXooglTYt+n1Vkt1vHd3FR5JwD++OdH3x/Ey4vb6PnBPE8tUFJQb2qbXXW1mAdkntuV69+veWigAsAJApYYG3TAxl60i4BXxVxeGkQx5Uah19h9qDwoBQLX5eIo1FZWGjjemEixr6hM7MLTVus3eiZQ5lqxvmH3N89DgAAAgAElEQVRFtDM8p+wvWRI6ggjA1v2LKg0N2YqCUl2FYZgwTavX3635AuayVRTrKmqK7mwcFaoqrs/mUKip+M29VfzX7eX9/aWaVop13FvqXENsxjBMfPhkHfmqsv2VAWi6gUeuXoy9stc8hqs3UK8Mw8SD5RIUzUDVnTHUzOCpqzp+fG0Bd9rWUp/P5bGQr21521tOJWt/HHxjEvVdVdGcz+TDarVYx89uLmJ2vYpf3l5qqTx5EY9ltdSfnmm/vruCn95Y6stttSvWNcxkqviDKxmjnR0UAqxKD+OIfEgzY2gfpMJezOesBUO5rjmNBZMhL7Jtb9iZjBXc+f6lMfzvT+YAAO8cT+LPT7MAgG+dG8JivuYsSsbiATxcKTvXvTQRQyLk3XK0vSwKGIj4MBL349rzPCrNxU+1+WHJ/kL945NF1BR9y4WufRLB7Jb+6dbLiYg22OemzBjqL0kUnSzQYl3Fz5oL2YBXxA+vjHdc/+NnWTxe3ej1JwpANGBNJbX7zbinXP7o7Un896M1lOoaVoq9D5jYi9/csxbFQ1E/kltkKJmmibVyA7Io4lmmAp9HxBuTG9c3DBMPV0s4ng47m2AA8NnzHJ6sVRDyyRiNt5b1rRTraKgGJlNB57K6qsPvkZCvKgj7ZKw3B24s5uv4yfUFfPFkCqW6Bo8kIh32brkTbN/eHx5ZPRrLDRXlhrUmEgXg+XoVK8W6875ZyNdwoZm5remGs+l2fCAEVTfw5VMDWC5Y1x+K+nFzPo+aYsAjix0bFyHf7ksSrIQhvof3U7Guoq7qGIzsz2TEmqLD79k8A03RDNxaKOD8aPSVKV8p1VXcmCvgC8eSkEUB2aqCtOv9t1yoIxbwIODd/vcxDLNrw3bAeu4qitZy2+1m16swTBPT6RD+7/VFiALwT19ozRB9/8EqkkEvQj4ZJwfDO/odTdOEYW5sRNZVHd5Nmsv3Q75qVZyMxLYueb69WECxpjlBjpsLBXxlFxmUDU2HYWBHr5FtrdTAg+US3j2RgigKeK/5nWFn5JqmiUJNRTxofR/UVR26Ye6o9NdOlKg0tL6UCrvZlTdb9QesNloDir+6s4yvnx2ET3413pO7xZSFfWAvoOxSoTuLRYR8EoairR9k9uj6oFduWTiEXRPCwj4Zp4cjGIn78dXTAx0pdRGfx7mezb5f+/4mkkF87cwgTg5YH34fPlnH/eWis8MW7OFDgLbmlUXkqiruLZU2vY7W3Ilkllb/sH8K0dbsDEWGUPvLJ4tOVnCmtBG4qSmG81lvB3mWC/WWoBBgZYrkq6oTFHJfH7B2LUt1DYmgp2NjqR8+mcliqdCZ+eLO+rnfzBqay1bx2/srWC3VUVN0vP9gFZWGhidrZbx3dxW3m5nN7TvWS8U6rj3P48OnVjbRv3w0i0JVdfoeVrpkVf/m3mrLbu712Rx+fG0BM5kKfnFrGddm81hrm8T6x8fruDlfwGfPc/jVnRXnsfz85hJWi/VmEKiOuqrjwyfr+N2DVSdr+8FyGQu5WrNkzMpGWik2nB6O62UF799fRbai4CfXF5z7nMlUsJCrQdMN/Pb+Kn5zbxWGYTptA0TB6kNle2Mqju9eGHbeiL2eUwqCwO+5ffazG0tOpkCvHq2UcHexiHtLRfzy9nLHwJdCTcVPri/g9kIRpS5ThgFgqVDDg+USfnxtAX94lMGD5d1l1emGuWm2zF4mMtqfDaW66gTF7y2VMJut4lmmgj89Wcev76y4Tr6t98av73ZmOmptvbVM08T/+mQOnz3POpe5Px9+fXcZv76zsunjb2g6/vA4gz89WXdlFLZeZ6VYx1K+jjuLRXz8LNvx+eP+/HW7NpvHe/esz5VHK9brc20211O2oqob+Hwuv+Vn+ex6Ffmqgj8/Xcf799e6Vn7YqoqG5UKjZbK0ADiZqLZCVcXj1RJU3cCD5VJH9ktd1fHvny3gZzetIUhG89iZyVTw/v3N3wvvP1jFbLaKXFVpub+GZj0ntxYK+MWtZeeY+dWd5Y5BS8uFOj54tIZPZjZe85lMxfnzSnEjA6mu6ttmatoqDQ13FguoKTrmstWW37nUDDoZpvU8PctU8CxTwe2FgvN72Bmkp4etc+dcVXV+7jBjqsg+SId9CPtlvH0siU9msijWNEynQh1R2NfHY7izWMDxdAgA8J3zQ/DKG7uPgLXoFEUBXzs9CAAdCzj7NieTQTxYKSHklVCoqVB1E68NRTAU9eO1oQiA1sa8157nMd3ciQu8IjsSr4JuKePt1Obry+lA/bPxtHPFTNTNiYEQSnUV50dj21+ZdmwsHsDN+YKzAHWbz9Wg6taQiMlkCHVNR9Ar4bsXhrFabOAPjzM4NRRGwCPh5nwBsiTA75EgCkCxZi1AP3yyDgC4OB7Dfz/cPO19N1aKdTxaKWN2vYp/uGplN12bzeHJahk+jwSPJGAo6sfMehUTySr++DgDwwTWy2uYToew1DZN1c6UXq9YJwmCIKBQVfFg2TqxWcjVnP57j9fKziCG5WIdsiTi3lIRwzE/Tgxs7OCvlqxMA3uz5c9PreejPcDWze8erGKxmbH92/urMEw4wShbxC+j3JzOCliZvPZ54pdOpluCU0uFOlQ96zxuYOOk8/98Ou9clnUF1VTdaMkMiQU8zR1n66RVEgUYnGr3QtnHartumSvr5QYifk9L5pthmLi7VMSJgbDTG9T20dMsIn4Z12fzODEQRqFmHRu3Fgp4uFLC378x1nHf7pPP2WwVs9kqRAEYjPgR69IPtKpoXasGPpnJ4ulaBX//xhiKdRWDET/qqo7FfA23F4s4ng7BJ4vQTRNnhqMArBPvx6tlnBuJOr/7g+USbs7ncXYkiqpi/fvp4QgeLJdwbjSKyxNxZw22Uqw7nwMfPVvHhbEYPM1NiUpDx2/urWA45sf50RhKdRX/dWsZPo+I14YiODsSdYK9D5bLuDqVxOy6Ve7zjbODGIr6UWlmcfx/H8/hSyfTLRmFAJy+qwDw85sbZUi6YTob8I9WWj87yq6MlJlMBR8+Xcdb08mOTKJizQrgNzQdd5vB8ocrZTxcKeNb54YwELE24xXNwOPVMs6ORJApK/j4WRbT6SAGwj78/uEaVN1ErqLgq6cH8P6DVUynQjg+EMZCvoaVYh33l0pIuF7nz+fyEEVrc2c8EYDfI6HS0BD0Sk4W6ZvTCbx/fw2AFXz8t2vzmEgE8RcnUtANE797uIpKQ8daScGzTAUPVko4O2ydG54aiuD+svX5quomClUVN+bzzutoP0d3Fgoo1FR8+/wwrjVLm+2Mm7VyoyW77d8/W8APr4zhQfN27awh+/WrKToCXglP18pOhQwAvDWdRLai4E/N7z0A+PNT6zM3W7EeOwD88MoYMuUGnq9X8cWTKQiCgPfuriAZ9uKNyQQA6/hfzNdxY67QvO0EvLKIdNiHcsP6/NUME396so7n663fC0NRP55mypAlAVenklA0ExPJwJaZaocFA0P7wCOJ+P6lUQDAN84M4WmmjBMD4Y4x8hPJICaSGx9qdtqzuydR+5fSSCzQUmpmZ/sEvJJzn/+3uZsV8csttw8AyZDHaYy9WKjD79m/NMijqH3nodumhr1Dwgla/XM8HcZqsYFzIzzpJepGlkS8OZ180Q/j0DmWDuHmfMFZfLq5F7f2gnY6HYTfI2EyFcRf+YcRC3ggigKOpUMt6fKGYeKnNxdRaeiYTAadkw7bZiezvWjPZtB0A/eX7BMEDe+eSCHokzCfqzkDLd6aTuCTmVzHyVXr7Zgo1jV4JAE/v9XaI2KuGZhxZyTNZWuYy9aal6vIurKn3ru76mQ/i0Lr7r8sChiM+rCYt4JH7X0W7aBQOuxtGc7hNp4IYGa9gppifS83XNkCY4kAvnAsiRtzeefy9tuJBz3IV1vv1w5anRmJ4GzzhLudXf7ca+KGsIufOQoer5YgiyKmmxutO/Wzm4vwSCK+c34Y5YbWsmFXU/WW92ShpuJXd1ZwYiCEt4+nAFjvoY+a6/HN+ol++GQdlYaO9XK25fKGZmC9onScbBbrKkI+CX9xPOWU5tgBpy+eTGEkFsCfnmRweSIOVTfx/+6u4N0TKYwnAri/XMLp4Qg8kojlgnX8//iadU7wvYvD+PhZ1jmGH6+WnayIqWQIkijgwyfrWCrUEfbJmE6H8Hi1hM+eW/d9c37jM84+4b+7WETQKzllP+5gwnKhgZXiKv6i+VwBVsP4TLmB14YiuLdUgmaY0Bo6rs/mcXwg5Py8RxKgaAbuNYPK3bIK7ywWOgJDS4Ua4kEPzo5EnaA6APzrp3P43usj8Igi5nJVDEV9TlClWFORKTcwn6uhUFNhmlbJb1XRMJkMotzQMJ4IOp8BT9cqqDR0nB4O4+laxXkNRuN+6Ibp3G40IGO11EChZpXZJYIeqLqJZMiDtVIDxbqV7bNcaKDc0FqGExXrKvweCSMxP5YKVsYlADzL+BD0Sni+XsXVqQTyVQVeWcRw1I8TAyEsF+tOYPFZpoKlQs2Zkm1fBlhlWvYxlQh58Xy94nyWzeWqWC629gl6uFJypl7PZavO94QtU1IQD7SWG//+4aoTRP/wyXrLZ/daqYHJVLDleAGsTCM7eD+WCCDil3F/aeMYtF2fy2Em05y0XQhhIOzDaqmB1VIDb0wmMJetdnwuX5/NQzNMxAIep5zXNDe+k2xLhbpzrEcD1vv/L06kcFQwMLTPAl7J2aEdaNYrp8NeDEY3r13eblrVkOtnu2X7XBiL4eNn2a41md8+NwzVMPDvny1A0YyWqDTt3ZXJBH7pagyqd1nB2R+UzBjqH68s9mUqDRFRL0I+Gd+9MOx87kcDMoo1DWG/jIhfxlQyCBNwTh5Drp1992Sx9u9rURTwnfPDeJap4ORguKP0uKEZe+4/Yu/eNjQDVUVzThpswzE//B4JXzqZxpO1MkRRwMnBzqwIt+MDITxdqyDXNnhjIOLDWqkBrXl2sFZqLQOLBTx4czqBm/MFp7zLtlJsQBCAd0+k8TRTdk6+xhMBKM2NlhODIefkCQDOj0axkK/hneMpxAMePM1U8PGz1hNz+369koQarNvRdBPxoAcNTYfU/H3Xyw3npAgAJpIBBDwSHq6UcWY4AlEQWoKAT9eskyx759qtvR/ebnp+2SebdjuCo+bxagmJYGsPqY+fWcfkdoEhRTPglUXUVR3XnueczDzDMPGr28stgcFKs3GxZpgYivqdYEjRldFjXwZslLx8/cwgfusqv6k09JYAY9gvO4GUxXwNIa8M1TAQbbaKKNY0RP0eDER8mE4HnRNgALgxX4BumFjM11FprGOqGRh5nq1CFATcnC+gpurO5W6rpUZLYNPdbN1dHglYQW1VNzCbrSIe9GAo6sOD5e7B4E+bnwfu3mg2SRCQr6kQBCsDzyOJ+O39VTxeLWMuW8VEMoB4wItbCwX8+2cbj0HVTbx3b8U5ua8qekfZmTsubpomfnVnGdmKitPDERxLh1oCQ4YJLOXrzdIh4AvHkjAM4Oe3ljo+z75yegCz61XcXig6wZp//sKEUx5ll3adHYni6lQSa6UG/t/dFScQbSvWNGTLCmIBD+qqjlxVxVQqiBMDYfz2/iqeugLz9v0kQ16cGgrjo6dZVBo6xhMBrJWErp+bK8U6CjUVAxEfBEHA28dTWC7UW469+g6mHT5cKaHS0PH28RgeLJfwaLXU0XvHHQj68Ok6fLKIhmYgGpCRCHqRKTeQrFjfZ9+/PIqf3lhEtqJiOObDcqEBw0TL6/HHJxn8+an1e/k9ovM481UV87kqhmM+p0/SuZEoPnqWxYIriOR+T3w6k23JMq0qmrORYXtjKu58PxRqKgo167UYjQcwHPVDlgQs5a3G2Q9dmx56ex3iEcDA0AGKBTw7GpHr2WZalTsY1C3b5+RgeNNmaqIowCdKCHolVJupfNQ/yZDXSbEFui/67A8aNp8mInr1uRszf/nkQEe5h2GYG4GhHhoP+z0Szo50zzjpR2CooemI+GWU6tZCulBTMRr3Oyc49u1PpoItO/NfOplGuaHh1FC42R/CWoRPp4N4a9oq/7i/XERdtTafvnQqDZ8s4d8+s8qt3Bk8b00nUFV0XJqIAwAujJlOSYQt4BURaGZZTaaCKFRVfPg0g0sTcczlqljM1zESC+BHb0fxLx/NAgAuTcSd2wSsddGjlRJyVRWyJEASBOskvS0gd2UyjjPDkZasHF/b8/z6WByxoAenBiPOa71eaeDBchkjMatcZ7MdZvtkdqMZ/GavTnd2VtSnM1l84+xQbz/8krODNlvRDdMJAv3TWxM9ZbxnKwp+eXsZ755IIVtVMOMqH3maKbcEhQCr+awd8PvR25NYbPY2qavWgJEHKyXkqypeH49hrdTAUjNDJ+KXEfCKqCkGZMkKBU4mgxhPBFBpaLi7VHQCQ0sFq5yzoRn40duTMAwTxbqKEwMhCIKAd0+kMZOxjumpVBDP16tO9kShpjqZDevlhhMofLRSdjL6jg+EYBgmZtar+Hx2I3Dq1p5td3bEyuZZyNeQr6oYTwRwZSKBkwMRLBZquDmfh25YWRTuk/3XhsK4vVBsGbSjGSbuLxUR9XucCobRuB/Xm49lOOrHycEwHqxYkwHTYS+OD4Tx8bNsS8bHzflCS8YSsBHYLtZV/O7BmvOcTiStZs3fvTCMT2ayTv+2lWIdqm4gHvR09Gu9MBZ1gjNj8QDG4gGsVxpO4LDU0JygV0MzEPRKTvneQMSH710cRtAr4/7yRjDp8znrdzw5GEYi6MEnMzkMRf0YjPjgkQQn2Hx1KuG8pt8+N+QEuwHAJ0vweURoDd05ps6NRlGoqVivNFBTDBxzBUPdmaX/883xlhLXsF9GXdVxpfm5aAfEZjJVeCQBk8kgchXFCYy8NhTGw5UyPJLgZDplK1b52KWpOEbifvhlCY9WS3i+XsXnc3kkQx6EfTImEkHMZqt4+1gKP7+55AS2bKYJaM0P2b98bQBhn4wfX1vAfK6GYk1rCfT4PRK+8toAZpr9q2xfOzMAjyTij48zLcdGe/bu2ZEITg9FsFZqoFzXkGseV8cHQi2NvSdTQWdqtK3SONwT7bphYOgltN0XXT9Kv+zId3t6Ou2dOxOoW4M8ewzudgFAIiJ6NVydSmCpUOvaA8T9nR3YYoJoL6zv8L1ljCiagfFEEKeGZGc39cxwFImgFz7P5t9P7iDRRDKIi2Mxa+e/OXlGEgWnZN0qB7Ae5+nhCCoNDaNxvxMYmkqFWoIBQxFrAprVzNQqH/vm2aGW7nGxoAffvTDiPN7JZNA5SfvbSyOb7vLav9NkMohzo1HcXiggFfY5GzhfPpV2Tl7d2QgXRqMIeiUnM8Ju9Op+rd+YTOD18fiuhkqE/TK+fW5nQR47eHHYpsnaWRdfPT2A0XgAZjOzo329627YvJCvYSIZ7Hi957JVeGURyZC35fWYWbdOxD+ZyWKyrc1Ct1LQirKRGbSYr6Gq6PDKIkp1Db9/uOYEUIdjfqi64QSGgl4Jf31xFCZMyKIIVd8IePk9kvN440FPS9P5Dx6tOSWVQ12qCs6NRDGfq0LVzZZMG7sE6MGyNehG0QwnMz0RtDYrZcmahujOVrIDTV8/M4hcVcHvHlgB2SuTCWQrCkp1DQ3NQDzohSgKiAU9iAU9mEwGkSk3MJUKYTIZxPVmv5mpVAinBiOQRAHzuarT8sJoZujY3jmecsrbkiEvBEHAX10YhiRaPdYamo6b81b20Q8uj+G/bi91zXxpaAZ+fWcZyZDX+Z3+x9Vx57lOhry4Mhl3GokvF+vQDbMl2B7ySag0dFwci+H4QOuGeirkcwJDt+cLLb3FEm1TGu3PvkSwc3rjcNSPiWQAHknERDIIURQwFg84gcnpdNAJDImiAL+4EYj2e0Tn8+nSeBwNzcCpwTDuLhWdDJpUeOM+JVFwAjmyJOIHl0dxYy6PmfUqzg5HcKrZc9Y0TSi6gWeZCoo1Da8NWeWHAxEfHq6UEfCKuDqVcDJq5nJVpMM+p6fcVCrobBy4f+evNvvhvn08iUsTMYR8Mr5+dhD5qupkbF6aiOHOQtEJFtmllGG/7Gyqj8Q6j/+pVBCCsPHc2xl23zg7hAfLRZim1fPJnfl6fCCEK83MzS+fsjKQHq1YQc/hLu8x+7FcmohhIVdzevQeJYfrm+WQ2Wp94ZGELUfUb+fcSAz3loo4fQQP+v3mnjCnd8ni1AwTotCfAB8REb14p4cjOD28/fdpaA9Zuu5SjcYOSgS2o2gGfLKIkwNhJzA0GPFhuMuifCsXx1t7u9lBF68sYjq1sZt9dcpaoNvlELIodGSIiKKA714YhqIZTuPp9t39du610FbX9UrWc++TRUT9Hrx7Ig1gY2TBZhnUsmQ1x7UDQ92+uwVB2LYNALAxFdB9zbBP2nH21+vjMdycL3RkMb3q7GPi4UoJo/EAbi8UMbNewTfPDuH+stWUPOyTnRN1AMiUG5hIBlsmb2UripPBJgrA914fcU4g7RNpVTc7eqg0NAMRv4xowONcz10aZGfGnBwM4+5iEYv5Os6PRjEU9SMd9kHTTdxbKiEVtgIdXnnjFZbE1tfqeDqMxXwdF0ZjLc3N7aAQAIx2GVEeDXhwdSqJ5UIdb04n4PdIODVklZl++GQds9kqUiEf3jqWwG/urSJfVZ0KgzenEjg5GEbEL+Nfm1kkb0wmMJ4IIOCVEPAG8OZ0wmkO75FElOrWa9JeshjyyU7pqyQKXXvXHR8IO31zIn65ZRPa75Egidb62A6ouEtpfbKEH1weQ121qhrsNbWd3fi3l0bgkUT86s4yMmWrZDXotbJK2j9P3O8ruzxqNL7x+fbtc8PQm/3aumUP+j0i7i2VnCCOnQ2V7BIAsv/dfs6OD4RwLB1yHoO7zHE8EcTMuhXA9MkS3j2RagnGC4KVVeOTJdhJLPGg17l9dxllsi1I9f3Lo87PhHwy3pxOIuCVWjKLBEHA+dEYxhNBFGuqExAfiwdwbjSK00MRCIKA0bh1HE6lWks03c+rOzBkX+6RRCcomw77kA77cGshj5piYCQWwLmRKCqK3lJVMRTxoVzXEPCKXctkBUHoeByAFSS/OpWEphtOtpNdutwtA/HU0EaArF0s4MHfXRlF0Csf2UEdDAy9pL57YRj+LXbs/uGN8T3d/sXxGC6MRffcvJI6uXeoupWSabrBxtNEREeIvdDfy4bO9y4OY63UwB8fr3eUvfRK0QwYphVskiURb0xZ5QX92LA4OxLdtAQOsDIlAGyZleSVRYwnOvuk7IWdpNsRjBK6X97uWDrkPPZd6/L09rIOuzAWw93F4q56E71opbqKckPDTKaK82NRRP0elOoq5nM1px9VpmxNtJvNVlGqa/hkJov5XA33lkoQBTgna7GAxylXcjewfe/eCkTBOgl/ulbB/aUSTg6G8Xi1jFJdw7nRKO4uFltKRGRJcHpLvX0shYV8DVVFc7KIRMEKHAW8Ii6MRhEPeJAKe1uCkMMxP/7xzfFuL2+HyVQQ/5ycgCAISC17sV5W8Pp4DF5ZRMQvQxSElvfht88PYb2sOD2v3K0i7BPxq1MJrJXrGIr64JMlXByL4YNHGcRD1mMURcEJILx7ItX8faSWE213doT7vWA34O2V/dh8Xd5Xf/36KIo1tWUj1U0SBSdYZGfqXJ6IYywecJ6bL55M49d3VlBXDVyaiHVk8QAbrTfs3mdeWcSAK6iyVTsNv0dysqfshtKTSSuQMhTrXm0R8sn4qwvDiAY8m/5uADAS90MUNgYItffG8kjWJoDPI2Io6sdsttoSuBqN+XFpIoa6ajSnHG5o/7tXFp2smXaxgKclCCNLIi67SnDbfeX0QEdv24BXwkQygKnk1v29Lk8k8OGTdYR9ctdA3HDMjydrFVwci+/q3FSWRJweDuPBchlvTSeQKStde21tZy/f0YfB0f7tX2LtEeB2/Vi8MSi0P9xfBoYrxfnxahkBrwTNMNl4mojoCPnWuSHM52rbBh+2EvTKGI1bP29nDlVco5Z7YZeT2ydtZzaZnrUfPJKIsF+Gfw/Pxa40v47bT5y+dDKNR6tlRLZ5HvsxmcZuPu1ef4m9rsWEl2syWU3R8dObi/ja6cGu7QkMw8TvH645pVYAsFqq4weXx/B+szdMwLtxXC83m+oCVu8m53ZMYLFQQ9gvYzDqw7NMBc/XKy0TizTdxNfODGAkFoAoCHi8WnamxAHWCfVyoeaUOgLAQNiHpUIdiaAXXlnEsbTVl8cus/nuhWFkKwpGYgHI0uaTz3opI7Rf/2+eHUK+qrRkgLSzMy62EvBK+LvLY87tTiSDm/Zg2snkNvfnlF/eXXaa/XPdnpewT95xOaTddDrsk1t+H3fWznSXTBL7vv/H1XF4JAHFmopEs3StF984O+T0LkuGvFYAcIvb6Bag6va4Tg2FIW/SUsKaygZ4JRHvHE/i3Gi05TWxM34O2li8M5MN2CjT2sqxdKgla6ndVCqEqN+zo+dvM29MJnBiIIx40Otko1FvGBgi6jN30Mdd+m7X1x5Lh8CYHBHR0bGTk7ud8EgiBAFQdAO5ioL/ur2Mq1OJHZWxAdZJvKIZTpPNF1WSdGk81ntApE/ad/LjQS/e6lIOsx+6/cq97hM1B9334dH0R6bcgKabuLWQR8TvwUQi2FKOWFa0lqAQYDV1vTmfd3rD1BTDGSF+xzW2u6a0ZsatlxVMJAOYSATxaKWMG/OdvYHshrJvTiXglUUUaqpTHpYIefHFk2k8y1TwdK2CqqI7gQt39ogoCnjneAqPVsqIBTz7dpIpicKWQaFetAcr9rKBvFFSJux5I3qvTfLlZvZM2N85ufHb54fgk8Utg+N2QOVbO+zj1Y09sMcri33bVL86tflnzhuTVnZNyCdDlsRtkwUOi70EhQDrPcCA0N4wMETUZy0ZQ1229UyYzNYiIqJd8aBHunsAAB01SURBVEpWQ1s7qyJTbuA0dhYY+unNRWi6ia+etnZ4u5V5HIRuvSKOEvcKQOpxPSAIwgvPGFI0A797sIo3p5POOqdU17BcaODRSrllAq+7B5DbbVcACLBKmbIVBaulhjMOG7D6d50cCONXd5eh6SYSQS+GY36nj4ib+6kURcEpi/m3z+ahaIbT++T18TjOjURR1wxUFQ2z2WpHM9p+BXNfRfbnwl6m544lAnhtKIwLY3vLbPnm2UEsFepdM496eX32su4+PhDC7YXili0++mkiGXT6/hAdJDY6Ieoz95eX3m319vJs9BER0SvG02xCbZ+Q95J5YzdgtU/W91La9qo50ezNMvgCp7E6zaddL1mvJ6wCtl5G/PrOMm4vdGbS9NNyoY5MWcH12ZwzMWqz0c52o/Rzo53liu5pVfaYcMCatGQvpc6NRBELepzm3nb2RDK00RtlKhXEycEwvnN+uOtj+NtLI/jhlbGWy2RJRNgnYzDix4/entxVSeZhZX8u7CWrz25MvdeMoXjQu2XPsoNwcSyGH1wePfL9Z+jw4xFO1GfujKFu4+pN9J46TkREBFgZQ4ruDgz1fhu5qpVt9KIyhl6Eoai/JZPlRegWBOq5lGybHkOZsoJMWdlzpsZW8jWr8bMoCKhtkhFks4OQk8kg7i62ZglNp4JIhbzQTROiKODqdAKKbuDsSBRXJhOQRcEp8To1GMHN+YIzBSnsswJDfo+IL55Mb/kY2vtK0dbswNBWDZSPEkEQGDikI4FHOVGfuXsM2ePq3QGiF50CTkREry5fM2PI/n7Z7ORN0Qz822fzXXsQrZcbkESeML8Meu3hYgWGXuxCIlO2SriWCvWO/kEAoOoG/vvhGhIhr1MqF3dNP7o6lUC5oUGWxJa+Ij5ZwldPD3a9z/OjUZwcDDsZKJFmzxnN4KKq3+zMdw5KITpaGBgi6jN3Tba9o+teuJgwnckkREREvfBIIiqKBr35vdIeWNB0A/eXSxiMWiVTj1fLTmBIFKyhCJmygpCPQaGD5pSSudYAvTefFl54Rbrd32ozf3qyjpViAyvFBmRJgE8WIYoCQj4Jo/HAjpuluwmC0FKWZDcjNhgY6js7IBR1BfOI6PBjYIioz9zjJ+1dPbuvg3VZ98kkRERE2/G29Rhqb148m63i5nzBOXF29xHyyqLTE4b9Mg6OHZATNiJDjp57DL3gcfWmaaKuGphIBrBUqLesb2wLuRrOjkQwl6uhXNcQCljH2g8uj3Vcd7fCzeOXTXr7Lx704sun0hiJ+be/MhEdGkenuJzogEhdSsk0Y2PkqgkwX4iIiHbF65SSNQNDbSkn9uaEPQp8sz5CgRc0qv4ospv4dssW3t1UshcTGXq6VkahpsI0rZ5N37802vV6sYAHVyYTONvMDKoqWt8fiygK+MHlUbxzPNX32yYr4CZ3mQRGRIcXt4uI+kzuMq5ed5eSmSYzhoiIaFc8kgDDBGbWKwA6M1Dbp2G6/+bO7rCb+tIB2OI7v9fJTy9q+fB0rYw/P806WSR+WYLfI+HiWAzpiBfv319zrjuVsrJ4Tg1FUG5oiO1TSRIbAhMR9Q8/UYn6TBQFyKIAzTC79hiyMDJERES9sxvD2uPB2zOGdFeGKmA1oba5v4s4cejgWMEf0/nqbx1X39ttCc2ytIP2cKUMAM4UMr/XOg4vjlvTz945noSqmwh6JYwnAs7PXZlMHPAjJSKi3WCOINE++J9vTeDEQGiTjCH2GCIiot0Z3qbvhx38OTtilfGozZpmrfnfVNiaAsWJQwdHbAsICS3/1mvGkADzANtP22VrpbrVcLrYbDzdXop4fCCM08MRTCSDPfdNIiKiF48ZQ0T7RBIFV4+h1kUcl0xERLQbUb8Hr4/HcHO+AKCzEbFdLnZpPI6GZmC5OU683LD6vBxLh3BmOILxBJv2HpRuASFbr5lbB9l8WjdM/PTGIiJ+GWrzuLKXM372qCIiOlQYGCLaJ4IgbGQMuaeSweRuGhER7Zq7oXRHYMgwIQpWWbNHshpVlxsafnFrGYAViJhKhQ7y4R55W2UF9T6uHgeWL7ReaaCq6KgqesvlcvPYIiKiw4Of6kT7RBIFGM2tNdU9lewFjpklIqJXn3sEvbusyDBMqLrhZKH4ZBGaYSJXUZzreEQu/Q5a+2aQ+++7G1e//UJC36QRkaYb+JePZvFwpdRyeaWhOWWHtrVSo+Xv0ebY+aCP2UJERIcNVwdE+0QWrckxqr4xVthOAWe+EBER7ZZ3k4yh/7ixgEcrZciS9S1jZ3WsuwJDksRvoINmP+N2rKa1x1Dvt7ZZWMgdMGoP8jzLVFBpaKg2m0d/9jzX8u//8fki3ru7AgCoKTreu7uChyslJxgEAMmQ1Z8qHvD2+qCJiOglx1Iyon2SaC6gshXF6fkgCdaCjj0/iYhot7yblPHUFCsYIDWzguwAkjvzg02nD55dStYt06fn5tNb1JK5b/75ehWjcT/8Hgm6YeLDJ+uIBz24OpVwrjuXrSLglZzjJFdVoekGnmcrWG0eM69NRFBVdDxaKSPotU4b3MEiIiI6HPjJTrRPUs3AUKbcgF1JZqeAC1yYExHRLm2WMWSzgz+xgAcAA0Mvmv2U26+VOxbU+1QybDqVzH3pZ89z+Ox567/XVR01V7+gDx5lAFij5m0/v7WEsM86PZhOBXFmOApJFPDmVAIfPcsC6JxIRkRErz6WkhHtE79HQsQvYz5Xg6JbCzGz+X+BxWRERLRLm/UYstk9huzSHzcOPzh450ajAICQr3M/tteXQxCETXsVbtd7yCuLqKl6x+XrFQWyJOBYOoRKQ8dKsYHTw2G8ezLtHEuCIOD4gNW0fDQe6O1BExHRS4+BIaJ9dH40ivWygocrZesCs7ljyHU5ERHtkruUzI4FLORrHZcBwKWJGBKuEiJmexy8qVQIP3p70gnouTeHeh1XLwobvYrabdeSWhYFVBUd7ZWIM5kKUiEvzo9FncsGI/6Onx+M+PGjtye7BriIiOjVxk92on10fCCMhmbg1nwBibAX6+UGAJNxISIi2jV31o8JYHa9ij88zjiXGa7I0PnRGM6PxgAArw2FmTH0ktmvHkPdNDQDNUVH0Csj7JOxVKgDAFTdRCrsQ8QV8EmF2WCaiOgoYcYQ0T47OxLFP1wdx1DUB6OZMcSFORER7cXr41awxzRNlBtay79pm6SU8Lvn5dDSY6jHlbgAoSXwZytUVfz2/krH5T5ZxIWxKPweEQ3VQE3VEfBI+MvXBvD3b4w510uFvC3Hh91omoiIjgZ+6hMdAEkUnF1Bg+PqiYhojy6MxXB3sQgT6AgUGJvVGtFLR+q5yVD3zKBPn2eRragdl//D1XHrfkQBN+YKKNZUjMT9kEQBkijh3RMpfPo8h4GIDwDwzXODziRVIiI6OhgYIjog9tqv204fERFRz5pBgvbvlc0yhujlI/eYMtQcfL/ldd6aTmC9oqBc38gk8zd7SzU0w5k6BgDT6RCm0yHn7916CxER0eHHwBDRAbGbTRqm2XNPASIionZ2kMAwW79TmDH06ui5lGyLqWRu7xxPtfzd72o6zubRRETUjj2GiA6IvfhjxhAREfWDHSRo/14ZjPpe0COinXDvDe0mY2i7VUS3XlLxgMf5c5iBISIiasNvBqIDYmcM6UbropCIiGg37CCB6QoMHUuH8NZ04oU9JupN7xlD208f68adJRT0Sltck4iIjiJmDBEdENHVY4hxISIi2itRtIIE7mbB8aAHssTl3ctMcK0Ces8YEmBukzMkbrLIkCXrH0KcOEZERG34zUB0QOzUboNjyYiIqE9M04TqCgxJm0UF6KXU6+u124whAPjexRHkKgpEHiNERNSGgSGiA7Ixlax1t5CIiGg3REGACUDVjZbL6OW2l5doJz2GNjsGwj6Z/YWIiKgr5hoTHRD3Qo3rdiIi6gfTbA8MvcAHQ/vOajjOIRZERNRfDAwRHRBhkz8TERHthiBY/WZYSvZq2csrtJONJWaNERFRrxgYIjogrRlDXLQREVEfmIBmsJTsqBBgDbEgIiLqJwaGiA6Ie63OdTsREe2VKFj9ZhTNFRhixtBLby+bQ1Yp2XbX2fXNExHREcXAENEB4UKNiIj6zTRbp1RJ/LJ56e21lIwJQ0RE1G8MDBEdEHd6Pzd0iYhor8RmjyF3aZHIld2htqOpZFxkEBFRj7h8IDog3MQlIqJ+M83WQAF7DB1uVsYQU4aIiKi/GBgiOiCti3Uu3ImIaG9EwWpEzFKyV8veXiJh+4whHgJERNQjBoaIDgibTxMRUb+1J4+wjOjlt7fm09i+loyIiKhHDAwRHRDBlSXEZTsREe2VIAgdo8slBoYONbuv1HbXISIi6gUDQ0QHRGzJGOKijYiI9s5ozxji18uhxxZDRETUbwwMER0Q9w4e1+1ERLRXoiBAb4sMMVvkcLP6Sm19HR4CRETUKwaGiA4IewwREVG/tU+oYmDo1ZEMeXv+GYFbS0REtA/kF/0AiI4KoSVjiAs7IiLaG1EA9GZgyBpjDsisJXslfO/iMILe3pfh9lLCNM1Ny9K5xiAiol4xMER0QERmDBERUZ/ZZUVvTiUwnQ5xKtkrIh7sPVvIzTQ3X0twjUFERL1iKRnRAWHDaSIi6idREJxSMkEQ4JG4rDvs7KVE+zS6lusc0GMhIqLDgysIogPCTVwiIuo3u/k09x6Ohp2UiXEjioiIesXAENEBcS/muGYjIqK9ElwTqth0+mjYyBja4joH81CIiOgQYWCI6IC0TCXjso2IiPrAsDOGXvDjoIPhNJ/GFqVkPBiIiKhHDAwRHRD3bi4XbUREtFeiIDi9ZpgxdDTYG0tbtBji5hMREfWMgSGiA8KpZERE1G92SRG/V46GHfUr5LFAREQ9YmCI6IC0ZAxx1UZERHvELKGjqz1jyP13HhZERNQrBoaIDogospSMiIj2h8jRl0eCPXGsvceQ+288EoiIqFcMDBG9AFy0ERHRXrWUKL+4h0EHyGk+3ZYxZLgu4Lh6IiLqFQNDRERERK84xgKOBvtlbu893VJKdlAPhoiIDg0GhoheAO7mERHRXrm/S9hv6GhwSsk6xpK5M4YO8AEREdGhwMAQERER0SuOsYCjwX6djY5SMvd1eDQQEVFvGBgiegG4m0dERHvV0mOIXyxHwmYvM6eSERHRXjAwRPQCcM1GRET9xGDA0WBnA7WXkrVPKSMiIuoFA0NELwB3domIaK/cI+rZY+ho2HwqWed1iIiIdoqBIaIDZC/WuGYjIqJ+4vfK0dI5lczVfJpHAxER9YiBIaIDZC/VuJtHRER71dpj6MU9Djo4dpZY51SyDTwWiIioVwwMER0gO9Wfu3lERNRPLFE+WtrDQkZLxhAREVFvGBgiOkh2KRlXbUREtEfuYBC/V44GcZMeQ61TyXgwEBFRbxgYIjpAXKoREdF+YPPpo8GZStaWM9QSGDrIB0RERIcCA0NEB4i7eERE1C/uYBC/XY6GzaeSmR3XISIi2ikGhogOEJtPExHRfmDG0NFgv8odpWTu6/BYICKiHjEwRHSAxOY7jos2IiLaK04lO3rs9UN7KVlHN2oiIqIeMDBEdIDs3gBcvxMREVGv7ACg0REXYmSIiIh2j4EhogMkcCoZERH1ibt8jKVkR4P9OptttWTtgSIiIqJeMDBE9AIIzBkiIqI+Ylzo/2/v7mMsK+86gH9/swsLC7gsbm0pQ4RUqqFYW4KIadKYtoGKTVdNY9Y0FZWkmlCtpomWmlijNvGlipooCRbaakiRYI2kvtLaxH+EyktLeRG7KaUsAVmFUiJ12ZfHP+6Z4bLMws7ew9w5cz6fZLNzzrk7+0zml+ec+73PyzgccY0hwRAAMxAMwRpaWhvAAzwAs5q+l7itjMNKu5IdPnoIAFZrpmCoqn6jqh6pqi92fy6dunZlVe2uqgeq6pLZmwrDt7RQqAd4AGY1PfrUVLJxWGnxabkQALPa3MP3uKq19tHpE1V1bpJdSV6X5NVJPltVr22tHezh/4PBWn6I9/wOwIzKrmSjs9KIoUOSIQBm1EcwtJKdSW5ore1L8mBV7U5yYZJ/e5n+PxiE5cWnJUMAzOj5wZD7yhgs/ZaXwqA7HnoyDzz29PwaBMCG0McaQ++rqrur6rqq2t6dOyPJw1Ov2dOdg1FbeqDz/A7ArHzIMD7Lu5J1x0IhAPrwksFQVX22qu5Z4c/OJFcneU2SNyR5NMkfrLYBVfXeqrq9qm7fu3fvqn8AGBKBEAB9cU8Zn5WmkgHArF5yKllr7W1H842q6s+TfKY7fCTJmVOXF7tzK33/a5JckyQXXHCB2xwbnKd4APqxecE9ZWyWRonZiQyAPs26K9npU4c/luSe7uubk+yqqi1VdXaSc5J8YZb/CzYCn/QB0JfjN/exIgBDsvwcMd9mALDBzLr49O9V1RsyuT99LcnPJUlr7d6qujHJfUkOJLnCjmQwvTaARzoAZiMYGp+lYMhOZAD0aaZgqLX2nhe59pEkH5nl+8NGszzo3/McADPasmnTvJvAGntuKtkLp5O9atsWC5IDcExeru3qgRUYAg5AX4wYGp+FqSnp+w4cet617z/rtJxywnFzaBUAQ+eJAtaQNYYA6ItgaHyqe5A41FqePfj8YOjE44wgA+DYeKKANbQ0xNvaAADMapNdyUZn+le+b//zg6HNmzzWA3Bs3EFgDX3v4raccNxCdpy8Zd5NAQAGZnrE0L4D9nUBoB/WGII1tOPkLfnx8xfn3QwAYKCqJlPS9z69LwuVHDIIGYAZCYYAAGAgKpNNLB576v/yilO2ZP/Blu1bLToNwLETDAEADNS3nbg53/zWgXk3gzW0UJV9+w/myWf25/WL23LeGdvm3SQABk4wBAAwUJeed/q8m8Baq+Spb+1PkpxqpBAAPRAMAQAM1IKdyUan8lwwdMoJgiEAZmdXMgAAGIiFquw/2LJQySlbfMYLwOwEQwAAMBDdjvU5actmI8YA6IVgCAAABmKhS4ZONloIgJ4IhgAAYCCWRgxtMloIgJ4IhgAAYGAEQwD0RTAEAAADsTSVrORCAPREMAQAAAOxPJVMMgRATwRDAAAwEEsjhkwlA6AvgiEAABiIpTjIVvUA9EUwBAAAA7E0g2zBVDIAeiIYAgCAweimkgmGAOiJYAgAAAajJUkWPMUD0BO3FAAAGIhDk1zIVDIAeiMYAgCAgWhdMGRXMgD6IhgCAICBaEtTyYwYAqAngiEAABiI56aSzbcdAGwcgiEAABiI1s0lM5UMgL4IhgAAYGBMJQOgL4IhAAAYiENtabt6wRAA/RAMAQDAQCzvSmbEEAA9EQwBAMBALAVDC57iAeiJWwoAAAzE0lQyI4YA6ItgCAAABqIbMGRXMgB6IxgCAICh6JKhMmIIgJ4IhgAAYCBalwwZMQRAXwRDAAAwEIfsSgZAzwRDAAAwEG15Ktl82wHAxiEYAgCAgTGVDIC+CIYAAGBgTCUDoC+CIQAAGJgFI4YA6IlgCAAABuLNr92Rxe0nzrsZAGwgm+fdAAAA4Ogsbt+axe1b590MADYQI4YAAAAARkowBAAAADBSgiEAAACAkRIMAQAAAIyUYAgAAABgpARDAAAAACMlGAIAAAAYKcEQAAAAwEgJhgAAAABGSjAEAAAAMFKCIQAAAICREgwBAAAAjJRgCAAAAGCkBEMAAAAAIyUYAgAAABgpwRAAAADASAmGAAAAAEZKMAQAAAAwUoIhAAAAgJESDAEAAACMlGAIAAAAYKQEQwAAAAAjJRgCAAAAGKlqrc27Dcuqam+Sh+bdjp7sSPLf824EG4Jaoi9qiT6pJ/qiluiLWqIvaok+rZd6+s7W2itWurCugqGNpKpub61dMO92MHxqib6oJfqknuiLWqIvaom+qCX6NIR6MpUMAAAAYKQEQwAAAAAjJRh6+Vwz7wawYagl+qKW6JN6oi9qib6oJfqilujTuq8nawwBAAAAjJQRQwAAAAAjJRjqWVW9vaoeqKrdVfXBebeH9a2qzqyqz1fVfVV1b1W9vzt/WlXdUlVf6f7e3p2vqvqTrr7urqrz5/sTsN5U1aaququqPtMdn11Vt3U181dVdXx3fkt3vLu7ftY82836U1WnVtVNVfUfVXV/Vf2gvoljUVW/3N3j7qmqT1XVCfomjlZVXVdVj1fVPVPnVt0XVdVl3eu/UlWXzeNnYb6OUEu/393n7q6qv6mqU6euXdnV0gNVdcnUee/3Rm6lWpq69oGqalW1ozseRL8kGOpRVW1K8qdJfjjJuUl+sqrOnW+rWOcOJPlAa+3cJBcluaKrmQ8m+Vxr7Zwkn+uOk0ltndP9eW+Sq9e+yaxz709y/9Tx7ya5qrX2XUmeTHJ5d/7yJE9256/qXgfT/jjJP7bWvifJ92VSV/omVqWqzkjyi0kuaK2dl2RTkl3RN3H0PpHk7YedW1VfVFWnJflwkh9IcmGSDy+FSYzKJ/LCWrolyXmttdcn+c8kVyZJ9zy+K8nrun/zZ92Hb97vkaxcS6mqM5NcnOTrU6cH0S8Jhvp1YZLdrbWvttaeTXJDkp1zbhPrWGvt0dband3XT2fyxuuMTOrmk93LPpnkR7uvdyb5izZxa5JTq+r0NW4261RVLSb5kSQf644ryVuS3NS95PBaWqqxm5K8tXs9pKq2JXlzkmuTpLX2bGvtG9E3cWw2JzmxqjYn2Zrk0eibOEqttX9N8sRhp1fbF12S5JbW2hOttSczCQNe8KaOjW2lWmqt/XNr7UB3eGuSxe7rnUluaK3ta609mGR3Ju/1vN/jSP1SMvlA41eSTC/kPIh+STDUrzOSPDx1vKc7By+pGy7/xiS3JXlla+3R7tJjSV7Zfa3GeDF/lMnN6FB3/O1JvjH1wDNdL8u11F1/qns9JMnZSfYm+Xg3NfFjVXVS9E2sUmvtkSQfzeTT00cz6WvuiL6J2ay2L9JHcTR+Nsk/dF+rJValqnYmeaS19qXDLg2ilgRDsA5U1clJ/jrJL7XWvjl9rU22DrR9IC+qqt6R5PHW2h3zbgsbwuYk5ye5urX2xiT/m+emaiTRN3F0umHxOzMJG1+d5KQYqUGP9EX0oap+LZMlHq6fd1sYnqramuRDSX593m05VoKhfj2S5Myp48XuHBxRVR2XSSh0fWvt093p/1qahtH9/Xh3Xo1xJG9K8s6q+lomw5rfkskaMad20zeS59fLci1117cl+Z+1bDDr2p4ke1prt3XHN2USFOmbWK23JXmwtba3tbY/yacz6a/0TcxitX2RPoojqqqfTvKOJO/ugsZELbE6r8nkA5Avdc/ii0nurKpXZSC1JBjq178nOafbaeP4TBYsu3nObWId69ZNuDbJ/a21P5y6dHOSpZXpL0vyt1Pnf6pb3f6iJE9NDaVmxFprV7bWFltrZ2XS9/xLa+3dST6f5F3dyw6vpaUae1f3ep+4kiRprT2W5OGq+u7u1FuT3Bd9E6v39SQXVdXW7p63VEv6Jmax2r7on5JcXFXbu1FsF3fnGLmqensm0/Df2Vp7ZurSzUl21WSnxLMzWTj4C/F+jxW01r7cWvuO1tpZ3bP4niTnd89Tg+iXNr/0SzharbUDVfW+TH6hm5Jc11q7d87NYn17U5L3JPlyVX2xO/ehJL+T5MaqujzJQ0l+orv290kuzWQBvGeS/MzaNpcB+tUkN1TVbye5K91iwt3ff1lVuzNZPG/XnNrH+vULSa7vHny/mkl/sxB9E6vQWrutqm5Kcmcm0zTuSnJNkr+LvomjUFWfSvJDSXZU1Z5MdvFZ1XNSa+2JqvqtTN7UJ8lvttZWWjiWDewItXRlki1JbunWub+1tfbzrbV7q+rGTILsA0muaK0d7L6P93sjt1IttdauPcLLB9EvlQ9hAAAAAMbJVDIAAACAkRIMAQAAAIyUYAgAAABgpARDAAAAACMlGAIAAAAYKcEQAAAAwEgJhgAAAABGSjAEAAAAMFL/D+sSjY5OEcdQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "#plt.plot(t ,  linewidth=5)\n",
    "plt.plot(angle , alpha = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.save(arr=t, file=Data_folder + Prefix + \"/\" + Prefix + \"_first_turn_angle_zp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(arr=time_by_angle, file=Data_folder + Prefix + \"/\" + Prefix + \"_first_turn_angle_DF\")"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}