ObliqueIntegrationTest_CA1_pyr_cAC_13AUG16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#pip install sciunit quantities efel scipy json2html neurom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#% matplotlib notebook\n",
    "% matplotlib inline\n",
    "from json2html import *\n",
    "from quantities import mV, nA, ms, V, s\n",
    "import IPython\n",
    "import json\n",
    "import collections\n",
    "\n",
    "#import sciunit\n",
    "from hippounit import models\n",
    "from hippounit import tests\n",
    "#from hippounit import capabilities\n",
    "\n",
    "#import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from neuron import h\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\"><tr><th>mean_threshold</th><td>3.4*mV</td></tr><tr><th>threshold_sem</th><td>0.2*mV</td></tr><tr><th>exp_n</th><td>92</td></tr><tr><th>mean_prox_threshold</th><td>4.5*mV</td></tr><tr><th>prox_threshold_sem</th><td>0.5*mV</td></tr><tr><th>prox_n</th><td>33</td></tr><tr><th>mean_dist_threshold</th><td>2.6*mV</td></tr><tr><th>dist_threshold_sem</th><td>0.2*mV</td></tr><tr><th>dist_n</th><td>44</td></tr><tr><th>mean_nonlin_at_th</th><td>142</td></tr><tr><th>nonlin_at_th_sem</th><td>9</td></tr><tr><th>mean_nonlin_suprath</th><td>129.0</td></tr><tr><th>nonlin_suprath_sem</th><td>6.0</td></tr><tr><th>mean_peak_deriv</th><td>2.6*V/s</td></tr><tr><th>peak_deriv_sem</th><td>0.4*V/s</td></tr><tr><th>mean_amp_at_th</th><td>4.828*mV</td></tr><tr><th>amp_at_th_sem</th><td>0.28*mV</td></tr><tr><th>mean_time_to_peak</th><td>13.5*ms</td></tr><tr><th>time_to_peak_sem</th><td>0.2*ms</td></tr><tr><th>mean_async_nonlin</th><td>104.0</td></tr><tr><th>async_nonlin_sem</th><td>8.0</td></tr><tr><th>async_n</th><td>23</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load target data\n",
    "#observation=json.load(open('oblique_target_data.json'))\n",
    "\n",
    "with open('oblique_target_data.json') as f:\n",
    "    observation = json.load(f, object_pairs_hook=collections.OrderedDict)\n",
    "\n",
    "IPython.display.HTML(json2html.convert(json = observation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#To make it possible to calculate with data including quantities\n",
    "for key, value in observation.iteritems():\n",
    "    observation[key] = eval(value)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Calculate experimental standard deviations and add to observation\n",
    "observation[\"threshold_std\"]=float(observation[\"threshold_sem\"]*math.sqrt(observation[\"exp_n\"]))*mV\n",
    "observation[\"prox_threshold_std\"]=float(observation[\"prox_threshold_sem\"]*math.sqrt(observation[\"prox_n\"]))*mV\n",
    "observation[\"dist_threshold_std\"]=float(observation[\"dist_threshold_sem\"]*math.sqrt(observation[\"dist_n\"]))*mV\n",
    "observation[\"nonlin_at_th_std\"]=float(observation[\"nonlin_at_th_sem\"]*math.sqrt(observation[\"exp_n\"]))\n",
    "observation[\"nonlin_suprath_std\"]=float(observation[\"nonlin_suprath_sem\"]*math.sqrt(observation[\"exp_n\"]))\n",
    "observation[\"peak_deriv_std\"]=float(observation[\"peak_deriv_sem\"]*math.sqrt(observation[\"exp_n\"]))*V /s\n",
    "observation[\"amp_at_th_std\"]=float(observation[\"amp_at_th_sem\"]*math.sqrt(observation[\"exp_n\"]))*mV\n",
    "observation[\"time_to_peak_std\"]=float(observation[\"time_to_peak_sem\"]*math.sqrt(observation[\"exp_n\"]))*ms\n",
    "observation[\"async_nonlin_std\"]=float(observation[\"async_nonlin_sem\"]*math.sqrt(observation[\"async_n\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/neurom/io/neurolucida.py:263: UserWarning: This is an experimental reader. There are no guarantees regarding ability to parse Neurolucida .asc files or correctness of output.\n",
      "  warnings.warn(msg)\n",
      "No handlers could be found for logger \"neurom.io.neurolucida\"\n"
     ]
    },
    {
     "data": {
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xpI31cikzM5AF+gqgVClZeM9C6p8VniaN6UY8PZ64FWPmbDP+QT9F64tQa9RE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\nwWAwGNqd/wPXsoA0nI2/nwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57bd5a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load and visualize cell model\n",
    "\n",
    "model = models.Model()\n",
    "\n",
    "# outputs will be saved in folders named like this:\n",
    "model.name=\"CA1_pyr_cAC_13AUG16\"\n",
    "\n",
    "# path to mod files\n",
    "model.modelpath = \"/home/osboxes/bsp_data_repository-master/optimizations/CA1_pyr_cAC_13AUG16/mechanisms/\"\n",
    "\n",
    "# if this doesn't exist mod files are automatically compiled\n",
    "model.libpath = \"x86_64/.libs/libnrnmech.so.0\"\n",
    "\n",
    "# path to hoc file\n",
    "# the model must not display any GUI!!\n",
    "model.hocpath = \"/home/osboxes/bsp_data_repository-master/optimizations/CA1_pyr_cAC_13AUG16/checkpoints/cell.hoc\"\n",
    "\n",
    "# If the hoc file doesn't contain a template, this must be None\n",
    "model.template_name = 'CCell()'\n",
    "\n",
    "# should be None, if there is no Section List in the model for the soma\n",
    "model.SomaSecList_name = 'somatic'\n",
    "\n",
    "# if the soma is not in a section list, add its name here:\n",
    "model.soma = None\n",
    "\n",
    "model.load_mod_files() #This automatically compiles mod files, if they are not yet compiled\n",
    "model.initialise() # Loads the model, creates stand alone cell from template. \n",
    "# uses the soma section list to set the location of somatic stimuli (and measurement)\n",
    "# If the given section list contains more elements, the last one is used\n",
    "\n",
    "'''\n",
    "from PyNeuronToolbox.morphology import shapeplot\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "# use this to visualize hoc morphology\n",
    "plt.figure(figsize=(7,7))\n",
    "shapeax = plt.subplot(111, projection='3d')\n",
    "shapeax.view_init(75,66)\n",
    "shapeplot(h,shapeax)\n",
    "plt.show()\n",
    "'''\n",
    "\n",
    "from neurom import load_neuron\n",
    "from neurom import viewer\n",
    "morph_path = \"/home/osboxes/bsp_data_repository-master/optimizations/CA1_pyr_cAC_13AUG16/morphology/dend-990803_axon-051208AM2.asc\"\n",
    "viewer.draw(load_neuron(morph_path));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dendrites and locations to be tested:  [['CCell[0].apic[29]', 0.07142857142857142], ['CCell[0].apic[29]', 0.6428571428571428], ['CCell[0].apic[68]', 0.05555555555555555], ['CCell[0].apic[68]', 0.5], ['CCell[0].apic[69]', 0.07142857142857142], ['CCell[0].apic[69]', 0.6428571428571428], ['CCell[0].apic[88]', 0.30000000000000004], ['CCell[0].apic[88]', 0.8999999999999999], ['CCell[0].apic[89]', 0.16666666666666666], ['CCell[0].apic[89]', 0.5]]\n"
     ]
    }
   ],
   "source": [
    "# The model tested must contain SectionLists containing the sections of the trunk and oblique dendrites\n",
    "model.ObliqueSecList_name = 'oblique_dendrites'\n",
    "model.TrunkSecList_name = 'trunk'\n",
    "\n",
    "# according to the experiment the distance of the obliques tested from the trunk sould be max. 120 microns\n",
    "# but the cell may not have enough obliques this close that meet all the other criteria: originate from the trunk, have no child, close to soma\n",
    "model.max_dist_from_soma = 160  \n",
    "\n",
    "# Automatically chooses the oblique dendrites and locations to be stimulated during the test based on the section lists\n",
    "model.find_good_obliques()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "base_directory = '/home/osboxes/BBP_project/150904_neuronunit/neuronunit/'\n",
    "\n",
    "# Set the test\n",
    "#The vectors of the somatic and dendritic voltage values are saved in files during the simulation. \n",
    "#If force_run=False, and the files already exist, the test uses the saved files for further calculations \n",
    "#If force_run=True, or the files don't exist, the test runs the whole simulation.\n",
    "show_plot=True\n",
    "test = tests.ObliqueIntegrationTest(observation, force_run_synapse=False, force_run_bin_search=False, base_directory=base_directory, show_plot=show_plot)\n",
    "\n",
    "\n",
    "# Set the number of processes running parallel\n",
    "import multiprocessing\n",
    "test.npool = multiprocessing.cpu_count() - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusting synaptic weights on all the locations ...\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[29]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[29]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[68]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[68]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[69]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[69]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[88]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[88]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[89]\n",
      "No dendritic spike could be generated on at least one of the locations of dendrite CCell[0].apic[89]\n",
      "The figures are saved in the directory:  /home/osboxes/BBP_project/150904_neuronunit/neuronunit/figs/oblique/CA1_pyr_cAC_13AUG16/\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n",
      "/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:67: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:79: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n",
      "/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:91: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
      "/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:113: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/usr/lib/pymodules/python2.7/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labeled objects found. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results are saved in the directory:  /home/osboxes/BBP_project/150904_neuronunit/neuronunit/results/CA1_pyr_cAC_13AUG16/\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57b542090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57b634310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9qHpK9XvnMwkAADhJBy2fuKd6XvX6luh9ecudJ5672v/Sltu1va56Z8tV5JcligEAuB85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\nAABwH/1/S4lTDN/iuvEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff579bc8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uIh7i/FKafyFwPdEjy6PAS8Dn07zdiJcf3Q/ck6adSjyYKkmSJPU51VS3TwF+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ZAAAL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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff579d0d650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GOH9YdXr10BVtnlx9e/Xg6h9Xr58tv7H6qeo7q5Oqf7bKtgAAcNDY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff579ba5c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jiDU1xwPbA0cTySHWy37fDjgL+GB2Phl4tur5ynSkFYDdgQn5V1mSpJZirJYG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\nJEmSJEmSJEmSJEmSJEmSJEmS1MT/BwM8NNBZFEQ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff579779750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rSVLusjy9rjNQ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\nJEmSJEmSVAH/DzXQLL6iPM79AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57bc86c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kKUsmsCVJkiRJkiRJWZpZdwDFGhmJ19j74eF4Pzy89L0k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OLPs8M4Jqb7IiHYWblrHID8taPlSAe07pha+9syxhhjmg+XolKmHsncVsDWFP5S7oiS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\nD6+tHwumhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff579124fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pzj9eXbl6SPW5ppHnq1dnVT9ffXaH++5S3acpJL9hduy86k3VvWfPmfe46sPVB6rbrax6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\nDAhnAAAAGNgN4fzk6j3VtdUl1cNmnvfg6uLFee+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff578bc0bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff578a99090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57886d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57870d0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MIowDAMAgwjgAAAwijAMAwCDCOAAADCKMAwDAIMI4AAAMIowDAMAg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\nKF4ZvZDyF921Aj8TCu/TqXxhlj3j+VZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57850a450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff578eea550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57b343250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3tIT3W6snVO+cnBQAABwpthPcf9kS3C+u3lK9tnrc5KQAAOBIsZ3g/kb18urc6jdX+75j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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff57b548fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the test\n",
    "# Call SciUnit's judge function. \n",
    "#This calls the generate_prediction function of the test to generate prediction (data of model behaviour)\n",
    "#Also calls the compute_score function of the test to calculate the error\n",
    "score = test.judge(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\"><tr><th>model_mean_prox_threshold</th><td>nan mV</td></tr><tr><th>model_mean_async_nonlin</th><td>nan</td></tr><tr><th>model_n</th><td>0</td></tr><tr><th>model_mean_time_to_peak</th><td>nan ms</td></tr><tr><th>model_async_nonlin_std</th><td>nan</td></tr><tr><th>model_mean_amp_at_th</th><td>nan mV</td></tr><tr><th>model_amp_at_th_std</th><td>nan mV</td></tr><tr><th>model_dist_threshold_std</th><td>nan mV</td></tr><tr><th>model_mean_dist_threshold</th><td>nan mV</td></tr><tr><th>model_mean_nonlin_at_th</th><td>nan</td></tr><tr><th>model_mean_nonlin_suprath</th><td>nan</td></tr><tr><th>model_mean_threshold</th><td>nan mV</td></tr><tr><th>model_time_to_peak_std</th><td>nan ms</td></tr><tr><th>model_peak_deriv_std</th><td>nan V/s</td></tr><tr><th>model_threshold_std</th><td>nan mV</td></tr><tr><th>model_nonlin_at_th_std</th><td>nan</td></tr><tr><th>model_nonlin_suprath_std</th><td>nan</td></tr><tr><th>model_mean_peak_deriv</th><td>nan V/s</td></tr><tr><th>model_prox_threshold_std</th><td>nan mV</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize model feature values in a table\n",
    "for key, value in score.prediction.iteritems():\n",
    "\t\tscore.prediction[key] = str(value)\n",
    "               \n",
    "IPython.display.HTML(json2html.convert(json = score.prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Model CA1_pyr_cAC_13AUG16 (Model) achieved score \n",
      " p_value_threshold = nan,\n",
      " p_value_prox_threshold  = nan,\n",
      " p_value_dist_threshold = nan,\n",
      " p_value_peak_dV/dt_at_threshold = nan,\n",
      " p_value_nonlin_at_th = nan,\n",
      " p_value_suprath_nonlin = nan,\n",
      " p_value_amplitude_at_th = nan,\n",
      " p_value_time_to_peak_at = nan,\n",
      " p_value_nonlin_at_th_asynch = nan\n",
      " on test 'Oblique integration test (ObliqueIntegrationTest)'. ===\n"
     ]
    }
   ],
   "source": [
    "#Summarize and print the score achieved by the model on the test using SciUnit's summarize function\n",
    "score.summarize()"
   ]
  }
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