SomaticFeaturesTest_CA1_pyr_cAC_16AUG16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": 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\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",
    "from neuron import h\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\"><tr><th>AP1_amp.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>5.46582296368</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>84.8916666031</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>AP1_amp</td></tr></table></td></tr><tr><th>APlast_amp.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>12.94176933</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>61.2768380869</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>APlast_amp</td></tr></table></td></tr><tr><th>AHP_depth.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>4.91970618493</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>12.1357640551</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>AHP_depth</td></tr></table></td></tr><tr><th>voltage_deflection.Step-2.0</th><td><table border=\"1\"><tr><th>Std</th><td>14.4013027106</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>-45.0851328288</td></tr><tr><th>Stimulus</th><td>Step-2.0</td></tr><tr><th>Type</th><td>voltage_deflection</td></tr></table></td></tr><tr><th>voltage_deflection.Step-1.0</th><td><table border=\"1\"><tr><th>Std</th><td>5.97743835886</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>-22.9514081558</td></tr><tr><th>Stimulus</th><td>Step-1.0</td></tr><tr><th>Type</th><td>voltage_deflection</td></tr></table></td></tr><tr><th>voltage_deflection_begin.Step-2.0</th><td><table border=\"1\"><tr><th>Std</th><td>15.7193976351</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>-50.2771542801</td></tr><tr><th>Stimulus</th><td>Step-2.0</td></tr><tr><th>Type</th><td>voltage_deflection_begin</td></tr></table></td></tr><tr><th>voltage_deflection_begin.Step-1.0</th><td><table border=\"1\"><tr><th>Std</th><td>7.60658440662</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>-26.9601994129</td></tr><tr><th>Stimulus</th><td>Step-1.0</td></tr><tr><th>Type</th><td>voltage_deflection_begin</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>123.499701459</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>83.4799264706</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>time_to_last_spike</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>141.569702022</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>187.077745098</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>time_to_last_spike</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>121.350947588</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>267.77495098</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>time_to_last_spike</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>112.16782043</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>279.147794118</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>time_to_last_spike</td></tr></table></td></tr><tr><th>time_to_last_spike.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>113.050566913</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>296.700294118</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>time_to_last_spike</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>33.1818009502</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>22.920294291</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_time_to_first_spike</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>66.7987755836</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>58.2856477082</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_time_to_first_spike</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>91.7364918604</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>95.7682432487</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_time_to_first_spike</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>120.094726255</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>145.369663428</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_time_to_first_spike</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>143.713839424</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>192.505146651</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_time_to_first_spike</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>22.9945399665</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>12.7755084053</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_first_ISI</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>39.8476297367</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>38.4188926731</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_first_ISI</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>48.0836477317</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>64.3547193671</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_first_ISI</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>49.5349726635</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>88.1586638814</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_first_ISI</td></tr></table></td></tr><tr><th>inv_first_ISI.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>48.2415016491</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>109.788308128</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_first_ISI</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>8.65938397872</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>4.19259087372</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_second_ISI</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>21.466084734</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>18.3120301336</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_second_ISI</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>30.1651087158</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>39.262063079</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_second_ISI</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>32.5073380676</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>58.9584670424</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_second_ISI</td></tr></table></td></tr><tr><th>inv_second_ISI.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>35.0635841451</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>75.5752149078</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_second_ISI</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>5.59320148694</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>2.26780561612</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_third_ISI</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>14.0361888911</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>11.4742926717</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_third_ISI</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>19.4915882727</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>23.9402894951</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_third_ISI</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>21.9318520268</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>37.3347648764</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_third_ISI</td></tr></table></td></tr><tr><th>inv_third_ISI.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>23.0096343861</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>49.7646305833</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_third_ISI</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>4.43826691184</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>1.15366726766</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_fourth_ISI</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>11.8788378935</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>8.01203074215</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_fourth_ISI</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>16.9548351502</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>16.8945859886</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_fourth_ISI</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>17.7187139157</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>28.5911378123</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_fourth_ISI</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>19.0269276537</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>37.1616506865</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_fourth_ISI</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>3.42660372442</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>0.629383867188</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_fifth_ISI</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>10.0140355603</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>5.81877916662</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_fifth_ISI</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>15.5430683467</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>13.3757219884</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_fifth_ISI</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>18.6803014237</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>22.7441425648</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_fifth_ISI</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>19.2396576537</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>31.2306022842</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_fifth_ISI</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.2</th><td><table border=\"1\"><tr><th>Std</th><td>17.231857258</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>5.71590657977</td></tr><tr><th>Stimulus</th><td>Step0.2</td></tr><tr><th>Type</th><td>inv_last_ISI</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.4</th><td><table border=\"1\"><tr><th>Std</th><td>8.49506521896</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>8.60364863426</td></tr><tr><th>Stimulus</th><td>Step0.4</td></tr><tr><th>Type</th><td>inv_last_ISI</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.6</th><td><table border=\"1\"><tr><th>Std</th><td>8.02817180026</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>12.4195455632</td></tr><tr><th>Stimulus</th><td>Step0.6</td></tr><tr><th>Type</th><td>inv_last_ISI</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.8</th><td><table border=\"1\"><tr><th>Std</th><td>7.63479359668</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>16.4950967344</td></tr><tr><th>Stimulus</th><td>Step0.8</td></tr><tr><th>Type</th><td>inv_last_ISI</td></tr></table></td></tr><tr><th>inv_last_ISI.Step1.0</th><td><table border=\"1\"><tr><th>Std</th><td>9.42799391629</td></tr><tr><th>Weight</th><td>1</td></tr><tr><th>Mean</th><td>20.5677718854</td></tr><tr><th>Stimulus</th><td>Step1.0</td></tr><tr><th>Type</th><td>inv_last_ISI</td></tr></table></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load target data\n",
    "\n",
    "with open('./stimfeat/PC_newfeat_No14112401_15012303-m990803_stimfeat.json') as f:\n",
    "    config = json.load(f, object_pairs_hook=collections.OrderedDict)\n",
    "    \n",
    "observation = config['features']\n",
    "IPython.display.HTML(json2html.convert(json = observation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\"><tr><th>Step-2.0</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>-2.0</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr><tr><th>Step-1.0</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>-1.0</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr><tr><th>Step0.2</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>0.2</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr><tr><th>Step0.4</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>0.4</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr><tr><th>Step0.6</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>0.6</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr><tr><th>Step0.8</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>0.8</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr><tr><th>Step1.0</th><td><table border=\"1\"><tr><th>Delay</th><td>1000</td></tr><tr><th>RecLocationX</th><td>0.5</td></tr><tr><th>TTX</th><td>0.0</td></tr><tr><th>StimLocationX</th><td>0.5</td></tr><tr><th>Threshold</th><td>-20.0</td></tr><tr><th>Amplitude</th><td>1.0</td></tr><tr><th>Duration</th><td>400.0</td></tr><tr><th>HypAmp</th><td>0.0</td></tr><tr><th>Type</th><td>SquarePulse</td></tr><tr><th>StimSectionName</th><td>soma[0]</td></tr><tr><th>RecSectionName</th><td>soma[0]</td></tr></table></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stimuli\n",
    "IPython.display.HTML(json2html.convert(json = config['stimuli']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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": {
      "image/png": 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H6uDpCUuSJNrPt6MOUqNUK4lbFceyB5ahVCmJXR5L9h3ZNJ1soujZIuJXxw/n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\nBMEk/B+hrEh2ptLe8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0314a5ea50>"
      ]
     },
     "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_16AUG16\"\n",
    "\n",
    "# path to mod files\n",
    "model.modelpath = \"/home/osboxes/bsp_data_repository-master/optimizations/CA1_pyr_cAC_16AUG16/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_16AUG16/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 = 'soma'\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",
    "# use this to visualize hoc morphology\n",
    "'''\n",
    "from PyNeuronToolbox.morphology import shapeplot\n",
    "from mpl_toolkits.mplot3d import Axes3D\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_16AUG16/morphology/051208AM2.asc\"\n",
    "viewer.draw(load_neuron(morph_path));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#all the outputs will be saved here\n",
    "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.SomaticFeaturesTest(observation, force_run=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\n",
    "\n",
    "# set the target data to be used in the test\n",
    "test.config=config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running stimulus: Step-2.0 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "running stimulus: Step-1.0 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "running stimulus: Step0.2 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "running stimulus: Step0.4 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "running stimulus: Step0.6 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "running stimulus: Step0.8 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "running stimulus: Step1.0 on model: CA1_pyr_cAC_16AUG16 at: CCell[0].soma[0](0.5)\n",
      "The figures are saved in the directory:  /home/osboxes/BBP_project/150904_neuronunit/neuronunit/figs/soma/CA1_pyr_cAC_16AUG16/\n",
      "Results are saved in the directory:  /home/osboxes/BBP_project/150904_neuronunit/neuronunit/results/CA1_pyr_cAC_16AUG16/\n"
     ]
    },
    {
     "data": {
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Jzj9EWq3n/9FHH7Fnzx727NnDVVddxfz58yXwt0HeYlswo32Cj7LBL+/gb7RPY9t8n/B1\nX3ZC1vZpe9psz18rVVVV/OEPf6CyshJFURg9ejRXX30177//Pvn5+UyaNInbb7+dXbt2kZ+fj8Ph\nYMCAAcyfP7/Jev5VVVVN1vP/6KOP6NatG7m5ubz33nuA2tt//vnnefHFF7V8y6KVAk3yCiX4+87B\nh9DzD2KSV3BDPcNzwtf9ICLBP3ok+IdowIABDSd23b399ttNHj/xxBNeXz9kyBBeeeWVJtt0Op3X\neQIDBw70Gvg9Xy9iXCsneQUTHEMb7eOsx0d97qX8B+PWnvB1r0uCf7RJ2ifK5CRX+9aiE75BpH1C\n2V8oS0D4+qbhKVzj/EX0RKvn/y9gILAWuC1KdcYcWc+/ffI22qcle1FfH4ahnmHoXIcy1NOTr2Wo\nRXRFo+c/AEgCRgImYFAU6hQiZgTO+Tct4G9tn0Bc+/UbjD0Cdstn+AbXLN/fNJp/u5C0T/REI/if\nD7gWovkCGBqFOoWIGd5m+LoLbm0f76t6tmRJ54YDhK/63Pbjex9N29K6VI4Efy1EI/inA65xkOXO\nx0K0HwGXdwiQEw/zDF/PVT39jfYJdMI3mKAf7KUnA9cnwikaOf9yINV5Pw0o8ywwc+bMhvsFBQUU\naDiBS4hoaBrkdPjraQcTEEM64duK9fybLu9AGEf7eC8rGhUXF1McppUNIDrBfyUwDXgXGA00G6fo\nHvyFaA9aOsM3nJdxDGaSl3/hOeErAT84nh3jBx98sFX7i0baZx1QBywDbMDqKNQpRMwIlPP3V75x\nm/9/1dAWdgu+TGSXdA5+boIIv2iN878NdbTPn6JUX0RpdTGXTz/9lD59+tC7d2+mT5/e4vpEdAWa\nQduaGb6+9hvKxVx8lfJfj9+n/ZIlnWODzPANkfvFXPR6Pbt37264mMuNN97Yon26X8xlwYIFzJo1\nizlz5jQpU1JSwp///GeKi4vp0KEDJ06cCMfbEVEQjtE+YV3eIWwnfAl0BAluH5Lz14TM8A2RVhdz\nefPNN7n22mvp0KEDAFlZWdF4uyIsmgd/h8Phu7TX4O9fKMs7OBxqGX0rx/m7Lzvh+n8IZR9uJUIo\nK8Klzfb8i3XFYdlPgVIQUnmtLuayc+dOHA4HI0aMwG6383//938yKqoNCynt47bN10GjNeP8Qwnc\n4biYi2d9MslLG202+IcatMNFq4u5uJaP/vLLL9m7dy9jxoxh165dfv9xRWzQe0n7uAdxz1+999+p\n90leLo37U/zsw1Ui+Elevg82zjI6f232bFvTst6+rfj7RiTCq80Gfy1pcTGXzp07c+aZZ2I0GhuW\nfj58+HDDNwYRu9yDqyu2tfQavoGDv8p/b95Zj4/6nKX81hee0T5N6/JXnwg/6TaGSKuLuVx22WV8\n8803gHre4dixY3Ts2DGi71WEh/swzWBW9WwWuB2+0z6u/XhuD2aGr95vvt5/8PfsoIeSOpLgHxuk\n5x8irS7m0qdPH/r370+vXr0wGo0899xzxMXFaflRiCB5G+3jHqw9A6n3tftDu4av/2DscNuj97K+\nDiqNzzdplv8rhwX4VuL+niTtEz0S/EOk5cVc7r//fu6///6WNFtoKFDw99XZbQiKbkM9fQXHxu2O\nZnU2KxvUCd/AwV9RHEGN9gktJSU9/2iRtE+UyYUs2h/3YOct7eOrs+sKmoojlJx/MD3/piOCWr62\nT3AnfAPP8HX/FiQ9/2iRnn8UycVc2ie9W87fFduCCXINQdPhKxXUvGxDSsdfzz+I0T6u/fj+puEs\no/e9j8aygXr+kvPXgvT8hYgwbz3/YNI+3nr+gU/4Bu75e87w9V7W0WT/zXbh7Pnr9KFPFGue83f4\nLCsiR4K/EBEWF9c8+LsLlPYJLecfOPg7nMHW3wxfV0D2H/wbe/6tG+0jwV8LEvyFiDBdiD3/Zuvq\nhBD8g0n7BDfD1/8JX1fax9XzD+WEr2fO373nLzn/6JHgL0SEeRvt42+Sl2cqR3EoQef8gxnt05rg\n35LlHQLn/CXga0GCvxARptc3zscIZoZvY1nX2WEIdrRPMOP8HUHM8A0u7dOy0T6+cv4yEi66JPi3\ngBbr+VssFn7zm9/Qt29f+vTpwzvvvNOatyCiSK9vWc+/4WdIJ3yd+fxW5vyDOeEbbNrHV84/lG8r\nIvwk+IfIfT3/DRs2sGDBgob1/FvKfT3/W265hVmzZjUrs2jRIgwGAxs2bKCoqIg77rijNW9DRJFe\n19jzD2aGr4vdbncWaFwiIhxDPQnTUE/3E76hpH08t0vPXxsS/EOk1Xr+ubm5VFVV4XA4KC8vJzs7\nO0rvWLRWS3v+3oZ6empJzz9cq3q69/z98X3eIPhZySL8ZJJXiLRaz3/kyJE8//zz5OXlUVNTw3//\n+9/ovWnRKoFm+AactevK0esCHygaevXBnPANIu3jf20fR4uWdG52cFN8fPUREdVmg39xcXh6CQUF\noY0r1mo9/4ULF5KQkMCRI0fYtm0bV155JT/++KOs598GBJrk5RlffeX8vQX/xn2EsKqnq11+yvq6\njGNj4Fbvt2SSV/M2S89fC202+IcatMNJi/X8V6xYwbhx4wDo0aMHqampHDp0iNNOOy0M70hEUuCe\nv/fXOezNR/t4ahqkdQTT83dV2JIZvk2Hejb2/EMJ/s0ObpLz14R0G0Ok1Xr+3bt356uvvgLg0KFD\nHDx4UNbzbyPc1/Zx8fdNz/s4fz3+YqNatrGeoNb2CWKGr6+2uXL+wZzw9bUP6flrq832/LWi1Xr+\n06ZNY/LkyeTn56MoCs888wwGg/z62oJQe/7NUi7Onr+32OgeSHU6XcO+Qpnk5V5f89m3/nr+vtM+\n/s5puB7b7U2/XUjwjy6JHiHSaj3/xMREGdvfRnkb7RPKwm7uJ3w9NT1QBNfzb2iXl/oaLxDkf5y/\n2rSWzfBtvl16/lqQtE+UyR94OxRgeQdPoQz1dFF7/sEFf8Uj7ePtql2+xvl7pn2CGeopOf/YJD3/\nKJL1/NunQOP8fVFc6zA4J3kFSvsEOkB4vsZb2setlN/XhrKqp/82O/eDBP9ok56/EBGm9zOU0pvG\nnLg6w9d9qKcvnid8/S+3oP5strCyl56//6GejhYN9fQ1zl+Cf3RJ8BciwvRe1vMP5iSn51DPQDl/\n930Fk/bR+0n7eO7f+2OHny8bjeWCzfmL6JLgL0SE6f3k/P1NsHLY/Z90bVI2lEleHj1/XxO6vG1z\n7/mDzucMX39X52qsz96krPT8oyucwX8ysBUoAh5zbjMAC4CvgXvDWJcQbUagGb6emgV05yJqgdM+\nwQkm7RPsPlyaB267n9e60lo2V80+9iEiKZzBXwH+AVxIY6C/AtgMjACGA7lhrE+INsE9pLUk7aPm\n/JUghnq61RnE8g6ePf9Q0j4NRX0M9Qym5+9wSM9fS+FO+9wGfAVc5Hx8PrDEeb8IGBzm+jQR7vX8\n16xZw6BBgzCZTF6XinCvt3v37vTv35/Nmze3uD6hHc+efzBpH1zr6ITQ8w9meQfPnL97kHa93v+V\nvPydYwh8aUaHQ3r+Wgpn8P8Q6AP8CnjCue90oML5fLnzcZsWifX8O3XqxMsvv8yECRN8lvnhhx8o\nKipix44dPP3009x1110trk9El+Jl2KS/pRVsNjUoNh3n7z3v7xoR5HA4gho+6qzd5z4a2hxgMTZF\nUdvu6/KS/lbqbKyvac8/+PaLcGjJOP9c4C2PbUcAV+Q6AWx3lisH0pzbU4Gd3nY4c+bMhvsFBQUU\nFBS0oFnR4W09/0mTJjWs53/dddfxpz/9idtuu41Vq1Zhs9l45JFHuOSSS5g5cyZ79+5ly5YtlJeX\nM3fuXEaOHEleXh55eXl+h+f973//Y+LEiQCMGDGCSZMmYbPZZImHNsY1gdY1k9Zbr9hqtarPuXr+\nzjH1di9pdNfy3577cR1AvLehafBvqM9LW4xGo4+yTcs1r6+xgO99NO35+2uzgOLiYoqLi8O2v5ZE\njqOoeX1PKUAlYAa6A8eBlcBo4Hvna97wtkP34B/rIrGefzAOHz5M7969Gx7n5uZy7Ngx8vLywv8m\nRVjZncEOGoO/66Bt9xLRG3r+Hmkfb8HfFUjVXnNjz9nbfl0Mzja4SvsL/o3LPajcrzXh3lP3DNyu\nkTxAsw6Kq76GeQyKBP9geHaMH3zwwVbtL5zdxtuBsajpnkcAG/ARahroa+Bj1ANHWIQrPxjqV81w\nr+d/7NgxcnJywvJeRGyyWxuDmuvLnXtQ9YivjcHY0pj2URRrwJ6/w1HfsN1fINXTtMKmBxBXOxMA\n34Hb4QCdztDwGs/6HI7ahvue+3C12WarcZUO2GYRfuEM/g85b+5swHVhrKOBlvnBcK/n787XQc21\n5r/L0aNH5aDRRtgtjT1/vZflEAyGxnX4gYblwW21zmDoUIOpw9E8kFZXV6tFHA7Ajk6nPl9TU4Mn\nV+A26JqmYaqqqtz2oYqLSwTUK8t5K+ta1dNR5/Ban91e3XBy23Mfrjbb7dXOLY0zmj2/aYjIkUle\nIQr3ev7uAVxRFJ8Hh0svvZQ333wTRVFYtmwZPXv2lHx/G2G1NqZKnLGzidTUpo9dV3ezVarBX3Eo\n2GzquImsrCyvZV2BOy5O3dnx48eb1eMqq7M3vVjQkSNHGvbhCsh6fTxAs2tFN155DsCBo8bhtT6r\ntQTXv4TnxYlc+7BYSp37ajzoyLWpo0eiR4gisZ7/tm3bKCwspLS0lE8++YR+/fqxePFiVq9ezdy5\nc3nxxRfp168fo0aNonv37iQnJ/PGG15Pn4gY5HBL+5SUOLc5HA098cxMBaOxcQqMq7NQe7CWeOJR\nFAelpeq3vtNPP73JvtevXw+A3a6mWRISzkRRFDZu3NisHd+tcH1DVQO7t85JVZX6uuPH1aOU5wWD\nXPW51vap31+PgsKGDRualKuu3sDOnY3v1ds+qqu3ofZfHA2dpzPOOKNZu0VkSPAPUSTW8+/Rowf7\n9+9vVnbQoEEMGjSo4fHDDz/Mww8/3JJmCw3Z3E74ujrINput4e8oKSkBozEDUFM+7733HpmZmZR8\nWkIeeVQmfMrSpYmkpdU1+bZns9lYsGABHTp0oKTkSzp2TEOvj2fp0qWkpqY2+xb5/FOPkJbqkXo8\nfpxly5ZhNptxOBwcOvRvDIZ03n9/E1lZWU3SkDabjfnz55ORYUSvN2Ert1B/oJ4NiRswGo0kJycD\n6iieQ4fm8tlnJjIzTU2C/4kTJ/j0009JS0vj2LH/0LVrJoriYP78+aSlpWGMa5qSEpEjaZ8ok4ks\n7Y/d7UTmvn3qz/r6et5552Hy883Ex/doSPlNmzaNS4ddSlelKzUHakiffojVNXczf34cN9yQ1CSQ\nzpgxg65duzJ0aBeOHn2Xbt1mU1ZWz9SpU3n88ccbyjosDh7//US27N7DrwYNw+5cKtpus8Ejj3Dr\nrbeSk5PD3r1PUVHxLRUV1zFv3jpmzpzZZNTQfffdS05OLYMGpZBQN5zD8w6SdHMSj9c/zhNPPIGi\nKDgc9Wzdej3FxTo2bzYxZUpBk3kEkyZNYsqU68nKspGcPIi0tOFs376XB2Y8wI1xN1L+fXmkfx3C\nSYJ/FD3wwAPcfPPNWjdDRJn7aJ/aWkhIiKesbDtLlhRx1aXTqSu3YSuv56GRD/H9e98zed1kEjsk\nYv7rdxwd/QdmPZDC/ff/jXPP1VFfr47o+eijj3j99QX89a9xKMp+8vLuJyGhJzNm7ORXv/oVlw28\njLrqOjZc/S2vTS/g/15/m9efeoOc0zpjcR6MlsyeDXFx3HffLeh0Jzh69FNOP/1Dfv/7t7j//uH0\n7du3ob7XX32WNxY+y1+mZGNYPYDy9bWkjUnj/p33MyJhBBMmTKCuro41a85n584SHntsH089NYYO\nHVIa9jFr1izKyg7xy19+THx8AkkH/sCJtZVce+ksplqn0vecvuhM0jmKFgn+QkSYzWZt8vjspM5s\nW/8ZB3bG0emFHlTuq2L3rmqeXvc07374LoM3d0XfcSsnTq5g3v3D6dlxIL/73c2YTDZqa2s5dOgQ\nN944mRkzHHTq1I28vPFYqs08ceerVJVaGP/BeLZevJU6Sx0npvyaWUs28M9/Pceoa35NQoKZequV\nzz//nPVbvr0yAAAbDUlEQVSLFjHorxezbt1AzOYkzjzzVaZPn0FhYV/GXtwV+3Y75bvL+XDKbUy/\n7Vb+/vNf0ePgO+SOPA3zBam8/vVaKqoquCX5Zo4ceRqwk5k5jb/85RCPPPIIffrkER+vp7a2li+/\nXMKcZ5/g7lv3Y3rr98RtzeOn5/bxxLztDB7Wg7+X/J2cSWk4DHVa/IraJQn+QkSY3Wqnd2+45Rb1\ncbfeBlZvsjK8cBQjVoxAydUz/z8V3DPjHvIGbWPduvNJSelMVdUEluxYzkPjH0KvNzUE/7/9bTKj\nR9dySZ9HMb17J1Wf1LD6ntU89/pb/PWubPp90I/TV60kzqDwzeqL6HnuMG64Qa080ZxMndXK3Xff\nzU139+S29Ffpds6bpKWdxZJ3vuS7ou+4IfVCjr67j8OzD1Ct/4mXj7zG1BtvZMorb3Dm384i7Yw0\njh+v54UXVvDic8/Affdy4sSHJCQksmhRLXl5edx0000o1Ubs+6s5uuwA02+6gt9dmsc5q/7LGaOv\nJ2dkDqVTSlm1+SSPPHMdFscBdtuuxJ7rdREAEQFywleICLPZbQwdCvn56uOeQ0tZ9JWdCy+8kISE\nBEpLyzh8uJ4XX+zHjh2/JT9/KZmZjzN//gKu6HUFKboUdDojRqONipOlvP/eF8w7458cntyDzLEW\nsi/IZvGGxYz82VC69NxKXd5SDu2ci9mczJtvfstTTz3V0BZzYjKbd1eB4RDDBynMXjWXvgtzsKyz\nMHf1XK7Jv4bsEXnUdk3EMXIFVb+2smyFnpdf+3vjPsxm3n9/IwMHdkRnfgaOZ9Kv3yeYzR154dkX\nuLPfnazqvYrakWVQU8dW27fU1MUxY97mhnH85pfNvPTSS1x9dU/i461s2vQrkg1DUCxNRw2JyJGe\nvxARVm+1otfDWcq5/OPOPqSkqEN+CgoKMJvNnDxZSrduUFHxFGed9QjJyX1JSEhg+/btFJ5biKPW\nQeWaGkwmBycrysnLzKXwP1PJXdeXYw/mcPiMeLZu2wpDB1NuraJo6994x3QHlZVVbN26lTuSkzn7\n22/JXb6cBWWV7DtUT59B1VTNvJXrn0/G8DMzWYOz+Mn2Eze8dgOZI/Ow68soLV3A8eP19OrVq8l8\nlISEBHbsOMLw4UmUlH4IT92KYtFRXVrNtj3bGPCzC9A/0xXL9NOoG3CIvQdq6Dz6Um7dtYvrt2xh\n3MaNfFdfz7Zt27ANyGLRroVsqqpgwclROPwsCCfCK2Z7/hkZGTIyJgwyMjK0bkK7Z7HZ0K/vj+m1\nvzPwP/dy3Dn5adCgQQ1LHZx3HpSVFdHtnDfZV1fHvrIyAHR55/L1/pN0GLef+FfV1x0ZfgE5B1fT\n8biJvPh4Kp376Du6kISahzHqFIafdiX/Yjpn9+rFwj59SI6LIzkuji++X8v1wOje6VxgGc3IBw+z\ncmBH9r2iDkM699xzKS8/QXn5MrKzRwLLuOyyy5q8H4dzuGmfPps4HD+edCWZB29fRa29Fl3HToy9\nvIxOphour7TQUaf25HuPuYSeiYmkGQykGgzsranhODDpgj7Yjs0mtcvDjM1J5AVZ2TNqYjb4nzx5\nUusmCBEWNpsNhTSoM2PNLOT0jp14cv/L3PPTTxy2WMi5+GJGjVrMJkd3CleuJ9dkImHECLoqCkcM\n0GtJLUpnEyYT5PeL54npd3PR+ec3rMe/ymbjrrVreWjQMJYtg7ysyxidk8eUKVMoLCykX0pKQ1uG\n9h9Mbi70OXYhXR/tSvyWY1gUhT/+8Y/s2rULnU6HXp8EQIcuV9I9v5zKIUO4eds2NlVXs62mhuqs\nLLLzzyIzczdrEwrJSNBT8L6N7Zf9ltFXjuSmYcMAOHx4A+tKoEuXjsydMIHExMSGdpRNncrK3r05\nLTGdn4BzOv2aDYlLaVzqofmaRyK8YqFrrcg63uJUdlnBz+ho7sTwY3/hyJ0l/LNTJ8anptI9O5u8\n+Hg6mkzE7ygkN+93nN35xibX/D087zDbbtxG93935/CgK+jU6QY6d/6Dz7qWLUumZ88FZGdf6fX5\nmj3VrNqbTMZ775P/7C85d9Uq3uvVi56JiWyuqeGTkhJWlx/hdyXnc1PcB3ROPp1eSUn0Skqid1IS\nPcxmck0m6usP8u23XRgxooqvDd8DMMo+Cp2+se1VVT+wenU/Ro2yo9N5zzCfPPk5P/74K0aOrOKN\n919lxu03c8PDX/G3f+xl8nnX8PRTetyOXcKNMzPS4hguwV+ICBs78my6xKdjuf5PvNW5M/euWMFD\n//43VFbCOefAuedCXh6kp0NGhvrTbAaTifKdJtZN1zHsiyQMWUZ0cQbQ6bzf9HoUQKfXe38eUOwK\nX525i74L8sgclUi//ft5KTubWaWlrK2vZ1xSEsMTEjg/IYEzDAb/qVfnc6vH7iOpZzw9/9Wyq7Sq\nVynT8d9vPuGPd/6Bw1PS6Jx0OrUVSSR+toAPXj294WS5aCTBX4gYd/EFp3N2cg4Zz7/NI/v2ceyC\nC8g2maCsDLZsgc2b4ehRKC1Vt5WWQn091Nej1FuwVCUQrzuhblMX1fF98/W8G6sjEYOuBp0OLvzz\nn0mrraXcbGbx449j9HMdgCbc1/J3xKPXWdHrWneytihLz3VHDnF0/ED2z1zJwl2zmfXl4yifPcXs\n305k6tSG441Agr8QMa/w/M70yDmL29/+nB+rqxkfQytX/nrTJt45fpxN553HuUlJmrblh49e4eIp\nN1E1/GaqP/w3AGsPr+WqNydyYns3BlXN5Lm/DqJnT02bGTNaG/xj9oSvEKcKu92OwWSiW2Ii3dxO\nesaC33fuzJiMDM0DP0BahxysdoVspXHl0gGdBrD51vXM+fZFHlw6nr7PduKMyon85rxCfntpL84+\nSy/fBlpIgr8QEeZQFAzG2FytclR6OqPS07VuBgDm087GZlPoaGh6zYIEQwJ3DP8jt17wOz7d+iVP\nL32XJ4/M4e8vlRB3shc5cT3obD6LLh2y6Zyu3nIykshKN5OWaCbVbCbFnECS2Yg5QU+8SY8hTo9e\np0Ovb79HDgn+p7iTJ06y8Pnn2fLDck6WHqCmrorq+hqq6+qoqq2nzmLHanNgsTmwWBUsVqXh4twN\n2TilyY+gBPyXCnwescX79vt6Df7X6+ogv3tC4ILtnDm9I1Yb9PC8lrDVClu3Yti9m4t++omcqjPY\nq7+BI8cSqC4xQ2U8xhozxrp4DNY4jJZydJZKKix6qhw6DuEgLqmSuJQy9ZZUiT6xCn1yFSRWYImv\noFpfTpWujGqlikqLhap6C5W1Viprrdiscby5qNJ7o9swCf6niG0/buPdV17kp51rKSk7yLHyExw8\nUcnh41Yy0+M4LSuJnPQMkhJSyEzLJSkxneTUHFLSMklOSyclNYPM7GwyO2STnNZ4aak459W+9Xr1\nT8VgDDz42mb1f9LQ4fB9rVa7zf9rA13n1ebw/XpHgH1Hym3v/Atl0DWa1N2WxMebsdngXPsRWLoU\nvvoKli1jw4kTvH3pL9iSOIQuG/sw9HvocEyHqasD+5lWDD87jKnLPkxZx7DHH+JE7QHKqks4WVFK\naXkFJ0trqawwUlVporIqjqr9UFnloLLSSkWlBdCRmmImNSWZ1NRUUlOzSU1JJSUljfTMdDIzsgK2\nvS2S4N+GWOotfPnJYr767EMOHdhMSfkRjpad5ODxKkrK7HTKNtI5K4WctCx6np7Pz0cP4MpJU+iV\n30vrprdrpZ+85PegJFRGoxGbDWrSUmHmTFb84hc89LcZJKwyceOrOsboDXS6wYHp7vVUJi5n48bv\nWL9+L7t2JXHg+zgOHLBQUlJLbm4GOTlZ5Ob2IDc3j06dutK3bw4ZGRlkZmaSkZHR5L7ZbNb6rWtC\ngn+MOLDvANs2bmL3tq3s3raRkuP7qK4qobK2nPKaSo6WVXDoWB2KAp1zE+iYnkp2Wjbn9TyH6665\ngAlTbyKnU8fAFYmo0xGH3c+3HaHS6/UYDFAdn0DB009zqKSWf/7TRNbBWjo89z1HEt5kwedb+P6B\ndL777iS5uTkMHnwNF144hJ49e3L22WfTpUsXubZ1kGLiU7LUWzDFmwIX1Jil3sK+n/axbeMmDuzZ\nw8kThykvPU5tdTn1ddVYLLVYrLVYbfXOm5V6mwWrzUK91Ua9zUa9xUq9xU6d1UFtvZ26eoU65xLm\nCQmQmKAnM81ERrKZNHMySeYUzurUkQsGncOoseO56NKL28RnJRrpMWBXpOcfjKRkPd33HWS4LYeO\ntx0gfuIX7Oj2FLNfMLFkSQkjR17EhAm/5LXXxja7vrAITUwE/7XfrWHIyKGatqGirIK3573Cxu+L\nOH7iJ8qqS6mqraa8po7yqnrKKu1U1yjo9WBO0JGYoMccr8ccbyDeZCDeaMBkMLrdTJjjE0lNysBk\nTMBoMmOKTyIxMZWk5HRSM7PIyu5E3uld6dGnF6d1PU3T9y8iR6+Lk+AfpPhUUPQ76TDuRyyzn+af\nH3zL0tl2pk+/maefvoVOnTpp3cRTRkwE/y3r10Ut+K9e8T2fvPM6+/b8QEnZIY6Vl3D4ZCWHjllI\nTdZzWnYSuenppCdn0jn7LFLScsk7rRvnDjiP/MEDJUiLkOmJwyZpn6A4EuHo+Ssx/moZs/5cRUHB\nFWzf/iQdOnTQummnnJgI/gf37m7V62uqa9ixZScHftrD4f372L9nOyeO7qWq8jhVteWUV5dxtKyc\nwydqqa5R6JRjIq9DCtmpHeh5el8uKejL5RMn0X/wgDC9IyEa6THgkJ5/UEqzFV6aV0ldnZ05c17i\n6quv1rpJp6yYCP7v/u95ln61ELvDjt3hUG92BzbnT7tDwWp3YLcr6n2bA5tdod6iUF+vUG8Bkwni\nTToS4nWkJhlJT4onNTGRZHMynTJPI7/nKPoPG8Mvr/0NiUmxNctSnNok7RMcq92K/Xwd52XdytNP\n38hpp8m37EiKieCff1Zv9HFxxOmNxMUZ0McZMcSZMBhMxBmNGE0JGI3xmOLNmBISMCcmY05MIjev\nC13OOoMevc6RgC5ilqR9glNrq8VgTmTixAeQuB95MRH853/6rdZNECJiJO0TnFprLXqHmXY67D7q\n5Bq+QkSYmvaRnn8gVocVHEYSZCWMqGhp8P85sAX42m2bAVjg3Hav2/Z/AcuAJ1tYlxBtmh7J+QfD\n7rCjU+IwyTSWqGhp8F8JeF5b5wpgMzACGA7kAgOAJGAkYAIGtbA+IdqsOJ0Ru2LVuhkxz+awgcOA\nTNCNjpYG/zLA4rHtfGCJ834RMNi5bbFz2xeAtjO5hNCAkQQsjjqtmxHz7IodHHHE6OrXp5xw5vzT\ngQrn/XLn43Sg0mObEO2KgQSsigT/QKTnH12BPuZc4C2PbUeACV7KlgNpzvupwE4gxXkf53Nl3iqZ\nOXNmw/2CggIKCgoCNEuItsOoM1Or1GrdjJhnd9hBkZ6/L8XFxRQXF4dtf4GC/1HgwiD3tRIYDXzv\nfM0bwAFgGvCu87lXvL3QPfgLcaoxYsYqwT8gu2JHkbSPT54d4wcffLBV+2tp2mcgan6/N2pO3wR8\n5Hz8NbAC9cCxDqhDHe1jA1a3qrVCtEFGnRmLBP+AbA4b2CXtEy0t/ZjXAGO8bL/Oy7bbWliHEKcE\no05y/sGwO6TnH00yyUuICDPqJO0TDDnhG10S/IWIMCNmrEjwD8Su2FHs0vOPFjnGChFhJn0CtgBp\nH0VROFx1mF0nd3Gi5gQV9RVU1FdgdVixO+w4FAd2Rf3pUBzNXq9D1/SxThdymdY+39p97Dy5U9I+\nUSTBX4gIM+l89/y/+ukr5q6Zy5LdS9Cho1tmN3KTc0kxpZBiSiHeEI9ep0ev0xOni0Ov06PT6ZoF\nUQWl6WOl6eNIlvFWriVlEgwJ6NfdImmfKJGPWYgIi9elUNcw/1FVY63hhkU3sPrQau4YegePFT5G\nl7QuGrUwdjx7OdLzjxIJ/kJEWKI+gzqlFEVR0Ol02B12xr81npykHDb+biNmo6xhDKAoYLUiPf8o\nkRO+QkRYvN6MjjhqrDUA/HPlP7E6rLw2/jUJ/G7sdtDr1ZuIPDnGChFhBgOYLZmcrD2JQ3Hw6PJH\n+e6m74jTx2ndtJhis0nKJ5ok+AsRYUYjxNepwf+DrR9w0ZkX0S2zm9bNijmS8oku+aiFiDCDARId\nueyv2M+T3z7J6798XesmxSTp+UeXZNeEiDCDAdJtPXnkm0fIScphaBe5rIU30vOPLgn+QkSYwQBZ\ndUNYsX8F9w2/T+vmxCy7HeLkNEjUyHFWiAgzGuGMqt+w6u7uDMqTK5n64nBI8I8mCf5CRJjBAHab\nnvM6n6d1U2KawwFeVowQESJpHyEizGBQT2YK/xRFxvhHk3zUQkSYwaCezBT+ORwS/KNJPmohIsxo\nlJ5/MCTtE10S/IWIMEn7BEd6/tElH7UQESZpn+BIzj+65KMWIsKk5x8cSftElwR/ISJMcv7BkbRP\ndMlHLUSESc8/OJL2iS75qIWIMMn5B0fSPtElwV+ICJO0T3Ak7RNd8lELEWGS9gmOBP/oko9aiAiT\n4B8cyflHl3zUQkSY5PyDIzn/6JLgL0SEGY0S/IMhaZ/oko9aiAgzmaC+XutWxD5J+0RXSz/qnwNb\ngK/dthUAPwFFwKtu2/8FLAOebGFdQrRpiYlQW6t1K2KfpH2iq6XBfyWQ77FNARYAFwKTndsGAEnA\nSMAEyGWMRLtjNkvwD4akfaKrpR91GWDxsn0Cai//N87H5wOLnfe/AOTK1aLdMZuhrk5NawjfJPhH\nVzg/6tVAD2AscCuQBaQDlc7ny52PhWhX9Hr1pK/k/f1TFEn7RFOga/jmAm95bDuC2sP3VO38WYPa\n+++OGvBTndvTUL8xCNHuuFI/CQlatyR2Sc8/ugIF/6OoOfxgpKD28uOA81BP8NYB04B3gdHAK95e\nOHPmzIb7BQUFFBQUBFmlEG2DK/hnZDRuUxTYtg127IDKSnU+gMmkniBOSlJ/um6ux/Hxp27vWIK/\nf8XFxRQXF4dtfy39MxoIPIp6Avd74HLgemAq4ADeAJ5xln0S9cTvOuBPXvalKJIMFae4s86CJUvg\n7LPVx99/D1OnwsmT0KcPpKaqs4Dr69WDRE2NequubrxfU6POFzCbmx4cPA8UiYlqmikuLvSb68Di\n/tPbtkiU2boV1q6FoqLwfOanOp36wbW4KxALfQgJ/uKU16sXvP029O4NmzdDQQE89RT85jeh9eRt\ntsaDg+eBwfW4ulotZ7eHfgP1G4nrXzKYn+Eo43LBBTBlSvCfR3vW2uAfKO0jhAgD9+Get9wCDz0E\nE7ydOQvAYICUFPUmRGtIhk2IKEhPh7Iy+PFH2L1bTfkIoSXp+QsRBdnZcPw4fPYZTJqk5teF0JIE\nfyGiICcHDh6EhQvh668Dlxci0iTtI0QU5OTAq69C9+7ws59p3RohJPgLERX5+eoon5tu0rolQqhk\nqKcQUVBfDy+8ANOmqRO5hGgtGecvhBDtUGuDv6R9hBCiHZLgL4QQ7ZAEfyGEaIck+AshRDskwV8I\nIdohCf5CCNEOSfAXQoh2SIK/EEK0QxL8hRCiHZLgL4QQ7ZAEfyGEaIck+AshRDskwV8IIdohCf5C\nCNEOSfAXQoh2SIK/EEK0QxL8hRCiHZLgL4QQ7ZAEfyGEaIck+AshRDskwV8IIdqhlgb/m4GVztsE\n5zYDsAD4GrjXrey/gGXAky2sSwghRJi1NPh/DgwFRgB3OrddAWx2bhsO5AIDgCRgJGACBrWmsdFS\nXFysdROakTYFLxbbJW0KjrQpeloa/Pc6f9oBm/P++cAS5/0iYLBz22Lnti9QDxgxLxZ/2dKm4MVi\nu6RNwZE2RU9rc/63AB8676cDFc775c7H6UClxzYhhBAaMwR4Phd4y2PbYWAiaq9+LDDeub0cSHPe\nTwV2AinO+zifK2tle4UQQmioM+qJ3Qy3bVcC9znv/xf1wNEfeN657Tm85/x3Aorc5CY3ucktpNtO\nNPA8sA01t18ExKN+i1iIelC4z63sk6ijfZ6KchuFEEIIIYQQQgghhGa0nAB2PrAcNU31T+e2u52P\nF9J4MvxaZ7mPUE9gR8PtznbESpsmoQ7V/RLIi5E2xQOLUNOOH6LOI9GiXZ2AtUAtjaPngm3HRcAK\n1M+1c4TbdSbq/9pXwOtubY1mu7x9VgC/BPa5Pda6TWOApc66+sdIm+YBxajD6Tto0KawGgC84Lw/\nh+hPAMtFDRig/pOOBD52Pr4HuAowov7D6IFrgLui0K544FVnvdkx0KbOwEtuj3NioE0A44C/Oe//\nBfitRu2KRx3CXOSsJ5TP50vUSZCDgWcj3K50GgPEw8BlGrTLs00ubwDfOO9r3SYz8K5H+7RuUz7w\nsvO5icCtqJ2KVrVJy7V9tJ4AdhSwOO9bgV6oR1b39nQDNgIOotfGG4HXAB3qAVHrNl0CxDnrejpG\n2gRwgsZ5IxlAV9R/lmi3q57GIcyh/M7MqD27amAV6t9fpNqF875rzo0VdXJmtNvl2SaAS1F7s4rz\ncXcN26Rz1ucAPgXmA4katwmgFEh23s9A/dtvdZu0DP6xMgGsL2oPu4zGSWoVNE5S89wWSUZgFI1B\nzFv90W5TrrNdhUAN6nwNrdsE6rpSA4AfgYGow95cf09ativYz8d9G6gH2GjIQ01rLEYNJFq3axLq\nN28XrT+rXNS0y1jU9Mk0Av9OI92mfUAd6vI504APCMPvTsvgX472E8AygWeAGzzak+psj7dtkXQ9\n6ldgl1hoUxnq10tQv1KeGQNtAvWz+hjo7fxpjIF2KT7qDLQN1KVSIs2VUrwJtcdYpnG7LkI9iFvd\ntmnZJsVZ3zfO+18CPdH+9zcKtWNzLvAgaoqn1Z+TlsF/JTDaeX+083E0ueYl3AUcA1ajfsig9nJX\nAttRg4vebVsk/Qz4HepXzl6oKQSt27QC9dsRqCe/9sdAm0D9Iy913i8BzoiBdukI/u+oBvVruis/\nuynC7QL1HNtzwFbn4x0atkvnrPsKGv/eH0Lbz8r1++vpfNwf2B0DbUoFTjoflzgfa/031WpaTgCb\ngBr0XRPVhqCeoPMcpXEd0R/tA4297Vho0z9QP6N3UHvYsdCmDNTURRHqKrPpGrXLgJpzPYmaux4c\nQjtGox5clwKnRbhdI1FTAq6/93EatMu9TV8A57k9t8ztvlZtcv3+bkMdFfUJjalCrdv0Puq5pGLU\nb9/RbpMQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQohw+n9+CDD82vpK+wAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f031441f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vTrUbC1xDzGB4APBpYH/gnJLzHURMS38esBswF7gS2LxWv4AkqansC6xP5CWI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9rve8B72HDCNYHt6wH/B8zx3vOLrKGBfRqwYux6YAF2n9b1NF/naxa5dcTWBJ5PDuxzK/BBnn+H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\nIiIiIiIiIiIiIiIiIiIiIiIiEjv/DzHZBYFKYb94AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f032ca9ff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZV1dXevToQVhYGBEREcTHxwNQUFCAyWTC29ubGTNmkJqaah3rSEZGBjU1NZw5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\nFREREbFa/x/10yNuli7B5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f032c32f590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JiIiIOA0VJiIiIuI07qivC0vviorKvw/zG0BgYDtG43S910RERG4pl1sxcdV04b/85S/M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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f031441f190>"
      ]
     },
     "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": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\"><tr><th>inv_first_ISI.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.993048659384</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.997207818109</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_first_ISI.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.99850224663</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>AP1_amp.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>116.918095661</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>-1.00080064051</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>57.1428571429</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>-1.00080064051</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>-1.00080064051</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>-1.00080064051</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_time_to_first_spike.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>-1.00080064051</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>32.5732899023</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>APlast_amp.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>79.1130022615</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_third_ISI.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>93.4579439252</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>9.18273645547</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>AHP_depth.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>7.86903448031</td></tr><tr><th>feature sd</th><td>6.93369176723</td></tr></table></td></tr><tr><th>inv_third_ISI.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>296.8</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>206.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>voltage_deflection_begin.Step-2.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>-70.7557371735</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>-999.2</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>time_to_last_spike.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>379.6</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>92.5925925926</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>34.0136054422</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_first_ISI.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.999100809271</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>5.04540867811</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_second_ISI.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>123.456790123</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_second_ISI.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>13.2625994695</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>9.18273645547</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>voltage_deflection.Step-1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>-27.033456893</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_last_ISI.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>33.1125827815</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fourth_ISI.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>20.5761316873</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>20.9205020921</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.6</th><td><table border=\"1\"><tr><th>feature mean</th><td>9.18273645547</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.4</th><td><table border=\"1\"><tr><th>feature mean</th><td>5.04540867811</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>voltage_deflection.Step-2.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>-71.4520875106</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_fifth_ISI.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>time_to_last_spike.Step1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>398.4</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>voltage_deflection_begin.Step-1.0</th><td><table border=\"1\"><tr><th>feature mean</th><td>-28.7789580806</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.2</th><td><table border=\"1\"><tr><th>feature mean</th><td>0.0</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr><tr><th>inv_last_ISI.Step0.8</th><td><table border=\"1\"><tr><th>feature mean</th><td>20.8768267224</td></tr><tr><th>feature sd</th><td>0.0</td></tr></table></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize model feature values in a table\n",
    "for key, value in score.prediction.iteritems():\n",
    "    for _key_, value in score.prediction[key].iteritems():\n",
    "        score.prediction[key][_key_] = str(value)\n",
    "               \n",
    "IPython.display.HTML(json2html.convert(json = score.prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Model CA1_pyr_cAC_16AUG16 (Model) achieved score Z_sum = 51.43 on test 'Somatic features test (SomaticFeaturesTest)'. ===\n"
     ]
    }
   ],
   "source": [
    "#Summarize and print the score achieved by the model on the test using SciUnit's summarize function\n",
    "score.summarize()\n"
   ]
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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