{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![NeuronUnit Logo](https://raw.githubusercontent.com/scidash/assets/master/logos/neuronunit/NeuronUnitBlack2.png)\n",
    "# Chapter 4\n",
    "Back to [Chapter 3](chapter3.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the third chapter we tested a reduced neuron model.  Here we'll test a multicomparmental Cerebellar Granule Cell model using data from the [NeuroElectro](http://neuroelectro.org) project.  \n",
    "### We'll test the resting potential and action potential width of a couple of cerebellar granule cell models.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import quantities as pq\n",
    "import sciunit\n",
    "from neuronunit import tests,capabilities\n",
    "from neuronunit import neuroelectro # This module allows interaction with NeuroElecto through a REST API.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we'll use models downloaded from [Open Source Brain](http://www.opensourcebrain.org) and run via [neuroConstruct](http://www.neuroconstruct.org) using [execnet](http://codespeak.net/execnet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Please add an NC_HOME environment variable corresponding                     to the location of the neuroConstruct directory.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/neuronunit-0.1.8.6-py3.5.egg/neuronunit/neuroconstruct/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mNC_HOME\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"NC_HOME\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.5/os.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    724\u001b[0m             \u001b[0;31m# raise KeyError with the original key value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 725\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    726\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecodevalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'NC_HOME'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-e9bc29934011>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mneuronunit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneuroconstruct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnc_models\u001b[0m \u001b[0;31m# Model classes for use with neuroConstruct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m                                                      \u001b[0;31m# (http://www.neuroconstruct.org), a model and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                                                      \u001b[0;31m# simulation manager for detailed neuron models.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpythonnC\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mputils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOSB_MODELS\u001b[0m \u001b[0;31m# Path to models downloaded from Open Source Brain.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/neuronunit-0.1.8.6-py3.5.egg/neuronunit/neuroconstruct/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     raise Exception(\"Please add an NC_HOME environment variable corresponding\\\n\u001b[0;32m---> 10\u001b[0;31m                      to the location of the neuroConstruct directory.\")\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mNC_HOME\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Please add an NC_HOME environment variable corresponding                     to the location of the neuroConstruct directory."
     ]
    }
   ],
   "source": [
    "import neuronunit.neuroconstruct.models as nc_models # Model classes for use with neuroConstruct \n",
    "                                                     # (http://www.neuroconstruct.org), a model and \n",
    "                                                     # simulation manager for detailed neuron models.  \n",
    "from pythonnC.utils.putils import OSB_MODELS # Path to models downloaded from Open Source Brain.\n",
    "\n",
    "# We will test cerebellar granule cell models.  \n",
    "brain_area = 'cerebellum'\n",
    "neuron_type = 'cerebellar_granule_cell'\n",
    "neuron_type_path = os.path.join(OSB_MODELS,brain_area,neuron_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will look up data for cerebellar granule cells by [NeuroLex](http://neurolex.org) ID to make sure we get an unambiguous neuron type.<br>\n",
    "You can browse [NeuroLex](http://neurolex.org) to find your favorite neuron type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neurolex_id = 'nifext_128' # Cerebellar Granule Cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we instantiate two tests parameterized by data retrieved on the fly from [NeuroElectro](http://neuroelectro.org).\n",
    "One for resting membrane potential:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data values from neuroelectro.org\n",
      "http://www.neuroelectro.org/api/1/nes/?nlex=nifext_128&e__name=Resting+Membrane+Potential\n"
     ]
    }
   ],
   "source": [
    "# Specify reference data for a test of resting potential for a granule cell.  \n",
    "reference_data = neuroelectro.NeuroElectroSummary(\n",
    "    neuron = {'nlex_id':neurolex_id}, # Neuron type lookup using the NeuroLex ID.  \n",
    "    ephysprop = {'name':'Resting Membrane Potential'}) # Ephys property name in the NeuroElectro ontology. \n",
    "reference_data.get_values() # Get and verify summary data for the combination above from neuroelectro.org. \n",
    "\n",
    "rmp_test = tests.RestingPotentialTest(\n",
    "                observation = {'mean':reference_data.mean*pq.mV, # In millivolts.  \n",
    "                               'std':reference_data.std*pq.mV},\n",
    "                name = 'Resting Potential')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And one for action potential width:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting data values from neuroelectro.org\n",
      "http://www.neuroelectro.org/api/1/nes/?nlex=nifext_128&e__name=Spike+Half-Width\n"
     ]
    }
   ],
   "source": [
    "# Specify reference data for a test of action potential width.  \n",
    "reference_data = neuroelectro.NeuroElectroSummary(\n",
    "    neuron = {'nlex_id':neurolex_id}, # Neuron type lookup using the NeuroLex ID.  \n",
    "    ephysprop = {'name':'Spike Half-Width'}) # Ephys property name in the NeuroElectro ontology. \n",
    "reference_data.get_values() # Get and verify summary data for the combination above from neuroelectro.org. \n",
    "\n",
    "spikewidth_test = tests.InjectedCurrentAPWidthTest(\n",
    "                observation = {'mean':reference_data.mean*pq.ms, # In milliseconds.  \n",
    "                               'std':reference_data.std*pq.ms},\n",
    "                name = 'Spike Width',\n",
    "                params={'injected_square_current':{'amplitude':6.0*pq.pA,\n",
    "                                            'delay':50.0*pq.ms,\n",
    "                                            'duration':500.0*pq.ms}}) # 6 pA of injected current in a 500 ms square pulse.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = tests.InjectedCurrentAPWidthTest(\n",
    "                observation = {'mean':reference_data.mean*pq.ms, # In milliseconds.  \n",
    "                               'std':reference_data.std*pq.ms},\n",
    "                name = 'Spike Width',\n",
    "                injected_square_current = {'amplitude':6.0*pq.pA,\n",
    "                                            'delay':50.0*pq.ms,\n",
    "                                            'duration':500.0*pq.ms}) # 6 pA of injected current in a 500 ms square pulse.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'injected_square_current': {'amplitude': array(6.0) * pA,\n",
       "  'delay': array(50.0) * ms,\n",
       "  'duration': array(500.0) * ms},\n",
       " 'params': {'injected_square_current': {'amplitude': array(6.0) * pA,\n",
       "   'delay': array(50.0) * ms,\n",
       "   'duration': array(500.0) * ms}}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's create a test suite to group these two tests of a granule cell's properties.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a test suite from these two tests.  \n",
    "suite = sciunit.TestSuite('EPhys Tests',(spikewidth_test,rmp_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tests and tests suites can be executed against single models or lists of model.  \n",
    "Let's instantiate a few different granule cell models and add them to a list to be tested.<br>\n",
    "Each of them is already parameterized in corresponding [NeuroML](http://neuroml.org) files associated with the [Open Source Brain](http://opensourcebrain.org) project.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_names = ['GranuleCell','cereb_grc_mc'] # The names of two granule cell models \n",
    "                                             # available at http://opensourcebrain.org.\n",
    "models = [] # Our list of models to be tested.  \n",
    "for model_name in model_names:\n",
    "    model_info = (brain_area,neuron_type,model_name) # Information about each model.  \n",
    "    model = nc_models.OSBModel(*model_info) # Instantiate a model using that information.  \n",
    "    models.append(model) # Add to the list of models to be tested.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run each test against each model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Executing test <i>Spike Width</i> on model <i>GranuleCell</i>... "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Score is <a style=\"color: rgb(128,128,128)\">N/A</a>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Executing test <i>Resting Potential</i> on model <i>GranuleCell</i>... "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation...\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'ellipsis' object has no attribute 'start'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-2a71a1c42b20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Running these 2 tests against these 2 models means 4 total simulations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mscore_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjudge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstop_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/sciunit/__init__.py\u001b[0m in \u001b[0;36mjudge\u001b[0;34m(self, models, skip_incapable, stop_on_error, deep_error)\u001b[0m\n\u001b[1;32m    422\u001b[0m                 score = test.judge(model, skip_incapable=skip_incapable, \n\u001b[1;32m    423\u001b[0m                                           \u001b[0mstop_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop_on_error\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m                                           deep_error=deep_error)\n\u001b[0m\u001b[1;32m    425\u001b[0m                 log('Score is <a style=\"color: rgb(%d,%d,%d)\">' % score.color()\n\u001b[1;32m    426\u001b[0m                   + '%s</a>' % score)\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/sciunit/__init__.py\u001b[0m in \u001b[0;36mjudge\u001b[0;34m(self, model, skip_incapable, stop_on_error, deep_error)\u001b[0m\n\u001b[1;32m    294\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mErrorScore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mErrorScore\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstop_on_error\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;31m# An exception.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/sciunit/__init__.py\u001b[0m in \u001b[0;36mjudge\u001b[0;34m(self, model, skip_incapable, stop_on_error, deep_error)\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_judge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_incapable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_incapable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mCapabilityError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m                 \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNAScore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/sciunit/__init__.py\u001b[0m in \u001b[0;36m_judge\u001b[0;34m(self, model, skip_incapable)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_capabilities\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# 2.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0;31m# 3.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/neuronunit/tests/__init__.py\u001b[0m in \u001b[0;36mgenerate_prediction\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrerun\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minject_square_current\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'injected_square_current'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 578\u001b[0;31m         \u001b[0mmedian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_median_vm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Use median for robustness.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    579\u001b[0m         \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_std_vm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m         \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'std'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/neuronunit/neuroconstruct/capabilities.py\u001b[0m in \u001b[0;36mget_median_vm\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mvm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_membrane_potential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;31m# A neo.core.AnalogSignal object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0mmedian_vm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmedian_vm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m   3509\u001b[0m     \"\"\"\n\u001b[1;32m   3510\u001b[0m     r, k = _ureduce(a, func=_median, axis=axis, out=out,\n\u001b[0;32m-> 3511\u001b[0;31m                     overwrite_input=overwrite_input)\n\u001b[0m\u001b[1;32m   3512\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3513\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3423\u001b[0m         \u001b[0mkeepdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3425\u001b[0;31m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3426\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m   3563\u001b[0m         \u001b[0mrout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpart\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3564\u001b[0m         \u001b[0mpart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3565\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpart\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3566\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3567\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rgerkin/Dropbox/miniconda3/lib/python3.5/site-packages/neo-0.4.1-py3.5.egg/neo/core/analogsignal.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m    207\u001b[0m                 \u001b[0mslice_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m                 \u001b[0mslice_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m                 \u001b[0mslice_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mslice_start\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'ellipsis' object has no attribute 'start'"
     ]
    }
   ],
   "source": [
    "# Running these 2 tests against these 2 models means 4 total simulations.  \n",
    "score_matrix = suite.judge(models,stop_on_error=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the results as a table.\n",
    "The 'cereb_grc_mc' model doesn't seem to spike, so there is insufficient data to produce a score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "score_matrix.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot some of the model output that led to those scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "fig,ax = plt.subplots(2,2,figsize=(8,6)) # Create plot windows.  \n",
    "for i,test in enumerate(score_matrix.tests): # For each test...\n",
    "    for j,model in enumerate(score_matrix.models): # And each model...\n",
    "        score = score_matrix[test,model] # Get the score.  \n",
    "        score.plot_vm(ax[i,j]) # And use a convenience method to plot the membrane potential.  \n",
    "fig.tight_layout() # Space the plots nicely. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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