{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To run the models in cerebmodels the user must be in the root of `~/cerebmodels/`**\n",
    "\n",
    "Since this notebook is in `~/cerebmodels/docs/notebooks/` the user must move up two directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/main-dev/cerebmodels/docs\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/main-dev/cerebmodels\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Execution - Case3: capability-mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Set-up\n",
    "\n",
    "### Import `ExecutiveControl` of cerebmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from executive import ExecutiveControl as ec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Search for models and pick a desired model\n",
    "\n",
    "### 2.1. See available model scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['subcell', 'synapses', 'cells', 'microcircuits', 'layers']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec.list_modelscales()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. See available models for a particular scale\n",
    "Below shows the available models at the level of cellular modelling scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PC2010Genet',\n",
       " 'GoC2007Solinas',\n",
       " 'GrC1994Gabbiani',\n",
       " 'PC2009Akemann',\n",
       " 'PC2013Marasco',\n",
       " 'PC2006Akemann',\n",
       " 'GoC2010Botta',\n",
       " 'PC2001Miyasho',\n",
       " 'PC2003Khaliq',\n",
       " 'GoC2011Souza',\n",
       " 'GrC2016Dover',\n",
       " 'PC2018Zang',\n",
       " 'PC1997aHausser',\n",
       " 'PC2015Masoli',\n",
       " 'PC2011Brown',\n",
       " 'PC2015aForrest',\n",
       " 'GrC2011Souza',\n",
       " 'PC2015bForrest',\n",
       " 'GrC2001DAngelo',\n",
       " 'PC1997bHausser',\n",
       " 'DCN2011Luthman',\n",
       " 'GrC2009Diwakar']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec.list_models( modelscale = \"cells\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Pick a desired model\n",
    "It is essential to instantiate `ExecutiveControl` to launch the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "exc = ec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "desired_model = ec.choose_model( modelscale = \"cells\", modelname = \"PC2003Khaliq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Launching (executing) the desired model\n",
    "### 3.1. Setting the parameters\n",
    "For our case of launching the model with ``CerbUnit``'s capability, one is required to define the run-time parameters and depending on what one intend to do with the simulation, with or without the stimulation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtimeparam = { \"dt\": 0.025, \"celsius\": 37, \"tstop\": 500, \"v_init\": -65 }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this particular example let us consider the case where the capability is with respect to electrophysiological measurement, in particular the resting membrane voltage (from soma). Since the first few iterations of a model might require it to stabilize let us consider injecting a dummy (zero amplitude) current clamp injection. This results in a second epoch and based on eFEL the resting membrane voltage is extracted from the moment prior to the stimulus (0 amplitude)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stimparam = { \"type\": [\"current\", \"IClamp\"],\n",
    "              \"stimlist\": [ {\"amp\": 0.0, \"dur\": 300.0, \"delay\": 200.0} ],\n",
    "              \"tstop\": runtimeparam[\"tstop\"] }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, another required parameter is where the user would like to stimulate. For instance if the interested site of injecting the current is the soma of the cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stim_here = desired_model.cell.soma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: The name \"soma\" here is not ad-hoc but dependent on how the soma is named in the model template. See **4.1** (below). Here, soma is named as it is but it could easily have been \"Soma\", \"somas\", etc ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Importing the desired capability\n",
    "For executing the model for the capability ``produce_soma_restingVm`` which is an electrophysiological measurement, the user must import ``ProducesEphysMeasurement``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cerebunit.capabilities.cells.measurements import ProducesEphysMeasurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Executing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sim produce_soma v_restingVm starting ...\n",
      "Simulation produce_voltage_response starting ...\n",
      "--- 0.156949 seconds ---\n",
      "File saving ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/envs/py3-cerebmodels/lib/python3.5/site-packages/pynwb/file.py:619: UserWarning: Date is missing timezone information. Updating to local timezone.\n",
      "  warn(\"Date is missing timezone information. Updating to local timezone.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved.\n",
      "Simulation produce_voltage_response Done.\n",
      "--- 0.27159400000000034 seconds ---\n",
      "Simulation produce_soma v_restingVm Done.\n",
      "--- 5.397078 seconds ---\n"
     ]
    }
   ],
   "source": [
    "desired_model = exc.launch_model( parameters = runtimeparam, stimparameters = stimparam, stimloc = stim_here,\n",
    "                                  onmodel = desired_model, mode = \"capability\",\n",
    "                                  capabilities = {\"model\": \"produce_soma_restingVm\",\n",
    "                                                  \"vtest\": ProducesEphysMeasurement} )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When executing the model in capability mode the model response(s) get saved. Therefore, in this mode there is not need to invoke ``ec.save_response()``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualizing the response\n",
    "### 4.1. List model regions of the chosen model\n",
    "Before one calls the visualization function the user must define the the region of interest (roi) from which the response will be visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['soma v']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exc.list_modelregions( chosenmodel = desired_model )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Visualize all events\n",
    "The method ``visualize_all`` plots all the events in respective sub-plot. In our particular case with not stimulation the simulation has only one event therefore only one sub-plot is seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exc.visualize_all(chosenmodel = desired_model, roi=\"soma v\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Visualize all events in one plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ec.visualize_aio(chosenmodel = desired_model, roi=\"soma v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}