{
 "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 - Case2: stimulation, raw-mode (i.e. without evoking capability)"
   ]
  },
  {
   "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": [
       "['cells', 'subcell', 'synapses', '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": [
       "['PC1997aHausser',\n",
       " 'PC1997bHausser',\n",
       " 'PC2010Genet',\n",
       " 'GoC2007Solinas',\n",
       " 'GrC1994Gabbiani',\n",
       " 'PC2009Akemann',\n",
       " 'PC2013Marasco',\n",
       " 'PC2006Akemann',\n",
       " 'GoC2010Botta',\n",
       " 'PC2001Miyasho',\n",
       " 'PC2003Khaliq',\n",
       " 'GoC2011Souza',\n",
       " 'GrC2016Dover',\n",
       " 'PC2018Zang',\n",
       " 'PC2015Masoli',\n",
       " 'PC2011Brown',\n",
       " 'PC2015aForrest',\n",
       " 'GrC2011Souza',\n",
       " 'PC2015bForrest',\n",
       " 'GrC2001DAngelo',\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",
    "Although it is not essential to instantiate `ExecutiveControl` to launch the model, it is recommended if one were to visualize the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "exc = ec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "desired_model = exc.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 stimulation and without ``CerbUnit``'s capability, one is required to define the run-time parameters and the stimulation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtimeparam = { \"dt\": 0.025, \"celsius\": 37, \"tstop\": 1000, \"v_init\": -65 }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the case of current clamp injection,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stimparam = { \"type\": [\"current\", \"IClamp\"],\n",
    "              \"stimlist\": [ {\"amp\": 0.1, \"dur\": 300.0, \"delay\": 200.0},\n",
    "                            {\"amp\": 0.1, \"dur\": 300.0, \"delay\": 500.+200.} ],\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. Executing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 0.22818700000000014 seconds ---\n"
     ]
    }
   ],
   "source": [
    "desired_model = exc.launch_model( parameters = runtimeparam, stimparameters = stimparam, stimloc = \"soma\",\n",
    "                                  onmodel = desired_model )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Saving the response\n",
    "In ``CerebUnit`` the model response are saved in an HDF5 file based on NWB:2.0 scheme. Execution of ``save_response()`` returns the filename and its path. Below, this return full path and filename is assigned to the attribute **``fullfilename``** of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "desired_model.fullfilename = exc.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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['soma v']"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 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": 15,
   "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": [
    "exc.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
}