{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change to the root directory of the project\n",
    "import os\n",
    "if os.getcwd().split(\"/\")[-1] == \"examples\":\n",
    "    os.chdir('..')\n",
    "    \n",
    "# This will reload all imports as soon as the code changes\n",
    "%load_ext autoreload\n",
    "%autoreload 2    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import matplotlib.pyplot as plt\n",
    "except ImportError:\n",
    "    import sys\n",
    "    !{sys.executable} -m pip install matplotlib\n",
    "    import matplotlib.pyplot as plt\n",
    "    \n",
    "import numpy as np\n",
    "\n",
    "from neurolib.models.aln import ALNModel\n",
    "from neurolib.utils.parameterSpace import ParameterSpace\n",
    "from neurolib.optimize.exploration import BoxSearch\n",
    "\n",
    "# a nice color map\n",
    "plt.rcParams['image.cmap'] = 'plasma'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aln = ALNModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = ParameterSpace({\"mue_ext_mean\": np.linspace(0, 3, 2), \"mui_ext_mean\": np.linspace(0, 3, 2)})\n",
    "# info: chose np.linspace(0, 3, 21) or more, values here are low for testing\n",
    "search = BoxSearch(aln, parameters, filename=\"example-1.hdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "search.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "search.loadResults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Number of results: {}\".format(len(search.results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example analysis of the results\n",
    "# The .results attribute is a list and can be indexed by the run \n",
    "# number (which is also the index of the pandas dataframe .dfResults).\n",
    "# Here we compute the maximum firing rate of the node in the last second\n",
    "# and add the result (a float) to the pandas dataframe.\n",
    "for i in search.dfResults.index:\n",
    "    search.dfResults.loc[i, 'max_r'] = np.max(search.results[i]['rates_exc'][:, -int(1000/aln.params['dt']):])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Input to I')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(search.dfResults.pivot_table(values='max_r', index = 'mui_ext_mean', columns='mue_ext_mean'), \\\n",
    "           extent = [min(search.dfResults.mue_ext_mean), max(search.dfResults.mue_ext_mean),\n",
    "                     min(search.dfResults.mui_ext_mean), max(search.dfResults.mui_ext_mean)], origin='lower')\n",
    "plt.colorbar(label='Maximum rate [Hz]')\n",
    "plt.xlabel(\"Input to E\")\n",
    "plt.ylabel(\"Input to I\")"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}