examples/example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GLMCC example usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # pandas is not required for GLMCC itself; if you don't have one yet, you need to install manually\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from glmcc import GLMCC  # model\n",
    "from glmcc import spiketime_relative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load sample data with pandas.  \n",
    "This sample data is taken from the [original code](https://github.com/NII-Kobayashi/GLMCC) and edited by the author.  \n",
    "**Note that \"spiketime\" should be in msec scale, not in sec scale.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neuron</th>\n",
       "      <th>spiketime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>93.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>107.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>116.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>218.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neuron  spiketime\n",
       "0       1      20.95\n",
       "1       1      93.55\n",
       "2       1     107.40\n",
       "3       1     116.45\n",
       "4       1     218.45"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('./sample_data.csv')  # spike train in msec scale\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rasterplot\n",
    "start, end = 1000.0, 2000.0  # msec\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.scatter(df.spiketime, df.neuron, s=20, c='k', edgecolors='w')\n",
    "plt.xlim(start, end)\n",
    "plt.xlabel('time [ms]')\n",
    "plt.ylabel('neuron #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simply look at cross-correlogram between two neurons, do as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLM not fitted yet, plotting only cross-correlogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm = GLMCC(delay=4.0)  # set synaptic delay to initialize GLMC\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "idx_i, idx_j = 4, 7\n",
    "\n",
    "# relative spiketime (target neuron - reference neuron)\n",
    "t_sp = spiketime_relative(spiketime_tar=list(df.query('neuron==@idx_i').spiketime), \n",
    "                          spiketime_ref=list(df.query('neuron==@idx_j').spiketime), window_size=50.0)\n",
    "glm.plot(ax=ax, t_sp=t_sp)\n",
    "\n",
    "ax.set_title(f'cross-correlogram: neuron {idx_i} to {idx_j}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's estimate the synaptic weights with GLMCC based on the cross-correlogram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_plot(ax, idx_i, idx_j, delay, window_size=50.0, verbose=True):\n",
    "    # prepare relative spiketime (target neuron - reference neuron)\n",
    "    t_sp = spiketime_relative(spiketime_tar=list(df.query('neuron==@idx_i').spiketime), \n",
    "    spiketime_ref=list(df.query('neuron==@idx_j').spiketime), window_size=window_size)\n",
    "\n",
    "    # model settings\n",
    "    glm = GLMCC(delay=delay)  # tune synaptic delay\n",
    "    glm.fit(t_sp, verbose=verbose)\n",
    "    glm.plot(ax=ax, t_sp=t_sp)\n",
    "\n",
    "    # recommended plot layouts\n",
    "    ax.set_xlabel(r'$\\tau [ms]$', fontsize=16)\n",
    "    ax.set_ylabel(r'$C(\\tau)$', fontsize=16)\n",
    "    ax.set_title(f'neuron {idx_i} to {idx_j}', fontsize=18)\n",
    "    ax.tick_params(direction='in', which='major', labelsize=12)\n",
    "    ax.yaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "    return glm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iterations until convergence: 21\n",
      "=============== GLMCC summary ===============\n",
      "connectivity from neuron j to neuron i: \n",
      "\t estimated J_ij, J_ji: 1.04, -0.01\n",
      "\t threshold J_ij, J_ji: 0.44, 0.46\n",
      "max log posterior: 7884\n",
      "=============================================\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "glm = fit_and_plot(ax=ax, idx_i=4, idx_j=7, delay=4.0)\n",
    "glm.summary()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "fit_and_plot(ax=axes[0], idx_i=4, idx_j=8, delay=2.0, verbose=False)\n",
    "fit_and_plot(ax=axes[1], idx_i=2, idx_j=8, delay=2.0, verbose=False)\n",
    "\n",
    "axes[1].set_ylabel('')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.9 ('venv': venv)",
   "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": "3.9.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "49386a9bf39ee3af13b8a40b858eaf15ee216b04fcde383189718f6df304bdd8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}