AreTwoPSTHsIdentical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up `Python` \n",
    "\n",
    "The analysis presented in the manuscript and detailed next is carried\n",
    "out with `Python 3` (the following code runs and gives identical results\n",
    "with `Python 2`). We are going to use the 3 classical modules of\n",
    "Python's scientific ecosystem: `numpy`, `scipy` and `matplotlib`. We are\n",
    "also going to use two additional modules: `sympy` as well as `h5py`. We\n",
    "start by importing these modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "import scipy\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 2 users __must__ also type:\n",
    "`from __future__ import print_function, division, unicode_literals, absolute_import`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the data\n",
    "\n",
    "\n",
    "Our data ([Pouzat and Chaffiol,2015](https://zenodo.org/record/14281)) are stored in\n",
    "[HDF5](http://www.hdfgroup.org/HDF5/) format on the\n",
    "[zenodo](https://zenodo.org/) server (`DOI:10.5281/zenodo.1428145`).\n",
    "They are all contained in a file named\n",
    "`CockroachDataJNM_2009_181_119.h5`. The data within this file have an\n",
    "hierarchical organization similar to the one of a file system (one of\n",
    "the main ideas of the HDF5 format). The first organization level is the\n",
    "experiment; there are 4 experiments in the file: `e060517`, `e060817`,\n",
    "`e060824` and `e070528`. Each experiment is organized by neurons,\n",
    "`Neuron1`, `Neuron2`, etc, (with a number of recorded neurons depending\n",
    "on the experiment). Each neuron contains a `dataset` (in the HDF5\n",
    "terminology) named `spont` containing the spike train of that neuron\n",
    "recorded during a period of spontaneous activity. Each neuron also\n",
    "contains one or several further sub-levels named after the odor used for\n",
    "stimulation `citronellal`, `terpineol`, `mixture`, etc. Each a these\n",
    "sub-levels contains as many `datasets`: `stim1`, `stim2`, etc, as\n",
    "stimulations were applied; and each of these data sets contains the\n",
    "spike train of that neuron for the corresponding stimulation. Another\n",
    "`dataset`, named `stimOnset` containing the onset time of the stimulus\n",
    "(for each of the stimulations). All these times are measured in seconds.\n",
    "\n",
    "The data can be downloaded with `Python` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    from urllib.request import urlretrieve # Python 3\n",
    "except ImportError:\n",
    "    from urllib import urlretrieve # Python 2\n",
    "\n",
    "name_on_disk = 'CockroachDataJNM_2009_181_119.h5'\n",
    "urlretrieve('https://zenodo.org/record/14281/files/'+\n",
    "            name_on_disk,\n",
    "            name_on_disk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file is opened with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = h5py.File(\"CockroachDataJNM_2009_181_119.h5\",\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the raster plots\n",
    "\n",
    "We make our raster plots with a short function `raster_plot` that we define with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def raster_plot(train_list,\n",
    "                stim_onset=None,\n",
    "                color = 'black'):\n",
    "    \"\"\"Create a raster plot.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    train_list: a list of spike trains (1d vector with strictly\n",
    "      increasing elements).\n",
    "    stim_onst: a number giving the time of stimulus onset. If\n",
    "      specificied, the time are realigned such that the stimulus\n",
    "      comes at 0.\n",
    "    color: the color of the ticks representing the spikes.\n",
    "\n",
    "    Side effect:\n",
    "    A raster plot is created.\n",
    "    \"\"\"\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    if stim_onset is None:\n",
    "        stim_onset = 0\n",
    "    for idx,trial in enumerate(train_list):\n",
    "        plt.vlines(trial-stim_onset,\n",
    "                   idx+0.6,idx+1.4,\n",
    "                   color=color)\n",
    "    plt.ylim(0.5,len(train_list))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raster plots of the responses of Neuron 1 to citronellal and terpineol are then obtained with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xtof/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
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RkWnnbMez51xptDRl5NPYvpTdTDytaIuvzNA6sv2cyr/Ps9Xnu86uWCuVioyN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\ns0wz95vkY1u9erV0d3dLrVaTer0u3d3djXLTn/w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aEBRKKwMBYhNCLk1MIHYcz/cPfrO/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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9095fc8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "citron_onset = f[\"e060817/Neuron1/citronellal/stimOnset\"][...][0]\n",
    "e060817citron = [[f[y][...] for y in\n",
    "                  [\"e060817/Neuron\"+str(i)+\"/citronellal/stim\"+str(x)\n",
    "                   for x in range(1,21)]]\n",
    "                 for i in range(1,4)]\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "plt.subplot(121)\n",
    "raster_plot(e060817citron[0],citron_onset)\n",
    "plt.xlim(-2,6)\n",
    "plt.ylim(0,21)\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Time (s)\",fontdict={'fontsize':15})\n",
    "plt.ylabel(\"Trial\",fontdict={'fontsize':15})\n",
    "plt.title(\"Citronellal\",fontdict={'fontsize':20})\n",
    "plt.subplot(122)\n",
    "terpi_onset = f[\"e060817/Neuron1/terpineol/stimOnset\"][...][0]\n",
    "e060817terpi = [[f[y][...] for y in\n",
    "                 [\"e060817/Neuron\"+str(i)+\"/terpineol/stim\"+str(x)\n",
    "                  for x in range(1,21)]]\n",
    "                for i in range(1,4)]\n",
    "raster_plot(e060817terpi[0],terpi_onset)\n",
    "plt.xlim(-2,6)\n",
    "plt.ylim(0,21)\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Time (s)\",fontdict={'fontsize':15})\n",
    "plt.ylabel(\"Trial\",fontdict={'fontsize':15})\n",
    "plt.title(\"Terpineol\",fontdict={'fontsize':20})\n",
    "plt.subplots_adjust(wspace=0.4,hspace=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choosing the initial bin size\n",
    "\n",
    "The bin width should be chosen large enough to have a few events per bin most of the time. We set this width such that an expected number of 3 events per bin was obtained using the spontaneous frequency estimated from 60 seconds long recordings without stimulus presentation—more specifically, the width was set to the smallest millisecond larger or equal to the targeted count divided by the product of the spontaneous frequency and the number of trials.\n",
    "\n",
    "For the neurons of the data set we get the following spontaneous discharge rates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The spontaneous discharge rates are:\n",
      "  Neuron 1: 8.82 (Hz)\n",
      "  Neuron 2: 20.48 (Hz)\n",
      "  Neuron 3: 13.02 (Hz)\n"
     ]
    }
   ],
   "source": [
    "e060817_spont_nu = [len(f[\"e060817/Neuron\"+str(i)+\"/spont\"])/60\n",
    "                    for i in range(1,4)] \n",
    "print(\"The spontaneous discharge rates are:\")\n",
    "for i in range(len(e060817_spont_nu)):\n",
    "    print(\"  Neuron {0}: {1:.2f} (Hz)\".format(i+1,e060817_spont_nu[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the initial PSTH and stablizing them\n",
    "\n",
    "For the citronellal response of Neuron 1 we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_mean = 3\n",
    "n_stim_citron = len(e060817citron[0])\n",
    "n1citron = np.sort(np.concatenate(e060817citron[0]))\n",
    "left = -5+citron_onset\n",
    "right = 6+citron_onset\n",
    "n1citron = n1citron[np.logical_and(left <= n1citron,n1citron <= right)]-citron_onset\n",
    "bin_width_citron = np.ceil(target_mean/n_stim_citron/e060817_spont_nu[0]*1000)/1000\n",
    "n1citron_bin = np.arange(-5,6+bin_width_citron,bin_width_citron)\n",
    "n1citron_counts, n1citron_bin = np.histogram(n1citron,n1citron_bin)\n",
    "n1citron_stab = 2*np.sqrt(n1citron_counts+0.25)\n",
    "n1citron_x = n1citron_bin[:-1]+bin_width_citron/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the terpineol response we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_stim_terpi = len(e060817terpi[0])\n",
    "n1terpi = np.sort(np.concatenate(e060817terpi[0]))\n",
    "left = -5+terpi_onset\n",
    "right = 6+terpi_onset\n",
    "n1terpi = n1terpi[np.logical_and(left <= n1terpi,n1terpi <= right)]-terpi_onset\n",
    "bin_width_terpi = np.ceil(target_mean/n_stim_terpi/e060817_spont_nu[0]*1000)/1000\n",
    "n1terpi_bin = np.arange(-5,6+bin_width_terpi,bin_width_terpi)\n",
    "n1terpi_counts, n1terpi_bin = np.histogram(n1terpi,n1terpi_bin)\n",
    "n1terpi_stab = 2*np.sqrt(n1terpi_counts+0.25)\n",
    "n1terpi_x = n1terpi_bin[:-1]+bin_width_terpi/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the even and odd stimuli terpineol responses we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n1terpi_even = np.sort(np.concatenate(e060817terpi[0][0:20:2]))\n",
    "n1terpi_even = n1terpi_even[np.logical_and(left <= n1terpi_even,n1terpi_even <= right)]-terpi_onset\n",
    "bin_width_terpi_even = np.ceil(target_mean/n_stim_terpi/e060817_spont_nu[0]*1000)/1000/2\n",
    "n1terpi_even_bin = np.arange(-5,6+bin_width_terpi_even,bin_width_terpi_even)\n",
    "n1terpi_even_counts, n1terpi_even_bin = np.histogram(n1terpi_even,n1terpi_even_bin)\n",
    "n1terpi_even_stab = 2*np.sqrt(n1terpi_even_counts+0.25)\n",
    "\n",
    "n1terpi_odd = np.sort(np.concatenate(e060817terpi[0][1:20:2]))\n",
    "n1terpi_odd = n1terpi_odd[np.logical_and(left <= n1terpi_odd,n1terpi_odd <= right)]-terpi_onset\n",
    "bin_width_terpi_odd = np.ceil(target_mean/n_stim_terpi/e060817_spont_nu[0]*1000)/1000/2\n",
    "n1terpi_odd_bin = np.arange(-5,6+bin_width_terpi_odd,bin_width_terpi_odd)\n",
    "n1terpi_odd_counts, n1terpi_odd_bin = np.histogram(n1terpi_odd,n1terpi_odd_bin)\n",
    "n1terpi_odd_stab = 2*np.sqrt(n1terpi_odd_counts+0.25)\n",
    "n1terpi_odd_x = n1terpi_odd_bin[:-1]+bin_width_terpi_odd/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define two functions returning the boundaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def c95(x): return 0.299958+2.348443*np.sqrt(x)\n",
    "\n",
    "def c99(x): return 0.312456+2.890606*np.sqrt(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just have to make the graph after computing the `cumsum` of the differences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f9095e637f0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hlUrhcDhgsVjQrVs3PP3002KbjIwMr7A1evRorrJNARUZGQm73R7sblCY4PVGgcTrjQKp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\nGfobWfkFSdbcNf0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9095ec0978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1_diff_y = (n1terpi_stab-n1citron_stab)/np.sqrt(2)\n",
    "X1 = np.arange(1,len(n1terpi_x)+1)/len(n1terpi_x)\n",
    "Y1 = np.cumsum(n1_diff_y)/np.sqrt(len(n1_diff_y))\n",
    "n1_diff_terpi_y = (n1terpi_even_stab-n1terpi_odd_stab)/np.sqrt(2)\n",
    "X2 = np.arange(1,len(n1terpi_odd_x)+1)/len(n1terpi_odd_x)\n",
    "Y2 = np.cumsum(n1_diff_terpi_y)/np.sqrt(len(n1_diff_terpi_y))  \n",
    "xx = np.linspace(0,1,201)\n",
    "fig = plt.figure(figsize=(14,8))\n",
    "plt.plot(xx,c95(xx),color='grey',lw=2,linestyle='dashed')\n",
    "plt.plot(xx,-c95(xx),color='grey',lw=2,linestyle='dashed')\n",
    "plt.plot(xx,c99(xx),color='grey',lw=2)\n",
    "plt.plot(xx,-c99(xx),color='grey',lw=2)\n",
    "plt.plot(X2,Y2,color='grey',lw=2)\n",
    "plt.plot(X1,Y1,color='black',lw=2)\n",
    "plt.xlabel(\"Normalized time\",fontdict={'fontsize':20})\n",
    "plt.ylabel(\"$S_k(t)$\",fontdict={'fontsize':20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before closing the session we close our file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  }
 ],
 "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": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}