Example_format_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of correctly formatting data\n",
    "\n",
    "For use in decoding (see \"Examples_all_decoders\" and \"Examples_kf_decoder\"), we need the following format of inputs:\n",
    "- Neural data should be a matrix of size \"number of time bins\" x \"number of neurons\", where each entry is the firing rate of a given neuron in a given time bin\n",
    "- The output you are decoding should be a matrix of size \"number of time bins\" x \"number of features you are decoding\"\n",
    "\n",
    "In this example, we load Matlab data that contains \n",
    "- The spike times of all neurons. In Matlab, \"spike_times\" is a cell of size \"number of neurons\" x 1. Within spike_times{i} is a vector containing all the spike times of neuron i.\n",
    "- A continuous stream of the output variables. In this example, we are aiming to decode velocity. In Matlab, \"vels\" is a matrix of size \"number of recorded time points\" x 2 (x and y velocities were recorded) that contains the x and y velocity components at all time points. \"vel_times\" is a vector that states the time at all recorded time points. \n",
    "\n",
    "We will put this data in the format described above, with the help of the functions \"bin_spikes\" and \"bin_output\" that are in the file \"preprocessing_funcs.py\"\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "###Import standard packages###\n",
    "import numpy as np\n",
    "from scipy import io\n",
    "\n",
    "###Import functions for binning data for preprocessing###\n",
    "from Neural_Decoding.preprocessing_funcs import bin_spikes\n",
    "from Neural_Decoding.preprocessing_funcs import bin_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "The data for this example can be downloaded at this [link](https://www.dropbox.com/sh/n4924ipcfjqc0t6/AACPWjxDKPEzQiXKUUFriFkJa?dl=0&preview=s1_data_raw.mat)\n",
    "\n",
    "It was recorded by Raeed Chowdhury from Lee Miller's lab at Northwestern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "###Load Data###\n",
    "folder='miller_lab_data/' #ENTER THE FOLDER THAT YOUR DATA IS IN\n",
    "# folder='/Users/jig289/Dropbox/MATLAB/Projects/In_Progress/BMI/Processed_Data/' \n",
    "data=io.loadmat(folder+'s1_data_raw.mat')\n",
    "spike_times=data['spike_times'] #Load spike times of all neurons\n",
    "vels=data['vels'] #Load x and y velocities\n",
    "vel_times=data['vel_times'] #Load times at which velocities were recorded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "dt=.05 #Size of time bins (in seconds)\n",
    "t_start=vel_times[0] #Time to start extracting data - here the first time velocity was recorded\n",
    "t_end=vel_times[-1]  #Time to finish extracting data - here the last time velocity was recorded\n",
    "downsample_factor=1  #Downsampling of output (to make binning go faster). 1 means no downsampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Put data in binned format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#When loading the Matlab cell \"spike_times\", Python puts it in a format with an extra unnecessary dimension\n",
    "#First, we will put spike_times in a cleaner format: an array of arrays\n",
    "spike_times=np.squeeze(spike_times)\n",
    "for i in range(spike_times.shape[0]):\n",
    "    spike_times[i]=np.squeeze(spike_times[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "###Preprocessing to put spikes and output in bins###\n",
    "\n",
    "#Bin neural data using \"bin_spikes\" function\n",
    "neural_data=bin_spikes(spike_times,dt,t_start,t_end)\n",
    "\n",
    "#Bin output (velocity) data using \"bin_output\" function\n",
    "vels_binned=bin_output(vels,vel_times,dt,t_start,t_end,downsample_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_data.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing 10 random neurons\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAABJCAYAAAA+NLe4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAMpElEQVR4nO3dT2tkxRrH8d+RcIXIoNABr0pgoN1MRGaIod0IHjcym97o0mW/gIv2kI3vINDgG+iVr0AaRFx4e+MmtLgyuPDgQPwzeqdXowH/4LmLydN5+kmdTnqqMzPR72czne46VU9VPVXnFD2ZKeq6FgAAAADgwT3xqAMAAAAAgMuOgxUAAAAAZOJgBQAAAACZOFgBAAAAQCYOVgAAAACQiYMVAAAAAGRa+cGqKIrNoij+WxTFQVEUXxVF8Z/web8oirooio1Vtw0AAAAAj8LaMoVv3rxZf/LJJwvL/PDDD/rxxx+1vb2te/fu6ZVXXvng4ODgg62tLR0eHurNN9/U119/rS+++OJ/WZEDAB4rg8FAktTv9x9xJACWwdpdHmP2j1ek3lzqG6u7d++eWea5557T9va2JOnKlSu6du2avv/+e0nSu+++q729PRVFMhYAAAAAuJQu9Hesbt++rS+//FKvvvqqPvroI73wwgu6fv36RTYJAAAAAA9dUdf1uQvv7OzUk8nkXGV/+eUXvf7663r//fd18+ZNvfHGG/r000/19NNP6+rVq5pMJtrY4NesAAAAAFwq+X8V8Lz++OMPvf3223rnnXf01ltvqaoqffvtt7p+/bquXr2q7777Ttvb27pz585FNA8AAAAAD9VS/3jFedR1rV6vp2vXrum9996TJL388sv6+eefZ2X4xgoAAADA38nKv7H6/PPP9eGHH+qzzz7TjRs3dOPGDX388cerbgYAAAAAHhsr/8bqtdde01m/t3X79u1VNwsAAAAAj8yF/quAAAAAAPBPwMEKAAAAADKt7K8CLvM/UC/7v1Vf5v/dutvtSpJGo1FjmUfZP9/2ZR7nVbksY3DZ5+0yxvywMDZoYrlhLmuOkON4WC4618jlv79l55hvrAAAAAAgEwcrAAAAAMjEwQoAAAAAMnGwAgAAAIBMHKwAAAAAIFNx1n/m6+3s7NSTyeQCwwEAAACAx1qRepNvrAAAAAAgEwcrAAAAAMjEwQoAAAAAMnGwAgAAAIBMHKwAAAAAIBMHKwAAAADItJZbwWAwkCT1+/3Za9Pv95euZzweS5JGo1FuaI+NOEbD4VCS1Ov1ZmX8+DWNWxzf8XisqqrU6/WWGutl4k3FvyjGZcr7/gyHQ7Xb7eS8N9V3nlgexGAw0Hg8VlmWK6u7qc5Fa6apf+cd06Yyy8RbVZXa7bbKspz7edH6XDbus2I+z3U583WenFpFri2bVxeV36bb7UqSyrKc7UntdltVVWk6narT6Wg0Gi0dx7J7xLLxLntvWLSfneeai5iHnDovYj6arjFnjdWq98sYxzJ7v//M57i3ijjP2++LyKXcZy17zpJ0IfO2Cqu6l1ldNldN9cVxkdJjE3PqrHvFg6yPs3LmrL3wou8dD9regzwDPOy++HYfdF/jGysAAAAAyMTBCgAAAAAycbACAAAAgEwcrAAAAAAgEwcrAAAAAMhU1HV97sI7Ozv1ZDK5wHAAAAAA4LFWpN7kGysAAAAAyMTBCgAAAAAycbACAAAAgEwcrAAAAAAgEwcrAAAAAMjEwQoAAAAAMnGwAgAAAIBMa6uucDAYaDweq6oqSVKv11O/35+9L0llWUqShsOhJKndbp+qp6oqtdvtU2V7vZ4kqd/vz7Vn9dr7MSa7xr9u+nw8Hp+qq9vtqqqqWfvG+upj9e1Ymagsy9n7o9Forp39/X1JUqfT0Wg0mutjvDa+XtSvOG6Lfk7118ebur5p3ofDoabTqTqdjsqynJtLP372p++z71O32z01XtGiufTjeFZbqTHx71t5E8v48TsrJ+P4HR4ean19Xbu7u3PjHuOObTTVmWKx+znzc2B55fMrXhvH1EvV0xSXnx/pZK0fHBzM+ihJ0+lUrVZrdp3Vf1YO+vG2+m0MU3MRy6fmuik34rUpi9ajtZfKTy/22/fN+u7ba+pT7POiufLjanX49+N8pNh8+j3f52JKzFU/dz52K7O/vz/LE4sp7jkmxlJVVWOeGZu/vb09Sff3aR+nLxP3H/vMx7xoz0ndR3x9vl/xfur36ajf78/uNa1WKzl3i/LOj7dvK+4ZTes/9jG1H3i29/v6/LhYDlr/fU5KOrXH2ZilYopjZmOVyhX72eJfX19Xq9WaWyP+Gcbvidaf2A+rM17X9CzQFHNTfsXxi7H5eFLitWfNr5d6XvN53LQvxOeA1LNJfA5bJr6mZ4VUnU3Pc9LJ/mv8ujrPc0McKyuTWlup56BFe8yie5qPJ8bZdE+I+7bF5scs9ezm9w7//JJ6vluUS/b53t6eWq2WDg4OkmVM0zN8rL/puSqWHQwGunXrVr+u61ObLN9YAQAAAEAmDlYAAAAAkImDFQAAAABk4mAFAAAAAJk4WAEAAABApqKu63MX3tnZqSeTyQWGAwAAAACPtSL1Jt9YAQAAAEAmDlYAAAAAkImDFQAAAABk4mAFAAAAAJk4WAEAAABAJg5WAAAAAJBpqX9u/Zlnnqmff/55SVK73VZVVcnXklSWpSRpOBzO1WFl2+22yrLUeDyeK+8Nh0NNp1N1Op25spJm7Vmd9vn+/r5ardYsjhiXF9vc29tTq9VSr9ebe+/o6EhlWWo0Gqnb7Z5qw7dtOp3O7PVoNNJgMNB4PFZZlur3+3M/WzuStLu7OxeT77O1U1WVer2e+v2+JM3qsv5Op9PZNa1Wa66+6XQ6iz2O4Wg0StYXx89fZ21ZnX7ebe5S9Xo2JpJOja/lShyDyGLq9XqzMbK4/TUxZ2N++b4t6mOq7OHh4SxGGweba0mzHDk6OtL6+vpsbmL/fH6k5iiOo48nNU9NY2JrM64NqyfOm5+nra0tHR4eanNzcy4+i8f6m+LX/3A4bFyrvpzXVG+qHePn0Na3z1Ff3taI/XlwcDCbQ2s/td/5/SeOSSrvUuX8GA8Gg7n90+ak2+0m10WMz7cV+960Dk2/39fW1tbc+rC8tHHze2CsJ8bgx9Xy0cbFz4G/3sfk88/33/ZcG8vYv3iv8eMWX9sYp/Zou3Y6nero6Eibm5tzbcV9r2m8U/ctu+/EXErtRan7po1JHLumvU7SLE/9WvDxWPxxr/D9j/eS1DNBvN5YDuzv78/uEX5e4z3Vr1E/Tlavv2f7+Wji59Nyx983rf/2vs/b1L7k16zV3/ScEHMg9iuO5aI123QPi3MT8zbu5z7X/fq2GP06jGtF0tw+Lqlxn7JxiHtcvB+knvf8/VXSLHdS9zk/Z03PFk38/S8+C8X3U3tS6r14L4p7v98j4/OBn1//jBrXlOWv30tjv2yc/Jwtera2+Oy5ZXNz88z7tb32OReviX1P7U2p8Yl93d3dnY21v85yZX19XUdHR5I0iyO1r8Z2mvIj5tatW7cO6rp+KZbjGysAAAAAyMTBCgAAAAAycbACAAAAgExL/Y5VURS/Sfrr+MffJD3Z8FqS7h3/uRGqsbK/HZe5Esp7G5LWJP0aysq1Z3Xa509J+tPFEePyYpv/Pr72bnjvieOy30h6MdGGb9v86l5/I+nZ4zL3JP0UfrZ2JOlOiMn32dp58jjGn47ff9aVe1L3x8z8Gepbc7HHMfymoT77XOE9q8/asXmQ5ucuVa9nYyKdHl/LFV82VYfFdFcnY2Rx+2tizsb88n1b1MdU2X+5GG0cbK6lkxx5QvfXkc1N7J/Pj9QcxXH08aTmqWlMNtzPCtel8sHP00vH/f09xGfxWH9T/PrfUPNa9eW8pnpT7Rg/h7a+fY768rZG7M+vdDKH1n5qv/P7j8LnqbxLlfNj/Kzm90+bkxeVXhcxPt+WNN/3pnVoftL9Ofbrw/LSxs3vgbGeGIPcNZaP0vwajNf7mHz++f7bnmtjGfsX7zV+3OJrubJxj7Zr13R//f4e2or7nsWUisdYW3bfibmU2otS900bE+l8e510kqd+Lfh4LP64V/j+x3tJ6pkgXm8sB57SyT3iRZ2eL5tfv0b9OG24+oyfjyZ+Pi131kIZ/77P29S+5Nes1d/0nBBzIPYrjuWiNdt0D4tzE/M27uc+1/36thj9OoxrRZrfx6XmfcrGIe5x8X6Qet7z91fpJHdS9zk/Z03PFk38/S8+C8X3U3tS6r14L4p7v98j4/OBn1//jBrXlOXvr0qL+bRorD3bZy1/zrpf2+u1Bdf4upv2pljGs77e0clY++ssV/7SyRdIFkdqX43tNOVHzK1n6ro+tS6XOlgBAAAAAE7jrwICAAAAQCYOVgAAAACQiYMVAAAAAGTiYAUAAAAAmThYAQAAAEAmDlYAAAAAkImDFQAAAABk4mAFAAAAAJk4WAEAAABApv8DAnPeSar9qScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qVz4p6b7i1xDvlHTRfVSxUq3/iNnMfk697zVcKemREMIfjhM5MAlm9tOSviTp67r8PZvfU+97YP8g6Ycl/aekj4YQ0i9RAlmYWUPS74QQTprZj6j3jtgRSc9J+tUQwtvTjA8Hj5n9hHo/DnOVpG9K+rh6/xBL3sTUmdkpSb+s3i8dPyfp19T7Lg25E1mZ2WclNSQdlXRO0u9L+keV5MriHwz+Qr2PzL4p6eMhhPWhddSZgAEAAAAAdo8f4QAAAACATJiAAQAAAEAmTMAAAAAAIBMmYAAAAACQCRMwAAAAAMiECRgAAAAAZMIEDAAAAAAyYQIGAAAAAJn8PzMuf0CrTddPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Showing 10 random neurons')\n",
    "for i in range(10):\n",
    "    neuron2plot = np.random.randint(0,neural_data.shape[1])\n",
    "    data2plot = neural_data[:,neuron2plot]\n",
    "    figure = plt.figure(figsize=(15,1))\n",
    "    ax = figure.add_subplot(111)\n",
    "    ax.scatter((np.arange(len(data2plot))*dt)[data2plot>0], data2plot[data2plot>0], marker='|',color='k', alpha=.6)\n",
    "    ax.text(1,4.5,f'{neuron2plot}')\n",
    "    ax.set_ylim(0.9,5)\n",
    "    ax.set_xlim(0,100)\n",
    "    ax.get_yaxis().set_ticks([])\n",
    "\n",
    "    if i != 9:\n",
    "        ax.get_xaxis().set_ticks([])\n",
    "    sns.despine()\n",
    "    plt.show()\n",
    "    \n",
    "# Plot velocity underneath\n",
    "figure = plt.figure(figsize=(15,1))\n",
    "ax = figure.add_subplot(111)\n",
    "ax.plot((np.arange(len(vels_binned))*dt), vels_binned[:,0], color='k')\n",
    "ax.plot((np.arange(len(vels_binned))*dt), vels_binned[:,1], color='r')\n",
    "ax.set_xlim(0,100)\n",
    "ax.text(1,45,f'velocity')\n",
    "ax.get_yaxis().set_ticks([])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "data_folder= folder #FOLDER YOU WANT TO SAVE THE DATA TO\n",
    "\n",
    "with open(data_folder+'example_data_s1.pickle','wb') as f:\n",
    "    pickle.dump([neural_data,vels_binned],f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}