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| { | ||
| "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": 2, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "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 preprocessing_funcs import bin_spikes\n", | ||
| "from preprocessing_funcs import bin_output" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Load Data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "###Load Data###\n", | ||
| "folder='/Users/jig289/Dropbox/MATLAB/Projects/In_Progress/BMI/Processed_Data/' #ENTER THE FOLDER THAT YOUR DATA IS IN\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": 3, | ||
| "metadata": { | ||
| "collapsed": 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": null, | ||
| "metadata": { | ||
| "collapsed": true | ||
| }, | ||
| "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": 4, | ||
| "metadata": { | ||
| "collapsed": 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": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Save Data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pickle\n", | ||
| "\n", | ||
| "with open('s1_test_data_p3.pickle','wb') as f:\n", | ||
| " pickle.dump([neural_data,vels_binned,pos_binned,acc_binned],f)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 2", | ||
| "language": "python", | ||
| "name": "python2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.11" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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