scaredyrat-extRet.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# scaredyrat-ext analyzes data from extinction protocol"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "# notebook formatting and loading settings, imports - keep same\n",
    "\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "import time\n",
    "import os, sys\n",
    "\n",
    "sys.path.append('src')\n",
    "import scaredyrattools as sr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change variables in this cell:"
   ]
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   "outputs": [],
   "source": [
    "inpath = '/Users/liannj/Documents/Anna/eCBData/Week10/4ExtR'\n",
    "\n",
    "## Make sure the folder exists already.\n",
    "## The file does not need to exist; it will be created.\n",
    "outpath = '/Users/liannj/Documents/Anna/eCBData/OutputWeek10'\n",
    "\n",
    "ntones = 5\n",
    "\n",
    "filelist = ['Raw data-eCB Fear Conditioning Spring 2018-Trial   163.xlsx',\n",
    "           'Raw data-eCB Fear Conditioning Spring 2018-Trial   164.xlsx',\n",
    "           'Raw data-eCB Fear Conditioning Spring 2018-Trial   165.xlsx',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "## don't need to change this - stays constant across files\n",
    "sheetlist = ['Track-Arena 1-Subject 1', 'Track-Arena 2-Subject 1', \n",
    "             'Track-Arena 3-Subject 1', 'Track-Arena 4-Subject 1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function calls below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true,
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   "source": [
    "for file in filelist:\n",
    "    for sheet in sheetlist:\n",
    "\n",
    "        ## set number of tones and input/output info\n",
    "        ID,ctx,anim = sr.animal_read(inpath,file,sheet)\n",
    "\n",
    "        ## use scaredyrattools function to find where each epoch is\n",
    "        tones = sr.find_tones(anim, ntones)\n",
    "        pretones = sr.find_pretones(anim, ntones)\n",
    "        shocks = sr.find_shock_responses(anim, ntones)\n",
    "        pshocks = sr.find_postshocks(anim, ntones)\n",
    "\n",
    "        ## use scaredyrattools function to find top n velocities\n",
    "        mtone = sr.get_top_vels(tones,10,ntones)\n",
    "        mptone = sr.get_top_vels(pretones,10,ntones)\n",
    "        mshock = sr.get_top_vels(shocks,5,ntones)\n",
    "        mpshock = sr.get_top_vels(pshocks,10,ntones)\n",
    "\n",
    "        ## define names of CSV files to save to\n",
    "        mToneOutfile = outpath + '/extRet-tone-max-vels-{}.csv' \n",
    "        mToneOutfile = mToneOutfile.format(ID)\n",
    "\n",
    "        mPToneOutfile = outpath + '/extRet-pretone-max-vels-{}.csv' \n",
    "        mPToneOutfile = mPToneOutfile.format(ID)\n",
    "\n",
    "        mShockOutfile = outpath + '/extRet-shock-max-vels-{}.csv' \n",
    "        mShockOutfile = mShockOutfile.format(ID)\n",
    "\n",
    "        mPShockOutfile = outpath + '/extRet-postshock-max-vels-{}.csv' \n",
    "        mPShockOutfile = mPShockOutfile.format(ID)\n",
    "\n",
    "        ## send files to previously defined CSVs\n",
    "        mtone.to_csv(mToneOutfile)\n",
    "        mptone.to_csv(mPToneOutfile)\n",
    "        mshock.to_csv(mShockOutfile)\n",
    "        mpshock.to_csv(mPShockOutfile)\n",
    "\n",
    "        ## use scaredyrattools function to find means\n",
    "        tmeans = sr.get_means(tones,'Tone',ntones)\n",
    "        ptmeans = sr.get_means(pretones,'Pre-tone',ntones)\n",
    "        smeans = sr.get_means(shocks,'Shock',ntones)\n",
    "        pshockmeans = sr.get_means(pshocks,'Post-shock',ntones)\n",
    "        allmeans = pd.concat([tmeans, ptmeans, smeans, pshockmeans],axis=1)\n",
    "\n",
    "        ## use scaredyrattools function to find SEMs\n",
    "        tSEMs = sr.get_SEMs(tones,'Tone',ntones)\n",
    "        ptSEMs = sr.get_SEMs(pretones,'Pre-tone',ntones)\n",
    "        sSEMs = sr.get_SEMs(shocks,'Shock',ntones)\n",
    "        pshockSEMs = sr.get_SEMs(pshocks,'Post-shock',ntones)\n",
    "        allSEMs = pd.concat([tSEMs, ptSEMs, sSEMs, pshockSEMs],axis=1)\n",
    "\n",
    "        ## use scaredyrattools function to find medians\n",
    "        tmeds = sr.get_meds(tones,'Tone',ntones)\n",
    "        ptmeds = sr.get_meds(pretones,'Pre-tone',ntones)\n",
    "        smeds = sr.get_meds(shocks,'Shock',ntones)\n",
    "        pshockmeds = sr.get_meds(pshocks,'Post-shock',ntones)\n",
    "        allmeds = pd.concat([tmeds, ptmeds, smeds, pshockmeds],axis=1)\n",
    "\n",
    "        ## define names of CSV files to save to\n",
    "        meanOutfile = outpath + '/extRet-mean-{}.csv' \n",
    "        meanOutfile = meanOutfile.format(ID)\n",
    "\n",
    "        SEMOutfile = outpath + '/extRet-SEM-{}.csv' \n",
    "        SEMOutfile = SEMOutfile.format(ID)\n",
    "\n",
    "        medOutfile = outpath + '/extRet-med-{}.csv' \n",
    "        medOutfile = medOutfile.format(ID)\n",
    "\n",
    "        allmeans.to_csv(meanOutfile)\n",
    "        allSEMs.to_csv(SEMOutfile)\n",
    "        allmeds.to_csv(medOutfile)\n",
    "        \n",
    "        ## sr tools to find freezing\n",
    "        toneFreezing, FTs = sr.get_freezing(tones,ntones,freezingThreshold=0.1, binSecs=1)\n",
    "        ptFreezing, ptFTs = sr.get_freezing(pretones,ntones,freezingThreshold=0.1, binSecs=1)\n",
    "        shockFreezing, shockFTs = sr.get_freezing(shocks,ntones,freezingThreshold=0.1, binSecs=1)\n",
    "        pshockFreezing, pshockFTs = sr.get_freezing(pshocks,ntones,freezingThreshold=0.1, binSecs=1)\n",
    "\n",
    "        allFreezing = pd.concat([toneFreezing, ptFreezing, shockFreezing, pshockFreezing],axis=1)\n",
    "        \n",
    "        ## exporting\n",
    "        freezingOutfile = outpath + '/extRet-freezing-{}.csv'\n",
    "        freezingOutfile = freezingOutfile.format(ID)\n",
    "        \n",
    "        allFreezing.to_csv(freezingOutfile)\n",
    "        \n",
    "        ## plot stuff\n",
    "        vels = pd.DataFrame(anim['Velocity'])\n",
    "\n",
    "        plt.style.use('seaborn-white')\n",
    "        plt.figure(figsize=(16,8),facecolor='white',edgecolor='white')\n",
    "\n",
    "        # Plots main velocity in black\n",
    "        line1, = plt.plot(vels,color='k',linewidth=0.1,label='ITI')\n",
    "\n",
    "        # Loops through tones, plots each one in cyan\n",
    "        i = 1\n",
    "        while i <= ntones:\n",
    "            tone = sr.find_tone_vels(anim,i)\n",
    "            line2, = plt.plot(tone,color='c',linewidth=0.5,label='Tone')\n",
    "            i += 1\n",
    "        \n",
    "        for timebin in FTs:\n",
    "            plt.plot([timebin[0],timebin[1]],[-0.3,-0.3],color='#ff4f38',linewidth=3)\n",
    "\n",
    "        plt.ylim(-0.5,35)\n",
    "        \n",
    "        sns.despine(left=True, bottom=True, right=True)\n",
    "        plt.title(ID + \" Extinction Retrieval\")\n",
    "        plt.legend(handles=[line1,line2])\n",
    "        plt.ylabel('Velocity (cm/s)')\n",
    "        plt.xlabel('Trial time (s)')\n",
    "\n",
    "        ## define where to save the fig\n",
    "        fname = outpath + '/extRet-plot-{}'\n",
    "        fname = fname.format(ID)\n",
    "\n",
    "        plt.savefig(fname, dpi=300)\n",
    "\n",
    "        plt.show()\n",
    "        plt.close()"
   ]
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   "cell_type": "code",
   "execution_count": null,
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