imaging-traces.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *svoboda lab / tactile navigation*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup evnironment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.io as sio\n",
    "import json\n",
    "import requests\n",
    "import re\n",
    "from scipy.stats import f_oneway\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "from helper.plots import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from colorsys import hsv_to_rgb, rgb_to_hsv\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_context('paper', font_scale=2.0)\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "from IPython.html import widgets\n",
    "from IPython.display import display\n",
    "from IPython.html.widgets import interactive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 'https://s3.amazonaws.com/neuro.datasets/svoboda.lab/tactile.coding/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iSession = 13\n",
    "session = '%02g' % iSession"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = requests.get(path + session + '/info.json').content\n",
    "info = json.loads(r)\n",
    "info['contributors'] = ', '.join(info['contributors'])\n",
    "info['dimensions'] = str(info['dimensions'])\n",
    "info = pd.DataFrame(info, index = [str(info['id'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "covariates = pd.DataFrame.from_csv(path + session + '/params/covariates.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize trial information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trialData = covariates[['trialType','trialNum']].groupby(['trialNum'], sort=True).first()\n",
    "trialData = trialData.join(covariates[['speed','trialNum','corPos','wallDistOL']].groupby(['trialNum'], sort=True).mean())\n",
    "trialData.sort('trialType', inplace = True, ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "speedThresh = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Load timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timeseries = pd.DataFrame.from_csv(path + session + '/sources/timeseries.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summarize units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "units = pd.DataFrame(timeseries.quantile(q=0.9, axis=1),columns=['activity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform regression on timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getTuningImaging(sourceId,keepInd,name,scale):\n",
    "    tmp = covariates\n",
    "    tmp['dff'] = timeseries.loc[sourceId].values\n",
    "    tmp = tmp[keepInd]\n",
    "    tmp = tmp[~tmp['itiPeriod'].values.astype(bool)]\n",
    "    tmp[name] = (tmp[name]/scale).round()*scale\n",
    "    df = tmp[['trialNum',name,'dff']]    \n",
    "    tuning, p = getTuning(df,'trialNum',name,'dff',sourceId)\n",
    "    return tuning, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getTuning(df,T,X,Y,sourceId):\n",
    "    df = df.groupby([T,X]).mean()\n",
    "    df = df.reset_index().set_index(T)\n",
    "    g = [df[df[X] == x][Y].values for x in df[X].unique()]\n",
    "    f, p = f_oneway(*g)\n",
    "    s = df.groupby([X])\n",
    "    d = {'meanRate':s.mean()[Y].values,'stdRate':s.std()[Y].values,'numTrials':s.count().values[:,0]}\n",
    "    tuning = pd.DataFrame(d).T\n",
    "    tuning.columns = s.mean().index.values\n",
    "    tuning['sourceId'] = sourceId\n",
    "    tuning.reset_index(level=0, inplace=True)\n",
    "    tuning.set_index('sourceId', inplace=True)\n",
    "    tuning.rename(columns={'index':'label'}, inplace=True)\n",
    "    tuning.index.name = None\n",
    "    return tuning, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create wall distance tuning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pattern = re.compile('OL_.*R|OL_out')\n",
    "indexOL = trialData.trialType.str.match(pattern, as_indexer=True)\n",
    "indexRun = trialData.speed > speedThresh\n",
    "trials = trialData[indexOL & indexRun]\n",
    "keepInd = [x in trials.index for x in covariates.trialNum.values]\n",
    "tuning = [getTuningImaging(x,keepInd,'corPos',3) for x in range(len(timeseries))]\n",
    "units['pVal'] = [x[1] for x in tuning]\n",
    "tuning = pd.concat([x[0] for x in tuning])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create running speed tuning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pattern = re.compile('OL_.*R|OL_out')\n",
    "indexOL = trialData.trialType.str.match(pattern, as_indexer=True)\n",
    "indexFar = trialData.wallDistOL  > 20\n",
    "trials = trialData[indexOL & indexFar]\n",
    "keepInd = [x in trials.index for x in covariates.trialNum.values]\n",
    "tuningSpeed = [getTuningImaging(x,keepInd,'speed',5) for x in range(len(timeseries))]\n",
    "units['pVal-speed'] = [x[1] for x in tuningSpeed]\n",
    "tuningSpeed = pd.concat([x[0] for x in tuningSpeed])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plotTimeseries(x, start, stop, c, ylabel=None):\n",
    "    plt.plot((covariates.index[start:stop] - covariates.index[start])/info.iloc[0]['rate-hz'], x[start:stop], c=c);\n",
    "    plt.xlabel('Time (s)')\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define time interval, time interval used in manuscript 2664, 3104"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start = 2664\n",
    "stop = 3104"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot wall distance trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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HSkgSU+Fz4ugqzAULBS6RoStK4Lof+IqZfYRux4+Y2T8BZwLzCtg2KYAkFh/n5LoKg6D7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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1105a6050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotTimeseries(covariates.corPos, start, stop, c='k', ylabel='Wall distance (mm)')\n",
    "plt.xlim([0, 60])\n",
    "plt.ylim([0, 30])\n",
    "plt.xticks(range(0,80,20))\n",
    "plt.yticks(range(0,40,10))\n",
    "plt.gca().invert_yaxis()\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sources used in the paper are 150, 159, and 160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sourcesList = units.index.values\n",
    "sources = widgets.Select(options = [str(x) for x in sourcesList])\n",
    "wS = interactive(lambda source:source, source=sources)\n",
    "display(wS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvfQSXq+X2bNnux5XWVlJ375967yun/n+N23aZJ9cwZryTMBWSpGenh60aS/Y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\nKrZt20ZGRoZr4EZiYqL9nk8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2a24JCQmMGzeODz/8MOIJtOGceOKJpKWlkZyczKhRo+zR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daaec10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = int(wS.kwargs['source'])\n",
    "plotTimeseries(timeseries.loc[u], start, stop, c='k', ylabel='Activity (Dff)')\n",
    "plt.xlim([0, 60])\n",
    "plt.xticks(range(0,80,20))\n",
    "plt.yticks(range(0,10,4))\n",
    "plt.ylim([-1, 9])\n",
    "sns.despine()"
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}