{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 08 - MedPC behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals\n",
    "\n",
    "- Implement a behavioral analysis for a Pavlovian task measured in epochs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the duration in food cup for rewarded and unrewarded trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is from a Pavlovian biconditional discrimination experiment where two lights (steady, flashing)\n",
    "and two sounds (tone, white noise) were presented for 10 seconds at a time. \n",
    "A trial consisted of one light presentation followed by a 5 second gap, then one sound presentation.\n",
    "There were four different trial types (AX-, AY+, BX+, BY-), \n",
    "such that each individual cue is equally presented with and without food reward.\n",
    "A successful discrimination in this task is when AY+ and BX+ trials have a greater response \n",
    "than AX- and BY- trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:01.428429Z",
     "start_time": "2017-07-26T19:10:00.350600Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "%matplotlib inline\n",
    "import os\n",
    "import numpy as np\n",
    "import nept\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Define where your data folder is located\n",
    "data_path = os.path.join(os.path.abspath('.'), 'data')\n",
    "data_folder = os.path.join(data_path, 'biconditional-tutorial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:03.097508Z",
     "start_time": "2017-07-26T19:10:03.093021Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's look at Rat 5\n",
    "rats = ['5']\n",
    "group1 = ['1', '3', '5', '7']\n",
    "group2 = ['2', '4', '6', '8']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:04.170934Z",
     "start_time": "2017-07-26T19:10:04.156942Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the sessions. MedPC data is stored in a file that is is in the format !YYYY-MM-DD.\n",
    "# The a and b indicate morning and afternoon sessions.\n",
    "sessions = []\n",
    "for file in sorted(os.listdir(data_folder)):\n",
    "    if file[0] == '!':\n",
    "        sessions.append(os.path.join(data_folder, file))\n",
    "\n",
    "# Find out the number of sessions\n",
    "n_sessions = len(sessions)\n",
    "print('Number of sessions:', n_sessions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:06.663496Z",
     "start_time": "2017-07-26T19:10:06.613461Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write a function that dictates the maping between the medpc values and the experimental conditions\n",
    "def assign_medpc_label(data):\n",
    "    \"\"\"Assigns events to proper labels.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data: dict\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    rats_data: dict\n",
    "        With mags, pellets, lights1, lights2, sounds1, sounds2, trial1, trial2, trial3, trial4 as keys.\n",
    "        Each contains nept.Epoch objects\n",
    "\n",
    "    \"\"\"\n",
    "    mag_start = np.array(data[1])\n",
    "    mag_end = np.array(data[2])\n",
    "    if len(mag_start) > len(mag_end):\n",
    "        mag_start = np.array(data[1][:-1])\n",
    "    pel_start = np.array(data[3])\n",
    "    pel_end = pel_start + 1\n",
    "    light1_start = np.array(data[4])\n",
    "    light1_end = np.array(data[5])\n",
    "    light2_start = np.array(data[6])\n",
    "    light2_end = np.array(data[7])\n",
    "    sound1_start = np.array(data[8])\n",
    "    sound1_end = np.array(data[9])\n",
    "    sound2_start = np.array(data[10])\n",
    "    sound2_end = np.array(data[11])\n",
    "\n",
    "    rats_data = dict()\n",
    "    rats_data['mags'] = nept.Epoch(mag_start, mag_end-mag_start)\n",
    "    rats_data['pellets'] = nept.Epoch(pel_start, pel_end-pel_start)\n",
    "    rats_data['lights1'] = nept.Epoch(light1_start, light1_end-light1_start)\n",
    "    rats_data['lights2'] = nept.Epoch(light2_start, light2_end-light2_start)\n",
    "    rats_data['sounds1'] = nept.Epoch(sound1_start, sound1_end-sound1_start)\n",
    "    rats_data['sounds2'] = nept.Epoch(sound2_start, sound2_end-sound2_start)\n",
    "\n",
    "    return rats_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:12.384875Z",
     "start_time": "2017-07-26T19:10:12.380353Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up the data in a dictionary, which is most useful when handling multiple rats\n",
    "data = dict()\n",
    "for rat in rats:\n",
    "    data[rat] = nept.Rat(rat, group1, group2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:30.734247Z",
     "start_time": "2017-07-26T19:10:14.005332Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Gather the data from all the sessions and add them to the dictionary\n",
    "for session in sessions:\n",
    "    rats_data = nept.load_medpc(os.path.join(data_folder, session), assign_medpc_label)\n",
    "\n",
    "    for rat in rats:\n",
    "        data[rat].add_session_medpc(**rats_data[rat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:30.779278Z",
     "start_time": "2017-07-26T19:10:30.735748Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make a dataframe from the dictionary\n",
    "df = nept.combine_rats(data, rats, n_sessions, only_sound=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:10:30.797291Z",
     "start_time": "2017-07-26T19:10:30.780780Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Take a look at the first few entries of the dataframe. \n",
    "# Notice we're computing more than only the food cup duration here.\n",
    "# We can filter it to only the values of interest in our plotting.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:14:06.084070Z",
     "start_time": "2017-07-26T19:14:06.020525Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plot the duration in food cup behavior\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "def plot_measure(measure):\n",
    "    \"\"\"Plots the measure overtime for one rat.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    measure: str\n",
    "        Must be 'durations', 'numbers', 'latency', or 'responses'.\n",
    "    \n",
    "    \"\"\"\n",
    "    # Filter the dataframe by rat\n",
    "    rat_idx = np.zeros(len(df), dtype=bool)\n",
    "\n",
    "    for rat in rats:\n",
    "        rat_idx = rat_idx | (df['rat'] == rat)\n",
    "    rats_df = df[rat_idx]\n",
    "\n",
    "    # Filter the dataframe by measure, here let's look at the duration of food cup entries\n",
    "    duration = df.loc[df.measure == measure]\n",
    "\n",
    "    # Plot using seaborn since seaborn and pandas (the dataframe) work well together\n",
    "    colours = [\"#9970ab\", \"#d6604d\", \"#1b7837\", \"#2166ac\"]\n",
    "    ax = sns.tsplot(data=duration, time=\"session\", unit=\"trial\", condition=\"rewarded\", value=\"value\",\n",
    "                    err_style=\"ci_band\", ci=68, color=colours)\n",
    "\n",
    "    # Set plotting parameters\n",
    "    legend_dist = 1.\n",
    "\n",
    "    if measure == 'durations':\n",
    "        ax.set(xlabel='Session', ylabel='Duration in food cup (s)')\n",
    "    elif measure == 'numbers':\n",
    "        ax.set(xlabel='Session', ylabel='Number of food cup entries')\n",
    "    elif measure == 'latency':\n",
    "        ax.set(xlabel='Session', ylabel='Latency to first entry (s)')\n",
    "    elif measure == 'responses':\n",
    "        ax.set(xlabel='Session', ylabel='Percent responses')\n",
    "\n",
    "    sns.despine()\n",
    "    plt.tight_layout()\n",
    "    plt.legend(bbox_to_anchor=(legend_dist, 1.))\n",
    "    plt.title('Rat 5 duration in food cup by outcome')\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:14:07.318916Z",
     "start_time": "2017-07-26T19:14:06.428284Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_measure('durations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-26T19:14:08.148522Z",
     "start_time": "2017-07-26T19:14:07.320917Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_measure('numbers')"
   ]
  }
 ],
 "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": "3.4.5"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "82px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}