DynamicBrain/solutions/Visual_Behavior_Neuropixels_SWDB_2022_Tutorial_Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../../resources/cropped-SummerWorkshop_Header.png\">  \n",
    "\n",
    "<h1 align=\"center\">Visual Behavior Neuropixels Workshop SWDB 2022 </h1> \n",
    "<h3 align=\"center\">Tuesday, August 23rd, 2022</h3> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<p>This notebook will introduce you to the Visual Behavior Neuropixels dataset and associated AllenSDK functions. \n",
    "    \n",
    "<p> In the following sections, you will learn how to explore the Visual Behavior Neuropixels dataset to find a session of interest, load that session, and extract neurophysiological and behavioral data using the AllenSDK. Once you're familiar with the data, you will use it to answer two scientific questions: \n",
    "    <h3>How does image novelty impact:</h3>\n",
    "<p> &emsp;1) Visual responses in VISp?  \n",
    "<p> &emsp;2) Mouse behavior during the change detection task?\n",
    "    \n",
    "<p> This tutorial will be split into two parts. In the first part, we'll learn the basics about the dimensions of the dataset and how to access session data. In the second part, we'll focus on answering our scientific questions.\n",
    "<h3> Session 1: Accessing/Understanding the data </h3>\n",
    "<p> &emsp;1) <a href='#Accessing-the-Dataset'>Accessing the Dataset</a>\n",
    "<p> &emsp;2) <a href='#Filtering-the-sessions-table-for-a-session-of-interest'>Filtering the sessions table for a session of interest</a>\n",
    "<p> &emsp;3) <a href='#Understanding-the-session-object'>Understanding the session object</a>\n",
    "<p> &emsp;4) <a href='#Exploring-the-Stimulus-Presentations-Table'>Exploring the Stimulus Presentations Table</a>\n",
    "<p> &emsp;5) <a href='#Exploring-the-Units-and-Channels-Tables'>Exploring the Units and Channels Tables</a> \n",
    "<p> &emsp;6) <a href='#Getting-Spike-Times'>Getting Spike Times</a>\n",
    "<p> &emsp;7) <a href='#Understanding-the-Trials-Table'>Understanding the Trials Table</a>\n",
    "<p> &emsp;8) <a href='#Putting-it-all-together-to-make-a-change-triggered-PSTH-for-hit-trials'>Putting it all together to make a change-triggered PSTH for hit trials</a>\n",
    "    \n",
    "    \n",
    "<h3> Session 2: Exploring the impact of novelty on behavior and cortical activity</h3>\n",
    "<p> &emsp;1) <a href='#Getting-reward,-pupil,-running-and-licking-data'>Getting reward, pupil, running and licking data</a>\n",
    "<p> &emsp;2) <a href='#Plotting-visual-responses-to-novel-and-familiar-images'>Plotting visual responses to novel and familiar images</a>\n",
    "<p> &emsp;3) <a href='#Calculating-hit-rates-for-novel-and-familiar-images'>Calculating hit rates for novel and familiar images</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec #Useful tool to arrange multiple plots in one figure (https://matplotlib.org/stable/api/_as_gen/matplotlib.gridspec.GridSpec.html)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3> Accessing the Dataset </h3>\n",
    "    \n",
    "<p>To answer our questions, we first need to access the data. The main entry point to the VBN dataset is the <code>VisualBehaviorNeuropixelsProjectCache</code> class.  This class is responsible for downloading any requested data or metadata as needed and storing it in well known locations.  For this workshop, all of the data has been preloaded onto the hard drives you have received, and is available on AWS.\n",
    "\n",
    "<p>We begin by specifying our data cache directory. Then we'll import the <code>VisualBehaviorNeuropixelsProjectCache</code>  class and instantiate it.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import platform\n",
    "platstring = platform.platform()\n",
    "\n",
    "data_dirname = 'visual-behavior-neuropixels'\n",
    "use_static = False\n",
    "if 'Darwin' in platstring or 'macOS' in platstring:\n",
    "    # macOS \n",
    "    data_root = \"/Volumes/Brain2022/\"\n",
    "elif 'Windows'  in platstring:\n",
    "    # Windows (replace with the drive letter of USB drive)\n",
    "    data_root = \"E:/\"\n",
    "elif ('amzn' in platstring):\n",
    "    # then on AWS\n",
    "    data_root = \"/data/\"\n",
    "    data_dirname = 'visual-behavior-neuropixels-data'\n",
    "    use_static = True\n",
    "else:\n",
    "    # then your own linux platform\n",
    "    # EDIT location where you mounted hard drive\n",
    "    data_root = \"/media/$USERNAME/Brain2022/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/allensdk_38/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from allensdk.brain_observatory.behavior.behavior_project_cache.\\\n",
    "    behavior_neuropixels_project_cache \\\n",
    "    import VisualBehaviorNeuropixelsProjectCache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this path should point to the location of the dataset on your platform\n",
    "cache_dir = os.path.join(data_root, data_dirname)\n",
    "\n",
    "cache = VisualBehaviorNeuropixelsProjectCache.from_local_cache(\n",
    "            cache_dir=cache_dir, use_static_cache=use_static)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">    \n",
    "    <h3>Filtering the sessions table for a session of interest</h3>\n",
    "    \n",
    "<p>The <code>ecephys_sessions_table</code> contains metadata for every <b>Neuropixels recording session</b> in the dataset. This will be the main metadata table we'll use to filter sessions for analysis. Let's look at what's in it (note: you can find a short description of every column in the cheat sheet):\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>behavior_session_id</th>\n",
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       "      <th>session_type</th>\n",
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       "      <th>1052342277</th>\n",
       "      <td>1052374521</td>\n",
       "      <td>2020-09-23 15:34:18.179</td>\n",
       "      <td>NP.1</td>\n",
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       "      <td>M</td>\n",
       "      <td>NeuropixelVisualBehavior</td>\n",
       "      <td>148</td>\n",
       "      <td>1696.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>180</td>\n",
       "      <td>1922.0</td>\n",
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       "      <td>2304.0</td>\n",
       "      <td>['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...</td>\n",
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       "      <td>2020-09-24 15:12:13.229</td>\n",
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       "      <td>530862</td>\n",
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       "      <td>NP.0</td>\n",
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      "text/plain": [
       "                    behavior_session_id      date_of_acquisition  \\\n",
       "ecephys_session_id                                                 \n",
       "1052342277                   1052374521  2020-09-23 15:34:18.179   \n",
       "1051155866                   1052162536  2020-09-17 15:05:39.665   \n",
       "1052533639                   1052572359  2020-09-24 15:12:13.229   \n",
       "1053925378                   1053960984  2020-10-01 16:07:18.990   \n",
       "1053941483                   1053960987  2020-10-01 17:03:58.362   \n",
       "\n",
       "                   equipment_name                 session_type  mouse_id  \\\n",
       "ecephys_session_id                                                         \n",
       "1052342277                   NP.1  EPHYS_1_images_G_3uL_reward    530862   \n",
       "1051155866                   NP.1  EPHYS_1_images_H_3uL_reward    524760   \n",
       "1052533639                   NP.1  EPHYS_1_images_H_3uL_reward    530862   \n",
       "1053925378                   NP.0  EPHYS_1_images_H_3uL_reward    532246   \n",
       "1053941483                   NP.1  EPHYS_1_images_H_3uL_reward    527749   \n",
       "\n",
       "                                                         genotype sex  \\\n",
       "ecephys_session_id                                                      \n",
       "1052342277          Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "1051155866                                                  wt/wt   F   \n",
       "1052533639          Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "1053925378          Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "1053941483          Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "\n",
       "                                project_code  age_in_days  unit_count  ...  \\\n",
       "ecephys_session_id                                                     ...   \n",
       "1052342277          NeuropixelVisualBehavior          148      1696.0  ...   \n",
       "1051155866          NeuropixelVisualBehavior          180      1922.0  ...   \n",
       "1052533639          NeuropixelVisualBehavior          149      1677.0  ...   \n",
       "1053925378          NeuropixelVisualBehavior          145      1823.0  ...   \n",
       "1053941483          NeuropixelVisualBehavior          180      1543.0  ...   \n",
       "\n",
       "                    channel_count  \\\n",
       "ecephys_session_id                  \n",
       "1052342277                 2304.0   \n",
       "1051155866                 2304.0   \n",
       "1052533639                 2304.0   \n",
       "1053925378                 2304.0   \n",
       "1053941483                 2304.0   \n",
       "\n",
       "                                                   structure_acronyms  \\\n",
       "ecephys_session_id                                                      \n",
       "1052342277          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1051155866          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1052533639          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1053925378          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1053941483          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "\n",
       "                   image_set prior_exposures_to_image_set  session_number  \\\n",
       "ecephys_session_id                                                          \n",
       "1052342277                 G                         32.0               1   \n",
       "1051155866                 H                          0.0               2   \n",
       "1052533639                 H                          0.0               2   \n",
       "1053925378                 H                          0.0               2   \n",
       "1053941483                 H                          0.0               2   \n",
       "\n",
       "                    experience_level prior_exposures_to_omissions  file_id  \\\n",
       "ecephys_session_id                                                           \n",
       "1052342277                  Familiar                          0.0        0   \n",
       "1051155866                     Novel                          1.0        1   \n",
       "1052533639                     Novel                          1.0        4   \n",
       "1053925378                     Novel                          1.0        5   \n",
       "1053941483                     Novel                          1.0        6   \n",
       "\n",
       "                    abnormal_histology abnormal_activity  \n",
       "ecephys_session_id                                        \n",
       "1052342277                         NaN               NaN  \n",
       "1051155866                         NaN               NaN  \n",
       "1052533639                         NaN               NaN  \n",
       "1053925378                         NaN               NaN  \n",
       "1053941483                         NaN               NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecephys_sessions_table = cache.get_ecephys_session_table()\n",
    "ecephys_sessions_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<p>This table gives us lots of useful metadata about each recording session, including when the session took place, the <code>genotype</code>, <code>sex</code> and <code>age</code> of the mouse that was run, what brain areas were recorded and some important info about the stimulus. \n",
    "\n",
    "<p>To demystify a few of these columns, let's briefly review the experimental design. Each mouse was trained with one of two image sets (<code>G</code> or <code>H</code>). The image set the mouse was trained with became highly familiar because each of the images was seen thousands of times. The image set the mouse was not trained with was observed for the first time during a Neuropixels recording session after the mouse had learned the task. \n",
    "    \n",
    "<p>For the majority of mice, we recorded two sessions: one with the trained <code>Familiar</code> image set and one with a <code>Novel</code> image set. Note that two of the eight images were shared across these two image sets as diagrammed below for an example mouse that was trained with image set <code>G</code>. \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/f1/d1/f1d140bc-228a-4328-bdca-e3383a1a46f0/image_sets_and_training_trajectories_diagram_defaultsdk_and_unfiltered.png\", width=\"900\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "We can define each recording session by a few parameters in our sessions table, including the <code>image_set</code> used (<code>G</code> or <code>H</code>), the <code>experience_level</code> of the mouse (indicating whether the mouse had seen the image set in previous training sessions) and the <code>session_number</code> (indicating whether it was the first or second recording day for the mouse). Let's look at how many sessions we have of each type:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 1.1:</b> How many <code>Novel</code> and <code>Familiar</code> sessions are there in the dataset (Hint: use the <code>experience_level</code> column)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience_level\n",
       "Novel       52\n",
       "Familiar    51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecephys_sessions_table.value_counts('experience_level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 1.2:</b>  How many <code>G</code> and <code>H</code> sessions?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image_set\n",
       "H    52\n",
       "G    51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecephys_sessions_table.value_counts('image_set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now let's count how many sessions were <code>Novel</code> or <code>Familiar</code> for each image set. (Hint: we can pass a list of column names to <code>value_counts</code>)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience_level  image_set\n",
       "Novel             H            42\n",
       "Familiar          G            41\n",
       "                  H            10\n",
       "Novel             G            10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecephys_sessions_table.value_counts(['experience_level', 'image_set'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 1.3:</b>  Use the <code>genotype</code> column to print how many unique genotypes were used in this dataset. How many sessions from each genotype are there?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt', 'wt/wt',\n",
       "       'Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print unique genotypes\n",
    "ecephys_sessions_table.genotype.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genotype\n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt    43\n",
       "wt/wt                                            38\n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print how many sessions for each\n",
    "ecephys_sessions_table.value_counts('genotype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>behavior_session_id</th>\n",
       "      <th>date_of_acquisition</th>\n",
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       "      <th>prior_exposures_to_omissions</th>\n",
       "      <th>file_id</th>\n",
       "      <th>abnormal_histology</th>\n",
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       "    <tr>\n",
       "      <th>genotype</th>\n",
       "      <th></th>\n",
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       "      <th>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</th>\n",
       "      <td>22</td>\n",
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       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt/wt</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                               behavior_session_id  \\\n",
       "genotype                                                             \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                   43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                   22   \n",
       "wt/wt                                                           38   \n",
       "\n",
       "                                               date_of_acquisition  \\\n",
       "genotype                                                             \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                   43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                   22   \n",
       "wt/wt                                                           38   \n",
       "\n",
       "                                               equipment_name  session_type  \\\n",
       "genotype                                                                      \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt              43            43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt              22            22   \n",
       "wt/wt                                                      38            38   \n",
       "\n",
       "                                               mouse_id  sex  project_code  \\\n",
       "genotype                                                                     \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt        43   43            43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt        22   22            22   \n",
       "wt/wt                                                38   38            38   \n",
       "\n",
       "                                               age_in_days  unit_count  \\\n",
       "genotype                                                                 \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt           43          43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt           22          22   \n",
       "wt/wt                                                   38          38   \n",
       "\n",
       "                                               probe_count  channel_count  \\\n",
       "genotype                                                                    \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt           43             43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt           22             22   \n",
       "wt/wt                                                   38             38   \n",
       "\n",
       "                                               structure_acronyms  image_set  \\\n",
       "genotype                                                                       \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                  43         43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                  22         22   \n",
       "wt/wt                                                          38         38   \n",
       "\n",
       "                                               prior_exposures_to_image_set  \\\n",
       "genotype                                                                      \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                            43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                            22   \n",
       "wt/wt                                                                    38   \n",
       "\n",
       "                                               session_number  \\\n",
       "genotype                                                        \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt              43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt              22   \n",
       "wt/wt                                                      38   \n",
       "\n",
       "                                               experience_level  \\\n",
       "genotype                                                          \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                22   \n",
       "wt/wt                                                        38   \n",
       "\n",
       "                                               prior_exposures_to_omissions  \\\n",
       "genotype                                                                      \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                            43   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                            22   \n",
       "wt/wt                                                                    38   \n",
       "\n",
       "                                               file_id  abnormal_histology  \\\n",
       "genotype                                                                     \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt       43                   0   \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt       22                   0   \n",
       "wt/wt                                               38                   0   \n",
       "\n",
       "                                               abnormal_activity  \n",
       "genotype                                                          \n",
       "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                  0  \n",
       "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt                  0  \n",
       "wt/wt                                                          0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note we could also do this by using groupby\n",
    "ecephys_sessions_table.groupby('genotype').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "In addition to different types of image sets, this dataset contains ephys recording sessions from 3 genotypes (C57BL6J, VIP-IRES-CrexAi32 and SST-IRES-CrexAi32). \n",
    "\n",
    "<p>For this tutorial, we need to select a session with:\n",
    "<ul>\n",
    "    <li><code>Sst-IRES-Cre</code> mouse</li>\n",
    "    <li><code>Novel</code> images presented</li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "<p><b>Task 1.4:</b>  Filter the <code>ecephys_sessions_table</code> for novel sessions and store the result in a new table called <code>novel_sessions</code>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "novel_sessions = ecephys_sessions_table[ecephys_sessions_table['experience_level']=='Novel']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now let's filter for novel sessions in Sst mice. (Hint, use both the <code>experience_level</code> and <code>genotype</code> columns). \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>behavior_session_id</th>\n",
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       "      <th>1053941483</th>\n",
       "      <td>1053960987</td>\n",
       "      <td>2020-10-01 17:03:58.362</td>\n",
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       "      <td>M</td>\n",
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       "      <td>180</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2304.0</td>\n",
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       "      <td>NP.1</td>\n",
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       "      <td>544456</td>\n",
       "      <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
       "      <td>M</td>\n",
       "      <td>NeuropixelVisualBehavior</td>\n",
       "      <td>120</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1048189115</th>\n",
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       "      <td>NP.1</td>\n",
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       "      <td>509808</td>\n",
       "      <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
       "      <td>M</td>\n",
       "      <td>NeuropixelVisualBehavior</td>\n",
       "      <td>264</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...</td>\n",
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       "      <td>2</td>\n",
       "      <td>Novel</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048196054</th>\n",
       "      <td>1048222325</td>\n",
       "      <td>2020-09-03 14:25:07.290</td>\n",
       "      <td>NP.0</td>\n",
       "      <td>EPHYS_1_images_H_3uL_reward</td>\n",
       "      <td>524925</td>\n",
       "      <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
       "      <td>F</td>\n",
       "      <td>NeuropixelVisualBehavior</td>\n",
       "      <td>166</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Novel</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065905010</th>\n",
       "      <td>1065929713</td>\n",
       "      <td>2020-11-24 14:21:48.847</td>\n",
       "      <td>NP.0</td>\n",
       "      <td>EPHYS_1_images_H_3uL_reward</td>\n",
       "      <td>544358</td>\n",
       "      <td>Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt</td>\n",
       "      <td>F</td>\n",
       "      <td>NeuropixelVisualBehavior</td>\n",
       "      <td>126</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...</td>\n",
       "      <td>H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Novel</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    behavior_session_id      date_of_acquisition  \\\n",
       "ecephys_session_id                                                 \n",
       "1053941483                   1053960987  2020-10-01 17:03:58.362   \n",
       "1064644573                   1064666428  2020-11-19 15:18:01.372   \n",
       "1048189115                   1048221709  2020-09-03 14:16:57.913   \n",
       "1048196054                   1048222325  2020-09-03 14:25:07.290   \n",
       "1065905010                   1065929713  2020-11-24 14:21:48.847   \n",
       "\n",
       "                   equipment_name                 session_type  mouse_id  \\\n",
       "ecephys_session_id                                                         \n",
       "1053941483                   NP.1  EPHYS_1_images_H_3uL_reward    527749   \n",
       "1064644573                   NP.1  EPHYS_1_images_H_3uL_reward    544456   \n",
       "1048189115                   NP.1  EPHYS_1_images_H_3uL_reward    509808   \n",
       "1048196054                   NP.0  EPHYS_1_images_H_3uL_reward    524925   \n",
       "1065905010                   NP.0  EPHYS_1_images_H_3uL_reward    544358   \n",
       "\n",
       "                                                         genotype sex  \\\n",
       "ecephys_session_id                                                      \n",
       "1053941483          Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "1064644573          Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "1048189115          Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   M   \n",
       "1048196054          Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   F   \n",
       "1065905010          Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt   F   \n",
       "\n",
       "                                project_code  age_in_days  unit_count  ...  \\\n",
       "ecephys_session_id                                                     ...   \n",
       "1053941483          NeuropixelVisualBehavior          180      1543.0  ...   \n",
       "1064644573          NeuropixelVisualBehavior          120      2254.0  ...   \n",
       "1048189115          NeuropixelVisualBehavior          264      1925.0  ...   \n",
       "1048196054          NeuropixelVisualBehavior          166      2288.0  ...   \n",
       "1065905010          NeuropixelVisualBehavior          126      1998.0  ...   \n",
       "\n",
       "                    channel_count  \\\n",
       "ecephys_session_id                  \n",
       "1053941483                 2304.0   \n",
       "1064644573                 2304.0   \n",
       "1048189115                 2304.0   \n",
       "1048196054                 2304.0   \n",
       "1065905010                 2304.0   \n",
       "\n",
       "                                                   structure_acronyms  \\\n",
       "ecephys_session_id                                                      \n",
       "1053941483          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1064644573          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1048189115          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1048196054          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "1065905010          ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...   \n",
       "\n",
       "                   image_set prior_exposures_to_image_set  session_number  \\\n",
       "ecephys_session_id                                                          \n",
       "1053941483                 H                          0.0               2   \n",
       "1064644573                 H                          0.0               2   \n",
       "1048189115                 H                          0.0               2   \n",
       "1048196054                 H                          0.0               2   \n",
       "1065905010                 H                          0.0               2   \n",
       "\n",
       "                    experience_level prior_exposures_to_omissions  file_id  \\\n",
       "ecephys_session_id                                                           \n",
       "1053941483                     Novel                          1.0        6   \n",
       "1064644573                     Novel                          1.0       27   \n",
       "1048189115                     Novel                          1.0       37   \n",
       "1048196054                     Novel                          1.0       38   \n",
       "1065905010                     Novel                          1.0       39   \n",
       "\n",
       "                    abnormal_histology abnormal_activity  \n",
       "ecephys_session_id                                        \n",
       "1053941483                         NaN               NaN  \n",
       "1064644573                         NaN               NaN  \n",
       "1048189115                         NaN               NaN  \n",
       "1048196054                         NaN               NaN  \n",
       "1065905010                         NaN               NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_novel_sessions = ecephys_sessions_table[(ecephys_sessions_table['genotype'].str.contains('Sst')) & \n",
    "                                            (ecephys_sessions_table['experience_level']=='Novel')]\n",
    "sst_novel_sessions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3>Understanding the session object</h3>\n",
    "<p>All data from a particular recording session is contained in the <code>ecephys_session</code> object. Let's load one of our novel, Sst sessions:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ecephys_session_1053941483.nwb: 100%|███████████████████████████████████████████████████████| 2.27G/2.27G [03:29<00:00, 10.8MMB/s]\n",
      "/opt/anaconda3/envs/allensdk_38/lib/python3.8/site-packages/hdmf/utils.py:577: FutureWarning: DynamicTable.__init__: Using positional arguments for this method is discouraged and will be deprecated in a future major release. Please use keyword arguments to ensure future compatibility.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "session_id = 1053941483\n",
    "session = cache.get_ecephys_session(\n",
    "            ecephys_session_id=session_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/d2/4d/d24d67b2-59ee-4246-b4a4-621877ba9e5f/vbn_session_object_summary.png\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "We can get a high-level summary of this session by accessing its <code>metadata</code> attribute:\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'equipment_name': 'NP.1',\n",
       " 'sex': 'M',\n",
       " 'age_in_days': 180,\n",
       " 'stimulus_frame_rate': 60.0,\n",
       " 'session_type': 'EPHYS_1_images_H_3uL_reward',\n",
       " 'date_of_acquisition': datetime.datetime(2020, 10, 2, 0, 3, 58, tzinfo=tzutc()),\n",
       " 'reporter_line': 'Ai32(RCL-ChR2(H134R)_EYFP)',\n",
       " 'cre_line': 'Sst-IRES-Cre',\n",
       " 'behavior_session_uuid': None,\n",
       " 'driver_line': ['Sst-IRES-Cre'],\n",
       " 'mouse_id': 527749,\n",
       " 'full_genotype': 'Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt',\n",
       " 'behavior_session_id': 1053960987,\n",
       " 'ecephys_session_id': 1053941483}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3>Exploring the Stimulus Presentations Table</h3>\n",
    "\n",
    "The first step to analyzing how the mouse and its neurons responded to various stimuli is to understand which stimuli were presented at what time. For this, we use the <code>stimulus presentations</code> table, which is a dataframe containing info about each stimulus. You can find a description of each column in the cheat sheet.\n",
    "\n",
    "<p>Every recording session consisted of three major visual stimulus epochs in the following order (diagrammed below):\n",
    "<p> &emsp;1) An active behavior session during which the mouse performed the change detection task\n",
    "<p> &emsp;2) Receptive field mapping and full-field flash stimuli\n",
    "<p> &emsp;3) Passive replay of stimulus shown during active behavior, but without the lickspout so the mouse can no longer respond.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/84/46/84463f18-1b45-4f4d-854f-767fac4f7126/experimental_session_diagram.png\", width=\"900\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Let's look at the stimulus presentations table for the session we already loaded\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>active</th>\n",
       "      <th>color</th>\n",
       "      <th>contrast</th>\n",
       "      <th>duration</th>\n",
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       "      <th>is_change</th>\n",
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       "      <th>orientation</th>\n",
       "      <th>...</th>\n",
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       "      <th>stimulus_name</th>\n",
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       "    </tr>\n",
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       "      <td>0.250212</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>25.254420</td>\n",
       "      <td>0</td>\n",
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       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>25.504632</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>26.005057</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>26.255269</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250208</td>\n",
       "      <td>165</td>\n",
       "      <td>2.0</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150</td>\n",
       "      <td>26.755685</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>27.005892</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.250207</td>\n",
       "      <td>210</td>\n",
       "      <td>3.0</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195</td>\n",
       "      <td>27.506307</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>27.756515</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250213</td>\n",
       "      <td>255</td>\n",
       "      <td>4.0</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>28.256929</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>28.507142</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           active  color  contrast  duration  end_frame  \\\n",
       "stimulus_presentations_id                                                 \n",
       "0                            True    NaN       NaN  0.250212         75   \n",
       "1                            True    NaN       NaN  0.250212        120   \n",
       "2                            True    NaN       NaN  0.250208        165   \n",
       "3                            True    NaN       NaN  0.250207        210   \n",
       "4                            True    NaN       NaN  0.250213        255   \n",
       "\n",
       "                           flashes_since_change image_name is_change omitted  \\\n",
       "stimulus_presentations_id                                                      \n",
       "0                                           0.0    im104_r     False   False   \n",
       "1                                           1.0    im104_r     False   False   \n",
       "2                                           2.0    im104_r     False   False   \n",
       "3                                           3.0    im104_r     False   False   \n",
       "4                                           4.0    im104_r     False   False   \n",
       "\n",
       "                           orientation  ...  position_y  rewarded  \\\n",
       "stimulus_presentations_id               ...                         \n",
       "0                                  NaN  ...         NaN     False   \n",
       "1                                  NaN  ...         NaN     False   \n",
       "2                                  NaN  ...         NaN     False   \n",
       "3                                  NaN  ...         NaN     False   \n",
       "4                                  NaN  ...         NaN     False   \n",
       "\n",
       "                          spatial_frequency  start_frame  start_time  \\\n",
       "stimulus_presentations_id                                              \n",
       "0                                       NaN           60   25.254420   \n",
       "1                                       NaN          105   26.005057   \n",
       "2                                       NaN          150   26.755685   \n",
       "3                                       NaN          195   27.506307   \n",
       "4                                       NaN          240   28.256929   \n",
       "\n",
       "                           stimulus_block  stimulus_index  \\\n",
       "stimulus_presentations_id                                   \n",
       "0                                       0             NaN   \n",
       "1                                       0             NaN   \n",
       "2                                       0             NaN   \n",
       "3                                       0             NaN   \n",
       "4                                       0             NaN   \n",
       "\n",
       "                                                         stimulus_name  \\\n",
       "stimulus_presentations_id                                                \n",
       "0                          Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "1                          Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "2                          Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "3                          Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "4                          Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "\n",
       "                           stop_time  temporal_frequency  \n",
       "stimulus_presentations_id                                 \n",
       "0                          25.504632                 NaN  \n",
       "1                          26.255269                 NaN  \n",
       "2                          27.005892                 NaN  \n",
       "3                          27.756515                 NaN  \n",
       "4                          28.507142                 NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimulus_presentations = session.stimulus_presentations\n",
    "stimulus_presentations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "This table is a record of every stimulus we presented to the mouse over the course of this experiment. The different stimuli are indexed by the <code>stimulus_block</code> column. Let's group this dataframe by stimulus block and see what stimulus was shown for each block.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 2.1:</b>  Use the <code>groupby</code> method to group the stimulus_presentations table by <code>stimulus_block</code>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                active  color  contrast  duration  end_frame  \\\n",
       "stimulus_block                                                 \n",
       "0                 4804      0         0      4593       4804   \n",
       "1                    1      0         0         1          1   \n",
       "2                 3645      0      3645      3645       3645   \n",
       "3                    1      0         0         1          1   \n",
       "4                  150    150       150       150        150   \n",
       "5                 4804      0         0      4804       4804   \n",
       "\n",
       "                flashes_since_change  image_name  is_change  omitted  \\\n",
       "stimulus_block                                                         \n",
       "0                               4804        4804       4804     4804   \n",
       "1                                  0           0          0        0   \n",
       "2                                  0           0          0        0   \n",
       "3                                  0           0          0        0   \n",
       "4                                  0           0          0        0   \n",
       "5                               4804        4804       4804     4804   \n",
       "\n",
       "                orientation  ...  rewarded  spatial_frequency  start_frame  \\\n",
       "stimulus_block               ...                                             \n",
       "0                         0  ...      4804                  0         4804   \n",
       "1                         0  ...         0                  0            1   \n",
       "2                      3645  ...         0               3645         3645   \n",
       "3                         0  ...         0                  0            1   \n",
       "4                         0  ...         0                  0          150   \n",
       "5                         0  ...      4804                  0         4804   \n",
       "\n",
       "                start_time  stimulus_index  stimulus_name  stop_time  \\\n",
       "stimulus_block                                                         \n",
       "0                     4804               0           4804       4804   \n",
       "1                        1               0              1          1   \n",
       "2                     3645            3645           3645       3645   \n",
       "3                        1               0              1          1   \n",
       "4                      150             150            150        150   \n",
       "5                     4804               0           4804       4804   \n",
       "\n",
       "                temporal_frequency  image_set  is_novel  \n",
       "stimulus_block                                           \n",
       "0                                0       4804      4593  \n",
       "1                                0          0         0  \n",
       "2                             3645          0         0  \n",
       "3                                0          0         0  \n",
       "4                                0          0         0  \n",
       "5                                0       4804      4593  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimulus_presentations.groupby('stimulus_block').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Let's look at the first row for each block (Hint, you can use <code>.head(1)</code> to get the first row)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "   stimulus_presentations_id  active  color  contrast    duration  end_frame  \\\n",
       "0                          0    True    NaN       NaN    0.250212         75   \n",
       "1                       4804   False    NaN       NaN   10.008400     216914   \n",
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       "3                       8450   False    NaN       NaN  288.991867     288914   \n",
       "4                       8451   False    1.0       0.8    0.250213     288929   \n",
       "5                       8601   False    NaN       NaN    0.250201     307589   \n",
       "\n",
       "   flashes_since_change image_name is_change omitted  ...  position_y  \\\n",
       "0                   0.0    im104_r     False   False  ...         NaN   \n",
       "1                   NaN        NaN       NaN     NaN  ...         NaN   \n",
       "2                   NaN        NaN       NaN     NaN  ...       -40.0   \n",
       "3                   NaN        NaN       NaN     NaN  ...         NaN   \n",
       "4                   NaN        NaN       NaN     NaN  ...         NaN   \n",
       "5                   0.0    im104_r     False   False  ...         NaN   \n",
       "\n",
       "   rewarded  spatial_frequency start_frame   start_time  stimulus_block  \\\n",
       "0     False                NaN          60    25.254420               0   \n",
       "1       NaN                NaN      216314  3660.861634               1   \n",
       "2       NaN               0.08      216914  3670.870034               2   \n",
       "3       NaN                NaN      271589  4582.883348               3   \n",
       "4       NaN                NaN      288914  4871.875214               4   \n",
       "5     False                NaN      307574  5195.429343               5   \n",
       "\n",
       "   stimulus_index                                stimulus_name    stop_time  \\\n",
       "0             NaN  Natural_Images_Lum_Matched_set_ophys_H_2019    25.504632   \n",
       "1             NaN                                  spontaneous  3670.870034   \n",
       "2             0.0                           gabor_20_deg_250ms  3671.120247   \n",
       "3             NaN                                  spontaneous  4871.875214   \n",
       "4             1.0                                  flash_250ms  4872.125427   \n",
       "5             NaN  Natural_Images_Lum_Matched_set_ophys_H_2019  5195.679544   \n",
       "\n",
       "  temporal_frequency  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                4.0  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimulus_presentations.groupby('stimulus_block').head(1).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "This shows us the structure of this experiment (and every experiment in this dataset). There are 5 stimulus blocks as follows:\n",
    "\n",
    "<p><b>block 0</b>: Change detection task. Natural images are flashed repeatedly and the mouse is rewarded for licking when the identity of the image changes. You can find more info about this task <a href=\"http://portal.brain-map.org/explore/circuits/visual-behavior-neuropixels\">here</a>.\n",
    "\n",
    "<p><b>block 1</b>: Brief gray screen (10 seconds)\n",
    "\n",
    "<p><b>block 2</b>: Receptive field mapping using gabor stimuli. For more details on this stimulus consult [this notebook](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_receptive_fields.html).\n",
    "\n",
    "<p><b>block 3</b>: Longer gray screen (5 minutes)\n",
    "\n",
    "<p><b>block 4</b>: Full-field flashes, shown at 80% contrast. Flashes can be black (color = -1) or white (color = 1). This stimulus is useful for calculating response latencies and current source density analysis for layer identification.\n",
    "\n",
    "<p><b>block 5</b>: Passive replay. Frame-for-frame replay of the stimulus shown during the change detection task (block 0), but now with the lick spout retracted so the animal can no longer engage in the task.\n",
    "\n",
    "    \n",
    "<p>For now, we will focus on block 0 and 5, the active behavior and the passive replay of the same images.\n",
    "\n",
    "<p>Let's confirm that the active behavior block (0) and the passive replay block (5) match frame for frame:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 2.2:</b>  How many unique images were shown during this session (Hint: use the <code>image_name</code> column)? \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['im104_r', 'omitted', 'im087_r', 'im111_r', 'im005_r', 'im034_r',\n",
       "       'im083_r', 'im024_r', 'im114_r', nan], dtype=object)"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get unique images\n",
    "stimulus_presentations.image_name.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 2.3:</b> How many image changes were there (Hint: use the <code>is_change</code> column)? What if you limit the count to the active behavior session?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "596"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total image changes\n",
    "stimulus_presentations.is_change.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "298"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# image changes during active behavior\n",
    "stimulus_presentations[stimulus_presentations['active']].is_change.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3>Exploring the Units and Channels Tables</h3>\n",
    "\n",
    "We'll use the units and channels tables to get info about each unit recorded during the session. These tables will help us filter the data for units in a particular brain region or for units that meet specific quality criteria. You can find a description of the columns in these dataframes in our cheat sheet\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Units table</h3>\n",
    "\n",
    "This table contains quality metrics and waveform metrics for every unit recorded during this session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PT_ratio</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>amplitude_cutoff</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>cumulative_drift</th>\n",
       "      <th>d_prime</th>\n",
       "      <th>firing_rate</th>\n",
       "      <th>isi_violations</th>\n",
       "      <th>isolation_distance</th>\n",
       "      <th>l_ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>presence_ratio</th>\n",
       "      <th>quality</th>\n",
       "      <th>recovery_slope</th>\n",
       "      <th>repolarization_slope</th>\n",
       "      <th>silhouette_score</th>\n",
       "      <th>snr</th>\n",
       "      <th>spread</th>\n",
       "      <th>velocity_above</th>\n",
       "      <th>velocity_below</th>\n",
       "      <th>waveform_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1061853767</th>\n",
       "      <td>0.738222</td>\n",
       "      <td>632.394165</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>23</td>\n",
       "      <td>79.63</td>\n",
       "      <td>10.843623</td>\n",
       "      <td>68.687706</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>246.203332</td>\n",
       "      <td>3.104812e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>good</td>\n",
       "      <td>-0.808696</td>\n",
       "      <td>2.795432</td>\n",
       "      <td>0.113092</td>\n",
       "      <td>4.519657</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.686767</td>\n",
       "      <td>0.137353</td>\n",
       "      <td>0.206030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061853941</th>\n",
       "      <td>0.490965</td>\n",
       "      <td>207.990510</td>\n",
       "      <td>0.273518</td>\n",
       "      <td>196</td>\n",
       "      <td>347.89</td>\n",
       "      <td>3.966892</td>\n",
       "      <td>1.747814</td>\n",
       "      <td>0.374140</td>\n",
       "      <td>60.672055</td>\n",
       "      <td>3.726235e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>good</td>\n",
       "      <td>-0.112609</td>\n",
       "      <td>0.618665</td>\n",
       "      <td>0.099188</td>\n",
       "      <td>2.541619</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.480737</td>\n",
       "      <td>0.206030</td>\n",
       "      <td>0.618090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061853761</th>\n",
       "      <td>0.538560</td>\n",
       "      <td>284.645010</td>\n",
       "      <td>0.475406</td>\n",
       "      <td>17</td>\n",
       "      <td>188.14</td>\n",
       "      <td>4.841153</td>\n",
       "      <td>5.819598</td>\n",
       "      <td>0.010472</td>\n",
       "      <td>74.209946</td>\n",
       "      <td>8.290955e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>good</td>\n",
       "      <td>-0.312478</td>\n",
       "      <td>1.265120</td>\n",
       "      <td>0.093662</td>\n",
       "      <td>2.558605</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.343384</td>\n",
       "      <td>-1.098827</td>\n",
       "      <td>0.233501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061853892</th>\n",
       "      <td>2.714716</td>\n",
       "      <td>180.353940</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>147</td>\n",
       "      <td>1289.78</td>\n",
       "      <td>6.065760</td>\n",
       "      <td>2.396936</td>\n",
       "      <td>0.157757</td>\n",
       "      <td>88.232357</td>\n",
       "      <td>5.768766e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>good</td>\n",
       "      <td>-0.066549</td>\n",
       "      <td>0.654593</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>2.419777</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.343384</td>\n",
       "      <td>-0.686767</td>\n",
       "      <td>0.425796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061853891</th>\n",
       "      <td>1.105938</td>\n",
       "      <td>533.637585</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>146</td>\n",
       "      <td>474.71</td>\n",
       "      <td>8.037788</td>\n",
       "      <td>2.563054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>147.532935</td>\n",
       "      <td>7.581718e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>good</td>\n",
       "      <td>-1.241809</td>\n",
       "      <td>1.728842</td>\n",
       "      <td>0.159304</td>\n",
       "      <td>5.942894</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-0.068677</td>\n",
       "      <td>0.686767</td>\n",
       "      <td>0.151089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PT_ratio   amplitude  amplitude_cutoff  cluster_id  \\\n",
       "id                                                               \n",
       "1061853767  0.738222  632.394165          0.000008          23   \n",
       "1061853941  0.490965  207.990510          0.273518         196   \n",
       "1061853761  0.538560  284.645010          0.475406          17   \n",
       "1061853892  2.714716  180.353940          0.500000         147   \n",
       "1061853891  1.105938  533.637585          0.000136         146   \n",
       "\n",
       "            cumulative_drift    d_prime  firing_rate  isi_violations  \\\n",
       "id                                                                     \n",
       "1061853767             79.63  10.843623    68.687706        0.000167   \n",
       "1061853941            347.89   3.966892     1.747814        0.374140   \n",
       "1061853761            188.14   4.841153     5.819598        0.010472   \n",
       "1061853892           1289.78   6.065760     2.396936        0.157757   \n",
       "1061853891            474.71   8.037788     2.563054        0.000000   \n",
       "\n",
       "            isolation_distance       l_ratio  ...  presence_ratio  quality  \\\n",
       "id                                            ...                            \n",
       "1061853767          246.203332  3.104812e-08  ...            0.99     good   \n",
       "1061853941           60.672055  3.726235e-03  ...            0.99     good   \n",
       "1061853761           74.209946  8.290955e-04  ...            0.99     good   \n",
       "1061853892           88.232357  5.768766e-04  ...            0.99     good   \n",
       "1061853891          147.532935  7.581718e-08  ...            0.99     good   \n",
       "\n",
       "            recovery_slope  repolarization_slope  silhouette_score       snr  \\\n",
       "id                                                                             \n",
       "1061853767       -0.808696              2.795432          0.113092  4.519657   \n",
       "1061853941       -0.112609              0.618665          0.099188  2.541619   \n",
       "1061853761       -0.312478              1.265120          0.093662  2.558605   \n",
       "1061853892       -0.066549              0.654593          0.051338  2.419777   \n",
       "1061853891       -1.241809              1.728842          0.159304  5.942894   \n",
       "\n",
       "           spread  velocity_above  velocity_below  waveform_duration  \n",
       "id                                                                    \n",
       "1061853767   60.0        0.686767        0.137353           0.206030  \n",
       "1061853941   70.0        0.480737        0.206030           0.618090  \n",
       "1061853761   60.0        0.343384       -1.098827           0.233501  \n",
       "1061853892   40.0       -0.343384       -0.686767           0.425796  \n",
       "1061853891   50.0       -0.068677        0.686767           0.151089  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units = session.get_units()\n",
    "units.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PT_ratio', 'amplitude', 'amplitude_cutoff', 'cluster_id',\n",
       "       'cumulative_drift', 'd_prime', 'firing_rate', 'isi_violations',\n",
       "       'isolation_distance', 'l_ratio', 'local_index', 'max_drift',\n",
       "       'nn_hit_rate', 'nn_miss_rate', 'peak_channel_id', 'presence_ratio',\n",
       "       'quality', 'recovery_slope', 'repolarization_slope', 'silhouette_score',\n",
       "       'snr', 'spread', 'velocity_above', 'velocity_below',\n",
       "       'waveform_duration'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "For more information about many of the metrics included in this table and how to use them to guide your analysis, see our [quality metrics tutorial](https://allensdk--2471.org.readthedocs.build/en/2471/_static/examples/nb/visual_behavior_neuropixels_quality_metrics.html). For now, here's a brief description of each column:\n",
    "\n",
    "<p><b>INDEX</b>: Unique identifier for each unit. This will be the key to the session waveforms, spike_amplitudes and spike_times dictionaries.\n",
    "\n",
    "<p><b>Waveform metrics</b>: Look <a href=\"https://github.com/AllenInstitute/ecephys_spike_sorting/tree/master/ecephys_spike_sorting/modules/mean_waveforms\">here</a> for more detail on these metrics and the code that computes them. For the below descriptions the '1D waveform' is defined as the waveform on the peak channel. The '2D waveform' is the waveform across channels centered on the peak channel.\n",
    "\n",
    "<p><code>peak_channel_id</code>:                      channel ID for the channel on which unit's waveform had the largest amplitude  \n",
    "<p><code>amplitude</code>:                            Peak to trough amplitude for mean 1D waveform in microvolts   \n",
    "<p><code>waveform_duration</code>:                    Time from trough to peak for 1D waveform in milliseconds     \n",
    "<p><code>waveform_halfwidth</code>:                   Width of 1D waveform at half-amplitude in milliseconds  \n",
    "<p><code>PT_ratio</code>:                             Ratio of the max (peak) to the min (trough) amplitudes for 1D waveform  \n",
    "<p><code>recovery_slope</code>:                       Slope of recovery of 1D waveform to baseline after repolarization (coming down from peak)  \n",
    "<p><code>repolarization_slope</code>:                 Slope of repolarization of 1D waveform to baseline after trough  \n",
    "<p><code>spread</code>:                               Distance in microns along the shank of probe for which the spike amplitude was above 12% of the peak channel amplitude  \n",
    "<p><code>velocity_above</code>:                       Slope of spike propagation velocity traveling in dorsal direction from soma (note to avoid infinite values, this is actaully the inverse of velocity: ms/mm)  \n",
    "<p><code>velocity_below</code>:                       Slope of spike propagation velocity traveling in ventral direction from soma (note to avoid infinite values, this is actually the inverse of velocity: ms/mm)  \n",
    "<p><code>snr</code>:                                  signal-to-noise ratio for 1D waveform        \n",
    "\n",
    "\n",
    "<p><b>Quality metrics</b>: Look <a href=\"https://github.com/AllenInstitute/ecephys_spike_sorting/tree/7e567a6fc3fd2fc0eedef750b83b8b8a0d469544/ecephys_spike_sorting/modules/quality_metrics\">here</a> for more detail on these metrics and the code that computes them.\n",
    "\n",
    "<p><code>amplitude_cutoff</code>:                     estimate of miss rate based on amplitude histogram (ie fraction of spikes estimated to have been below detection threshold)  \n",
    "<p><code>cumulative_drift</code>:                     cumulative change in spike depth along probe throughout the recording  \n",
    "<p><code>max_drift</code>:                            Maximum change in unit depth across recording  \n",
    "<p><code>d_prime</code>:                              classification accuracy based on LDA  \n",
    "<p><code>firing_rate</code>:                          Mean firing rate over entire recording  \n",
    "<p><code>isi_violations</code>:                       Ratio of refractory violation rate to total spike rate  \n",
    "<p><code>isolation_distance</code>:                   Distance to nearest cluster in Mahalanobis space   \n",
    "<p><code>l_ratio</code>:                              The Mahalanobis distance and chi-squared inverse cdf are used to find the probability of cluster membership for each spike.  \n",
    "<p><code>nn_hit_rate</code>:                          Fraction of nearest neighbors in PCA space for spikes in unit cluster that are also in unit cluster  \n",
    "<p><code>nn_miss_rate</code>:                         Fraction of nearest neighbors for spikes outside unit cluster than are in unit cluster  \n",
    "<p><code>presence_ratio</code>:                       Fraction of time during session for which a unit was spiking  \n",
    "<p><code>silhouette_score</code>:                     Standard metric for cluster overlap, computed in PCA space    \n",
    "<p><code>quality</code>:                              Label assigned based on waveform shape as described <a href=\"https://github.com/AllenInstitute/ecephys_spike_sorting/tree/7e567a6fc3fd2fc0eedef750b83b8b8a0d469544/ecephys_spike_sorting/modules/noise_templates\">here</a>. Either 'good' for physiological waveforms or 'noise' for artifactual waveforms.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 3.1:</b> Plot a histogram of <code>waveform_duration</code> across all units. What happens when you restrict the data to only units with <code>quality == 'good'</code>?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Unit count')"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram across all units\n",
    "fig, ax = plt.subplots()\n",
    "_ = ax.hist(units.waveform_duration, bins=np.arange(0, 2.5, 0.05))\n",
    "\n",
    "ax.set_xlabel('waveform duration (ms)')\n",
    "ax.set_ylabel('Unit count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Unit count')"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Only 'good' units\n",
    "fig, ax = plt.subplots()\n",
    "_ = ax.hist(units[units.quality=='good'].waveform_duration, bins=np.arange(0, 2.5, 0.05))\n",
    "\n",
    "ax.set_xlabel('waveform duration (ms)')\n",
    "ax.set_ylabel('Unit count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Channels Table\n",
    "This table contains info about where each channel was in the brain as well as it's relative position along the probe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anterior_posterior_ccf_coordinate</th>\n",
       "      <th>dorsal_ventral_ccf_coordinate</th>\n",
       "      <th>filtering</th>\n",
       "      <th>left_right_ccf_coordinate</th>\n",
       "      <th>probe_channel_number</th>\n",
       "      <th>probe_horizontal_position</th>\n",
       "      <th>probe_id</th>\n",
       "      <th>probe_vertical_position</th>\n",
       "      <th>structure_acronym</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1061851368</th>\n",
       "      <td>8315.0</td>\n",
       "      <td>4024.0</td>\n",
       "      <td>AP band: 500 Hz high-pass; LFP band: 1000 Hz l...</td>\n",
       "      <td>6882.0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1054059289</td>\n",
       "      <td>20</td>\n",
       "      <td>APN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061851369</th>\n",
       "      <td>8313.0</td>\n",
       "      <td>4014.0</td>\n",
       "      <td>AP band: 500 Hz high-pass; LFP band: 1000 Hz l...</td>\n",
       "      <td>6886.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1054059289</td>\n",
       "      <td>20</td>\n",
       "      <td>APN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061851370</th>\n",
       "      <td>8311.0</td>\n",
       "      <td>4004.0</td>\n",
       "      <td>AP band: 500 Hz high-pass; LFP band: 1000 Hz l...</td>\n",
       "      <td>6889.0</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>1054059289</td>\n",
       "      <td>40</td>\n",
       "      <td>APN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            anterior_posterior_ccf_coordinate  dorsal_ventral_ccf_coordinate  \\\n",
       "id                                                                             \n",
       "1061851368                             8315.0                         4024.0   \n",
       "1061851369                             8313.0                         4014.0   \n",
       "1061851370                             8311.0                         4004.0   \n",
       "\n",
       "                                                    filtering  \\\n",
       "id                                                              \n",
       "1061851368  AP band: 500 Hz high-pass; LFP band: 1000 Hz l...   \n",
       "1061851369  AP band: 500 Hz high-pass; LFP band: 1000 Hz l...   \n",
       "1061851370  AP band: 500 Hz high-pass; LFP band: 1000 Hz l...   \n",
       "\n",
       "            left_right_ccf_coordinate  probe_channel_number  \\\n",
       "id                                                            \n",
       "1061851368                     6882.0                     0   \n",
       "1061851369                     6886.0                     1   \n",
       "1061851370                     6889.0                     2   \n",
       "\n",
       "            probe_horizontal_position    probe_id  probe_vertical_position  \\\n",
       "id                                                                           \n",
       "1061851368                         43  1054059289                       20   \n",
       "1061851369                         11  1054059289                       20   \n",
       "1061851370                         59  1054059289                       40   \n",
       "\n",
       "           structure_acronym  \n",
       "id                            \n",
       "1061851368               APN  \n",
       "1061851369               APN  \n",
       "1061851370               APN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channels = session.get_channels()\n",
    "channels.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<p><b>INDEX</b>: Unique identifier for each channel. This is the id used in the units <code>peak_channel_id</code> column.  \n",
    "    \n",
    "<h4>General Metadata</h4>\n",
    "\n",
    "<p><code>anterior_posterior_ccf_coordinate</code>:    CCF coord in the AP axis  \n",
    "<p><code>dorsal_ventral_ccf_coordinate</code>:        CCF coord in the DV axis  \n",
    "<p><code>left_right_ccf_coordinate</code>:            CCF coord in the left/right axis  \n",
    "<p><code>structure_acronym</code>:                    CCF acronym for area to which unit was assigned  \n",
    "<p><code>structure_id</code>:                         CCF structure ID for the area to which unit was assigned  \n",
    "<p><code>probe_horizontal_position</code>:            Horizontal (perpindicular to shank) probe position of each unit's peak channel in microns  \n",
    "<p><code>probe_vertical_position</code>:              Vertical (along shank) probe position of each unit's peak channel in microns  \n",
    "<p><code>probe_channel_number</code>:                 Index of channel position on probe (0-383 with 0 at tip of probe)    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Because we will want to use info from both the units table and the channels table to filter our units, we can merge them into one table with all of the relevant columns. This merged table will include quality metrics and CCF locations for every unit. For example, we can use it to see which brain regions were recorded during this session:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "units = units.merge(channels, left_on='peak_channel_id', right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now we can use the merged units/channels table to show how many units were recorded in each brain area for this session. (Hint: use the <code>structure_acronym</code> column)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structure_acronym\n",
       "CA1      269\n",
       "APN      170\n",
       "VISpm    135\n",
       "POL      109\n",
       "VISal     84\n",
       "LP        75\n",
       "VISp      75\n",
       "VISrl     74\n",
       "DG        72\n",
       "MGv       70\n",
       "VISl      69\n",
       "CA3       67\n",
       "VISam     60\n",
       "SUB       34\n",
       "LGv       34\n",
       "ZI        30\n",
       "PIL       27\n",
       "MRN       25\n",
       "MB        21\n",
       "TH        13\n",
       "MGm        8\n",
       "MGd        8\n",
       "PoT        6\n",
       "SGN        5\n",
       "DT         3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units.value_counts('structure_acronym')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3>Getting Spike Times</h3>\n",
    "    \n",
    "The spike times for every unit are stored in a dictionary called <code>spike_times</code>. The keys for this dictionary are the unit_ids (the index values for the units table).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1061853767,\n",
       " 1061853941,\n",
       " 1061853761,\n",
       " 1061853892,\n",
       " 1061853891,\n",
       " 1061853885,\n",
       " 1061853809,\n",
       " 1061853958,\n",
       " 1061853946,\n",
       " 1061853757]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spike_times = session.spike_times\n",
    "list(spike_times.keys())[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Let's grab the spike times for the first unit in this list\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.02294146 4.02470812 4.02677479 4.02880813 4.03397479 4.04140813\n",
      " 4.04354146 4.0634748  4.06940814 4.07184147 4.11887482 4.12180816\n",
      " 4.12390816 4.12577482 4.12760816 4.12970816 4.13177483 4.1464415\n",
      " 4.1489415  4.1535415 ]\n"
     ]
    }
   ],
   "source": [
    "unit_spikes = spike_times[1061853767]\n",
    "print(unit_spikes[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 3.2:</b> Plot a raster of the spike times for unit 1061853810 over the first minute of the recording. Where is this unit located? Put the unit id and the brain region in the plot title.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the brain region for this unit (remember the column with this info is called 'structure_acronym')\n",
    "unit_region = units.loc[1061853810].structure_acronym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get spike times for this unit\n",
    "unit_spikes = spike_times[1061853810]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time, seconds')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the raster for the first 60 seconds (Hint: you can use matplotlibs \"eventplot\" to efficiently plot spike rasters)\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(12,6)\n",
    "ax.eventplot(unit_spikes[unit_spikes<60], color='k')\n",
    "ax.set_title(f'unit {1061853810} in {unit_region}')\n",
    "ax.set_xlabel('Time, seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3>Getting Spike Waveforms</h3>\n",
    "    \n",
    "The waveform for each unit across all recording channels is stored in the <code>session</code> object as a dictionary called <code>mean_waveforms</code>. The keys to this dictionary are the unit ids. Let's grab the waveform for this unit and plot it.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "waveforms = session.mean_waveforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 3.3:</b> Grab the waveform for unit 1061853810. What shape is the array?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 82)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_waveform = waveforms[1061853810]\n",
    "unit_waveform.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 3.5:</b> Plot the 2D waveform as an image (Hint: use <code>imshow</code>. Does where it appears on the probe make sense given the brain region (APN is in the midbrain)?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1d2df6b5910>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(unit_waveform, origin='lower')\n",
    "ax.set_xlabel('Sample number')\n",
    "ax.set_ylabel('Channel')\n",
    "plt.colorbar(im, label='uV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Let's zoom in on the relevant section of the probe. We can use the column <code>probe_channel_number</code> to get the index of the channel on the probe where the waveform has the largest amplitude.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peak_channel = units.loc[1061853810]['probe_channel_number']\n",
    "peak_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f91b7f8f190>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(unit_waveform[peak_channel-15:peak_channel+15], origin='lower', \n",
    "               extent=[0, 82, peak_channel-15, peak_channel+15])\n",
    "ax.set_xlabel('Sample number')\n",
    "ax.set_ylabel('Channel')\n",
    "plt.colorbar(im, label='uV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now let's plot the 1D waveform on the peak channel for this unit. We'll convert the x-axis units from samples to time (remember the sampling rate was 30 kHz). Compare what you see in this plot to the waveform metrics in the units table for this unit.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'uV')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now get the waveform on this channel\n",
    "unit_peak_waveform = unit_waveform[peak_channel]\n",
    "\n",
    "#make a time vector for your x axis\n",
    "waveform_time = np.arange(unit_peak_waveform.size)/30.\n",
    "\n",
    "#now plot the waveform\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(waveform_time, unit_peak_waveform, 'k')\n",
    "ax.set_xlabel('ms')\n",
    "ax.set_ylabel('uV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "How do the waveform metrics in the metrics table for this unit compare to what we see in this plot?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "waveform_duration     0.178559\n",
      "PT_ratio               0.69187\n",
      "amplitude            254.60409\n",
      "Name: 1061853810, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(units.loc[1061853810][['waveform_duration', 'PT_ratio', 'amplitude']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<h3>Understanding the Trials Table</h3>\n",
    "       \n",
    "<p>Let's briefly review the timing of the change detection task (<code>stimulus_block</code> 0 or the <code>active</code>portion of the stimulus table). Remember the mouse is rewarded for licking to changes in the identity of a flashing natural image. \n",
    "<ul>\n",
    "<p><li>Each flash is 250 ms long with 500 ms of gray screen between images (ie, 750 ms from the start of flash <i>n</i> to the start of flash <i>n+1</i>).</li>\n",
    "\n",
    "<p><li> Mice are rewarded for licking within a reward window defined as 150-750 ms after an image change. </li>\n",
    "\n",
    "<p><li> Licks before the change abort the trial, and a new trial begins</li>\n",
    "\n",
    "<p><li> 5% of flashes can be omitted with the exception of 1) the flash immediately following an omission and 2) the flash immediately preceding a change which can't be omitted.</li>\n",
    "</ul>    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/e7/de/e7de5da4-a19f-4ef6-9bc3-3a5b088048ac/task_diagram.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "There are 5 possible ways each trial can be classified. Having a solid understanding of what these mean is vital to interpreting this data, so make sure you understand the diagram below and please ask questions if you're uncertain about anything:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/30/91/30919450-3121-4629-bf25-d2ab9e4f8b0d/doc_trial_classification_diagram.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now let's explore the behavior trials table. This table contains lots of useful information about every trial in the change detection task.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>initial_image_name</th>\n",
       "      <th>change_image_name</th>\n",
       "      <th>stimulus_change</th>\n",
       "      <th>change_time_no_display_delay</th>\n",
       "      <th>go</th>\n",
       "      <th>catch</th>\n",
       "      <th>lick_times</th>\n",
       "      <th>response_time</th>\n",
       "      <th>reward_time</th>\n",
       "      <th>reward_volume</th>\n",
       "      <th>hit</th>\n",
       "      <th>false_alarm</th>\n",
       "      <th>miss</th>\n",
       "      <th>correct_reject</th>\n",
       "      <th>aborted</th>\n",
       "      <th>auto_rewarded</th>\n",
       "      <th>change_frame</th>\n",
       "      <th>trial_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trials_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.20983</td>\n",
       "      <td>26.19343</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[25.72637, 25.87602]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.98360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.71046</td>\n",
       "      <td>28.34528</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[28.02817]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.63482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.96234</td>\n",
       "      <td>32.73245</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[32.39844]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.77011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.46618</td>\n",
       "      <td>35.85175</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[35.53463]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.38557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.46863</td>\n",
       "      <td>39.62138</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[39.15441, 39.28716]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.15275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time  stop_time initial_image_name change_image_name  \\\n",
       "trials_id                                                               \n",
       "0            25.20983   26.19343            im104_r           im104_r   \n",
       "1            26.71046   28.34528            im104_r           im104_r   \n",
       "2            28.96234   32.73245            im104_r           im104_r   \n",
       "3            33.46618   35.85175            im104_r           im104_r   \n",
       "4            36.46863   39.62138            im104_r           im104_r   \n",
       "\n",
       "           stimulus_change  change_time_no_display_delay     go  catch  \\\n",
       "trials_id                                                                \n",
       "0                    False                           NaN  False  False   \n",
       "1                    False                           NaN  False  False   \n",
       "2                    False                           NaN  False  False   \n",
       "3                    False                           NaN  False  False   \n",
       "4                    False                           NaN  False  False   \n",
       "\n",
       "                     lick_times  response_time  reward_time  reward_volume  \\\n",
       "trials_id                                                                    \n",
       "0          [25.72637, 25.87602]            NaN          NaN            0.0   \n",
       "1                    [28.02817]            NaN          NaN            0.0   \n",
       "2                    [32.39844]            NaN          NaN            0.0   \n",
       "3                    [35.53463]            NaN          NaN            0.0   \n",
       "4          [39.15441, 39.28716]            NaN          NaN            0.0   \n",
       "\n",
       "             hit  false_alarm   miss  correct_reject  aborted  auto_rewarded  \\\n",
       "trials_id                                                                      \n",
       "0          False        False  False           False     True          False   \n",
       "1          False        False  False           False     True          False   \n",
       "2          False        False  False           False     True          False   \n",
       "3          False        False  False           False     True          False   \n",
       "4          False        False  False           False     True          False   \n",
       "\n",
       "           change_frame  trial_length  \n",
       "trials_id                              \n",
       "0                   NaN       0.98360  \n",
       "1                   NaN       1.63482  \n",
       "2                   NaN       3.77011  \n",
       "3                   NaN       2.38557  \n",
       "4                   NaN       3.15275  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials = session.trials\n",
    "trials.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['start_time', 'stop_time', 'initial_image_name', 'change_image_name',\n",
       "       'stimulus_change', 'change_time_no_display_delay', 'go', 'catch',\n",
       "       'lick_times', 'response_time', 'reward_time', 'reward_volume', 'hit',\n",
       "       'false_alarm', 'miss', 'correct_reject', 'aborted', 'auto_rewarded',\n",
       "       'change_frame', 'trial_length'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<p>Unlike the stimulus presentations table in which every row corresponded to a visual stimulus presentation, for the behavior trials table every row corresponds to one trial of the change detection task. Here is a quick summary of the columns:\n",
    "\n",
    "<p><code>start_time</code>: Experiment time when this trial began in seconds.\n",
    "\n",
    "<p><code>stop_time</code>: Experiment time when this trial ended.\n",
    "\n",
    "<p><code>initial_image_name</code>: Indicates which image was shown before the change (or sham change) for this trial\n",
    "\n",
    "<p><code>change_image_name</code>: Indicates which image was scheduled to be the change image for this trial. Note that if the trial is aborted, a new trial will begin before this change occurs.\n",
    "\n",
    "<p><code>stimulus_change</code>: Indicates whether an image change occurred for this trial. \n",
    "\n",
    "<p><code>change_time_no_display_delay</code>: Experiment time when the task-control computer commanded an image change or sham change. This change time is used to determine the response window during which a lick will trigger a reward. Note that due to display lag, this is not the time when the change image actually appears on the screen. To get this time, you need the stimulus_presentations table (more about this below).\n",
    "\n",
    "<p><code>go</code>: Indicates whether this trial was a 'go' trial. To qualify as a go trial, an image change must occur and the trial cannot be autorewarded.\n",
    "\n",
    "<p><code>catch</code>: Indicates whether this trial was a 'catch' trial. To qualify as a catch trial, a 'sham' change must occur during which the image identity does not change. These sham changes are drawn to match the timing distribution of real changes and can be used to calculate the false alarm rate.\n",
    "\n",
    "<p><code>lick_times</code>: A list indicating when the behavioral control computer recognized a lick. Note that the control computer records events at the stimulus refresh rate (60 Hz). Therefore these times are not identical to the lick times from the licks dataframe, which record when the licks were registered by the lick sensor, which is sampled at a much higher rate (100 kHz). The licks dataframe should generally be used for analysis of the licking behavior rather than these times.\n",
    "\n",
    "<p><code>response_time</code>: Indicates the time when the first lick was registered by the task control software for trials that were not aborted (go or catch). NaN for aborted trials. For a more accurate measure of response time, the licks dataframe should be used.\n",
    "\n",
    "<p><code>reward_time</code>: Indicates when the reward command was triggered for hit trials. NaN for other trial types. \n",
    "\n",
    "<p><code>reward_volume</code>: Indicates the volume of water dispensed as reward for this trial. \n",
    "\n",
    "<p><code>hit</code>: Indicates whether this trial was a 'hit' trial. To qualify as a hit, the trial must be a go trial during which the stimulus changed and the mouse licked within the response window (150-750 ms after the change time).\n",
    "\n",
    "<p><code>false_alarm</code>: Indicates whether this trial was a 'false alarm' trial. To qualify as a false alarm, the trial must be a catch trial during which a sham change occurred and the mouse licked during the response window.\n",
    "\n",
    "<p><code>miss</code>: To qualify as a miss trial, the trial must be a go trial during which the stimulus changed but the mouse did not lick within the response window.\n",
    "\n",
    "<p><code>correct_reject</code>: To qualify as a correct reject trial, the trial must be a catch trial during which a sham change occurred and the mouse withheld licking.\n",
    "\n",
    "<p><code>aborted</code>: A trial is aborted when the mouse licks before the scheduled change or sham change. On these trials, the change (sham change) never occurs and, instead, a new trial begins.\n",
    "\n",
    "<p><code>auto_rewarded</code>: During autorewarded trials, the reward is automatically triggered after the change regardless of whether the mouse licked within the response window. These always come at the beginning of the session to help engage the mouse in behavior.\n",
    "\n",
    "<p><code>change_frame</code>: Indicates the stimulus frame index when the change (on go trials) or sham change (on catch trials) occurred. \n",
    "\n",
    "<p><code>trial_length</code>: Duration of the entire trial in seconds.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 4.1:</b> How many go trials were there during this session? How many were hits? Use these values to calculate the overall hit rate.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total go trials: 295\n"
     ]
    }
   ],
   "source": [
    "# Get number of go trials\n",
    "num_go = trials.go.sum()\n",
    "print(f'Total go trials: {num_go}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total hits: 139\n"
     ]
    }
   ],
   "source": [
    "# Get number of hits\n",
    "num_hits = trials.hit.sum()\n",
    "print(f'Total hits: {num_hits}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hit rate: 0.4711864406779661\n"
     ]
    }
   ],
   "source": [
    "# Get hit rate\n",
    "hit_rate = num_hits/num_go\n",
    "print(f'Hit rate: {hit_rate}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    " <h3>Putting it all together to make a change-triggered PSTH for hit trials</h3>\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding the Peri-stimulus-time histogram\n",
    "<img src=https://neuronaldynamics.epfl.ch/online/x187.png>\n",
    "\n",
    "K is the number of trials   \n",
    "&Delta;t is the time bin width  \n",
    "n<sub>k</sub>(t; t + &Delta;t) is the total count of spikes across trials in that time bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "<p>Let's make a PSTH aligned on the change times for the first 20 hit trials. To find these times, we'll need to combine information from the trials table and the stimulus_presentations table as follows:\n",
    "        <p>1) Get the first 20 hit trials from the trials dataframe.\n",
    "        <p>2) Find what stimulus frame the change for these trials occurred on.\n",
    "        <p>3) Find the flashes (rows) in the stimulus_presentations table that started on these same frames (use the <code>start_frame</code> column).\n",
    "    <p>4) Get the <code>start_time</code> for these flashes. These are the times we're looking for.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>initial_image_name</th>\n",
       "      <th>change_image_name</th>\n",
       "      <th>stimulus_change</th>\n",
       "      <th>change_time_no_display_delay</th>\n",
       "      <th>go</th>\n",
       "      <th>catch</th>\n",
       "      <th>lick_times</th>\n",
       "      <th>response_time</th>\n",
       "      <th>reward_time</th>\n",
       "      <th>reward_volume</th>\n",
       "      <th>hit</th>\n",
       "      <th>false_alarm</th>\n",
       "      <th>miss</th>\n",
       "      <th>correct_reject</th>\n",
       "      <th>aborted</th>\n",
       "      <th>auto_rewarded</th>\n",
       "      <th>change_frame</th>\n",
       "      <th>trial_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trials_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>111.53134</td>\n",
       "      <td>123.29171</td>\n",
       "      <td>im111_r</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>True</td>\n",
       "      <td>119.03784</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[119.55534, 119.70437, 120.48827, 120.68841, 1...</td>\n",
       "      <td>119.55534</td>\n",
       "      <td>119.55534</td>\n",
       "      <td>0.003</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5685.0</td>\n",
       "      <td>11.76037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>126.54407</td>\n",
       "      <td>133.83337</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>im005_r</td>\n",
       "      <td>True</td>\n",
       "      <td>129.54671</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[130.28088, 130.81353, 130.94707, 131.0805, 13...</td>\n",
       "      <td>130.28088</td>\n",
       "      <td>130.28088</td>\n",
       "      <td>0.003</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6315.0</td>\n",
       "      <td>7.28930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>134.05003</td>\n",
       "      <td>143.57470</td>\n",
       "      <td>im005_r</td>\n",
       "      <td>im034_r</td>\n",
       "      <td>True</td>\n",
       "      <td>139.30535</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[139.80542, 139.93793, 140.07157, 140.17142, 1...</td>\n",
       "      <td>139.80542</td>\n",
       "      <td>139.80542</td>\n",
       "      <td>0.003</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>9.52467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>161.07253</td>\n",
       "      <td>170.61417</td>\n",
       "      <td>im111_r</td>\n",
       "      <td>im034_r</td>\n",
       "      <td>True</td>\n",
       "      <td>166.32763</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[166.74467, 166.87711, 167.02716, 167.14395, 1...</td>\n",
       "      <td>166.74467</td>\n",
       "      <td>166.74467</td>\n",
       "      <td>0.003</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8520.0</td>\n",
       "      <td>9.54164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>176.08527</td>\n",
       "      <td>183.35841</td>\n",
       "      <td>im034_r</td>\n",
       "      <td>im087_r</td>\n",
       "      <td>True</td>\n",
       "      <td>179.08806</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>[179.57194, 179.68808, 179.80451, 179.90462, 1...</td>\n",
       "      <td>179.57194</td>\n",
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      ],
      "text/plain": [
       "           start_time  stop_time initial_image_name change_image_name  \\\n",
       "trials_id                                                               \n",
       "26          111.53134  123.29171            im111_r           im104_r   \n",
       "28          126.54407  133.83337            im104_r           im005_r   \n",
       "29          134.05003  143.57470            im005_r           im034_r   \n",
       "34          161.07253  170.61417            im111_r           im034_r   \n",
       "37          176.08527  183.35841            im034_r           im087_r   \n",
       "\n",
       "           stimulus_change  change_time_no_display_delay    go  catch  \\\n",
       "trials_id                                                               \n",
       "26                    True                     119.03784  True  False   \n",
       "28                    True                     129.54671  True  False   \n",
       "29                    True                     139.30535  True  False   \n",
       "34                    True                     166.32763  True  False   \n",
       "37                    True                     179.08806  True  False   \n",
       "\n",
       "                                                  lick_times  response_time  \\\n",
       "trials_id                                                                     \n",
       "26         [119.55534, 119.70437, 120.48827, 120.68841, 1...      119.55534   \n",
       "28         [130.28088, 130.81353, 130.94707, 131.0805, 13...      130.28088   \n",
       "29         [139.80542, 139.93793, 140.07157, 140.17142, 1...      139.80542   \n",
       "34         [166.74467, 166.87711, 167.02716, 167.14395, 1...      166.74467   \n",
       "37         [179.57194, 179.68808, 179.80451, 179.90462, 1...      179.57194   \n",
       "\n",
       "           reward_time  reward_volume   hit  false_alarm   miss  \\\n",
       "trials_id                                                         \n",
       "26           119.55534          0.003  True        False  False   \n",
       "28           130.28088          0.003  True        False  False   \n",
       "29           139.80542          0.003  True        False  False   \n",
       "34           166.74467          0.003  True        False  False   \n",
       "37           179.57194          0.003  True        False  False   \n",
       "\n",
       "           correct_reject  aborted  auto_rewarded  change_frame  trial_length  \n",
       "trials_id                                                                      \n",
       "26                  False    False          False        5685.0      11.76037  \n",
       "28                  False    False          False        6315.0       7.28930  \n",
       "29                  False    False          False        6900.0       9.52467  \n",
       "34                  False    False          False        8520.0       9.54164  \n",
       "37                  False    False          False        9285.0       7.27314  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the first 20 hit trials. To do this we'll use iloc to index the trials dataframe by position\n",
    "first_20_hits = trials[trials.hit].iloc[:20]\n",
    "first_20_hits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the change frames for these trials\n",
    "change_frames = first_20_hits.change_frame.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
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       "      <td>0.250200</td>\n",
       "      <td>23792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>im104_r</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23777</td>\n",
       "      <td>420.868479</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>421.118680</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250200</td>\n",
       "      <td>24467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>im114_r</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24452</td>\n",
       "      <td>432.127859</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>432.378059</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250210</td>\n",
       "      <td>26267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>im024_r</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26252</td>\n",
       "      <td>462.153001</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>462.403210</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250213</td>\n",
       "      <td>26898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>im034_r</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26883</td>\n",
       "      <td>472.678441</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural_Images_Lum_Matched_set_ophys_H_2019</td>\n",
       "      <td>472.928654</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           active  color  contrast  duration  end_frame  \\\n",
       "stimulus_presentations_id                                                 \n",
       "125                          True    NaN       NaN  0.250208       5700   \n",
       "139                          True    NaN       NaN  0.250200       6330   \n",
       "152                          True    NaN       NaN  0.250210       6915   \n",
       "188                          True    NaN       NaN  0.250205       8535   \n",
       "205                          True    NaN       NaN  0.250205       9300   \n",
       "220                          True    NaN       NaN  0.250205       9975   \n",
       "267                          True    NaN       NaN  0.250210      12091   \n",
       "323                          True    NaN       NaN  0.250210      14612   \n",
       "345                          True    NaN       NaN  0.250208      15602   \n",
       "373                          True    NaN       NaN  0.250211      16862   \n",
       "383                          True    NaN       NaN  0.250210      17312   \n",
       "408                          True    NaN       NaN  0.250210      18437   \n",
       "419                          True    NaN       NaN  0.250212      18932   \n",
       "450                          True    NaN       NaN  0.250213      20327   \n",
       "480                          True    NaN       NaN  0.250200      21677   \n",
       "515                          True    NaN       NaN  0.250212      23252   \n",
       "527                          True    NaN       NaN  0.250200      23792   \n",
       "542                          True    NaN       NaN  0.250200      24467   \n",
       "582                          True    NaN       NaN  0.250210      26267   \n",
       "596                          True    NaN       NaN  0.250213      26898   \n",
       "\n",
       "                           flashes_since_change image_name is_change omitted  \\\n",
       "stimulus_presentations_id                                                      \n",
       "125                                         0.0    im104_r      True   False   \n",
       "139                                         0.0    im005_r      True   False   \n",
       "152                                         0.0    im034_r      True   False   \n",
       "188                                         0.0    im034_r      True   False   \n",
       "205                                         0.0    im087_r      True   False   \n",
       "220                                         0.0    im034_r      True   False   \n",
       "267                                         0.0    im005_r      True   False   \n",
       "323                                         0.0    im083_r      True   False   \n",
       "345                                         0.0    im104_r      True   False   \n",
       "373                                         0.0    im034_r      True   False   \n",
       "383                                         0.0    im005_r      True   False   \n",
       "408                                         0.0    im024_r      True   False   \n",
       "419                                         0.0    im087_r      True   False   \n",
       "450                                         0.0    im024_r      True   False   \n",
       "480                                         0.0    im104_r      True   False   \n",
       "515                                         0.0    im034_r      True   False   \n",
       "527                                         0.0    im104_r      True   False   \n",
       "542                                         0.0    im114_r      True   False   \n",
       "582                                         0.0    im024_r      True   False   \n",
       "596                                         0.0    im034_r      True   False   \n",
       "\n",
       "                           orientation  ...  position_y  rewarded  \\\n",
       "stimulus_presentations_id               ...                         \n",
       "125                                NaN  ...         NaN      True   \n",
       "139                                NaN  ...         NaN      True   \n",
       "152                                NaN  ...         NaN      True   \n",
       "188                                NaN  ...         NaN      True   \n",
       "205                                NaN  ...         NaN      True   \n",
       "220                                NaN  ...         NaN      True   \n",
       "267                                NaN  ...         NaN      True   \n",
       "323                                NaN  ...         NaN      True   \n",
       "345                                NaN  ...         NaN      True   \n",
       "373                                NaN  ...         NaN      True   \n",
       "383                                NaN  ...         NaN      True   \n",
       "408                                NaN  ...         NaN      True   \n",
       "419                                NaN  ...         NaN      True   \n",
       "450                                NaN  ...         NaN      True   \n",
       "480                                NaN  ...         NaN      True   \n",
       "515                                NaN  ...         NaN      True   \n",
       "527                                NaN  ...         NaN      True   \n",
       "542                                NaN  ...         NaN      True   \n",
       "582                                NaN  ...         NaN      True   \n",
       "596                                NaN  ...         NaN      True   \n",
       "\n",
       "                          spatial_frequency  start_frame  start_time  \\\n",
       "stimulus_presentations_id                                              \n",
       "125                                     NaN         5685  119.082772   \n",
       "139                                     NaN         6315  129.591550   \n",
       "152                                     NaN         6900  139.349710   \n",
       "188                                     NaN         8520  166.372299   \n",
       "205                                     NaN         9285  179.132955   \n",
       "220                                     NaN         9960  190.392379   \n",
       "267                                     NaN        12076  225.688551   \n",
       "323                                     NaN        14597  267.740381   \n",
       "345                                     NaN        15587  284.254183   \n",
       "373                                     NaN        16847  305.271771   \n",
       "383                                     NaN        17297  312.778041   \n",
       "408                                     NaN        18422  331.543742   \n",
       "419                                     NaN        18917  339.800654   \n",
       "450                                     NaN        20312  363.070096   \n",
       "480                                     NaN        21662  385.588960   \n",
       "515                                     NaN        23237  411.860914   \n",
       "527                                     NaN        23777  420.868479   \n",
       "542                                     NaN        24452  432.127859   \n",
       "582                                     NaN        26252  462.153001   \n",
       "596                                     NaN        26883  472.678441   \n",
       "\n",
       "                           stimulus_block  stimulus_index  \\\n",
       "stimulus_presentations_id                                   \n",
       "125                                     0             NaN   \n",
       "139                                     0             NaN   \n",
       "152                                     0             NaN   \n",
       "188                                     0             NaN   \n",
       "205                                     0             NaN   \n",
       "220                                     0             NaN   \n",
       "267                                     0             NaN   \n",
       "323                                     0             NaN   \n",
       "345                                     0             NaN   \n",
       "373                                     0             NaN   \n",
       "383                                     0             NaN   \n",
       "408                                     0             NaN   \n",
       "419                                     0             NaN   \n",
       "450                                     0             NaN   \n",
       "480                                     0             NaN   \n",
       "515                                     0             NaN   \n",
       "527                                     0             NaN   \n",
       "542                                     0             NaN   \n",
       "582                                     0             NaN   \n",
       "596                                     0             NaN   \n",
       "\n",
       "                                                         stimulus_name  \\\n",
       "stimulus_presentations_id                                                \n",
       "125                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "139                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "152                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "188                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "205                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "220                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "267                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "323                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "345                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "373                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "383                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "408                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "419                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "450                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "480                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "515                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "527                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "542                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "582                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "596                        Natural_Images_Lum_Matched_set_ophys_H_2019   \n",
       "\n",
       "                            stop_time  temporal_frequency  \n",
       "stimulus_presentations_id                                  \n",
       "125                        119.332980                 NaN  \n",
       "139                        129.841750                 NaN  \n",
       "152                        139.599920                 NaN  \n",
       "188                        166.622504                 NaN  \n",
       "205                        179.383160                 NaN  \n",
       "220                        190.642584                 NaN  \n",
       "267                        225.938760                 NaN  \n",
       "323                        267.990591                 NaN  \n",
       "345                        284.504391                 NaN  \n",
       "373                        305.521982                 NaN  \n",
       "383                        313.028251                 NaN  \n",
       "408                        331.793951                 NaN  \n",
       "419                        340.050866                 NaN  \n",
       "450                        363.320309                 NaN  \n",
       "480                        385.839160                 NaN  \n",
       "515                        412.111126                 NaN  \n",
       "527                        421.118680                 NaN  \n",
       "542                        432.378059                 NaN  \n",
       "582                        462.403210                 NaN  \n",
       "596                        472.928654                 NaN  \n",
       "\n",
       "[20 rows x 21 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the flashes in the stimulus_presentations table that started on these frames\n",
    "first_20_hit_flashes = stimulus_presentations[np.isin(stimulus_presentations.start_frame, change_frames)]\n",
    "first_20_hit_flashes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([119.08277242, 129.59154992, 139.34971032, 166.37229874,\n",
       "       179.13295492, 190.39237874, 225.68855056, 267.7403812 ,\n",
       "       284.25418258, 305.27177131, 312.77804052, 331.5437416 ,\n",
       "       339.80065404, 363.07009625, 385.58896026, 411.86091404,\n",
       "       420.86847919, 432.12785948, 462.15300081, 472.67844132])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the display start times for these flashes\n",
    "first_20_hit_change_times = first_20_hit_flashes.start_time.values\n",
    "first_20_hit_change_times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now that we have our change times, we're almost ready to make our PSTH. Next we need to get spike times for a good neuron. To start out, let's filter our units dataframe for 'good' units, based on the following criteria:\n",
    "    <p> &emsp; a) <code>quality</code> == 'good'\n",
    "            <p> &emsp; b) <code>firing_rate</code> &#62; 1 Hz\n",
    "            <p> &emsp; c) <code>snr</code> &#62; 1\n",
    "            <p> &emsp; d) <code>isi_violations</code> &#60; 1\n",
    "                                                         \n",
    "<p>Then we'll grab an example unit from this curated list\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter units by the quality criteria\n",
    "good_units = units[(units['quality']=='good') &\n",
    "                   (units['firing_rate']>1) &\n",
    "                   (units['snr']>1) &\n",
    "                   (units['isi_violations']<1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab an example V1 unit id from good_units\n",
    "v1_unit_id = good_units[good_units['structure_acronym']=='VISp'].index.values[0] #just take the first one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makePSTH(spikes, startTimes, windowDur, binSize=0.001):\n",
    "    '''\n",
    "    Convenience function to compute a peri-stimulus-time histogram\n",
    "    (see section 7.2.2 here: https://neuronaldynamics.epfl.ch/online/Ch7.S2.html)\n",
    "    INPUTS:\n",
    "        spikes: spike times in seconds for one unit\n",
    "        startTimes: trial start times in seconds; the first spike count \n",
    "            bin will be aligned to these times\n",
    "        windowDur: trial duration in seconds\n",
    "        binSize: size of spike count bins in seconds\n",
    "    OUTPUTS:\n",
    "        Tuple of (PSTH, bins), where:\n",
    "            PSTH gives the trial-averaged spike rate for \n",
    "                each time bin aligned to the start times;\n",
    "            bins are the bin edges as defined by numpy histogram\n",
    "    '''\n",
    "    bins = np.arange(0,windowDur+binSize,binSize)\n",
    "    counts = np.zeros(bins.size-1)\n",
    "    for start in startTimes:\n",
    "        startInd = np.searchsorted(spikes, start)\n",
    "        endInd = np.searchsorted(spikes, start+windowDur)\n",
    "        counts = counts + np.histogram(spikes[startInd:endInd]-start, bins)[0]\n",
    "    \n",
    "    counts = counts/len(startTimes)\n",
    "    return counts/binSize, bins[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 4.2:</b> Plot a PSTH for one VISp unit aligned to the change times for the first 20 hit trials.  \n",
    "        <p>1) Get the spike times for this unit\n",
    "        <p>2) Call <code>makePSTH</code> to make a PSTH for this unit aligned to <code>first_20_hit_change_times</code>. Use a <code>binSize</code> of 10 ms (0.01) and a <code>windowDur</code> of 1 second.\n",
    "        <p>3) Plot the PSTH. What do you make of it? When is this neuron active?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the spike times for this unit\n",
    "unit_spike_times = spike_times[v1_unit_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the PSTH\n",
    "unit_psth, bins = makePSTH(unit_spike_times, first_20_hit_change_times, windowDur=1, binSize=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'unit id: 1061854501')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PSTH\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(bins, unit_psth, 'k')\n",
    "ax.set_xlabel('Time from change (seconds)')\n",
    "ax.set_ylabel('Firing Rate')\n",
    "ax.set_title(f'unit id: {v1_unit_id}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SESSION 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<h2> Getting reward, pupil, running and licking data </h2>\n",
    "<p>\n",
    "<p>Now let's grab the reward, licking, running and pupil tracking data for this session and align them to a common time point. We'll use the time of one of the rewards to align the data.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's get the four behavior data frames\n",
    "rewards = session.rewards\n",
    "eye_tracking = session.eye_tracking\n",
    "running_speed = session.running_speed\n",
    "licks = session.licks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<b>Reward dataframe:</b> The timestamps and water volumes for every reward given to the mouse. The <code>auto_rewarded</code> column is True for the first three change trials, when the reward is automatically given to the mouse to kick-start the behavior session. After that, the mouse must earn rewards by licking after a change in the reward window.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "      <th>timestamps</th>\n",
       "      <th>auto_rewarded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005</td>\n",
       "      <td>44.87655</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005</td>\n",
       "      <td>64.39291</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.005</td>\n",
       "      <td>80.90632</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003</td>\n",
       "      <td>119.55534</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003</td>\n",
       "      <td>130.28088</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  timestamps  auto_rewarded\n",
       "0   0.005    44.87655           True\n",
       "1   0.005    64.39291           True\n",
       "2   0.005    80.90632           True\n",
       "3   0.003   119.55534          False\n",
       "4   0.003   130.28088          False"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rewards.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Let's grab a random reward to align the rest of the behavior data to:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "reward_time = rewards.timestamps.iloc[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<b>Running dataframe:</b> One entry for each read of the analog input line monitoring the encoder voltage, recorded at ~60 Hz. <code>speed</code> is in units cm/s. \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamps</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.21123</td>\n",
       "      <td>-0.025043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.22482</td>\n",
       "      <td>11.007905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.24165</td>\n",
       "      <td>21.528507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.25821</td>\n",
       "      <td>31.042079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.27497</td>\n",
       "      <td>39.117779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamps      speed\n",
       "0    24.21123  -0.025043\n",
       "1    24.22482  11.007905\n",
       "2    24.24165  21.528507\n",
       "3    24.25821  31.042079\n",
       "4    24.27497  39.117779"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_speed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "    <p> Let's cut out a snippet of the running speed in a window 3 seconds before and after our <code>reward_time</code>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grab the pupil data corresponding to our window\n",
    "time_before = 3.0 #how much time to plot before the reward\n",
    "time_after = 3.0 #how much time to plot after the reward\n",
    "\n",
    "#Get running data aligned to this reward\n",
    "trial_running = running_speed[(running_speed['timestamps']>=reward_time-time_before)&\n",
    "                              (running_speed['timestamps']< reward_time+time_after)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "    <p>Now we can plot this running trace together with the reward for reference.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d26c91bbb0>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the running\n",
    "fig, ax_running = plt.subplots()\n",
    "fig.set_size_inches(14,6)\n",
    "ax_running.plot(trial_running['timestamps'], trial_running['speed'], 'k')\n",
    "\n",
    "# label our axes\n",
    "ax_running.set_xlabel('Time (seconds)')\n",
    "ax_running.set_ylabel('Running speed (cm/s)')\n",
    "\n",
    "# now plot the reward\n",
    "ax_running.axvline(reward_time, color='b')\n",
    "ax_running.legend(['run speed', 'reward time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<b>Eye tracking dataframe:</b> One entry containing ellipse fit parameters for the eye, pupil and corneal reflection for every frame of the eye tracking video stream. These fits are the result of a <a href=\"http://www.mackenziemathislab.org/deeplabcut\">DeepLabCut</a> model trained on these eye features.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamps</th>\n",
       "      <th>cr_area</th>\n",
       "      <th>eye_area</th>\n",
       "      <th>pupil_area</th>\n",
       "      <th>likely_blink</th>\n",
       "      <th>pupil_area_raw</th>\n",
       "      <th>cr_area_raw</th>\n",
       "      <th>eye_area_raw</th>\n",
       "      <th>cr_center_x</th>\n",
       "      <th>cr_center_y</th>\n",
       "      <th>...</th>\n",
       "      <th>eye_center_x</th>\n",
       "      <th>eye_center_y</th>\n",
       "      <th>eye_width</th>\n",
       "      <th>eye_height</th>\n",
       "      <th>eye_phi</th>\n",
       "      <th>pupil_center_x</th>\n",
       "      <th>pupil_center_y</th>\n",
       "      <th>pupil_width</th>\n",
       "      <th>pupil_height</th>\n",
       "      <th>pupil_phi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.36447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.38113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.39780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.41446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.43113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamps  cr_area  eye_area  pupil_area  likely_blink  \\\n",
       "frame                                                            \n",
       "0         1.36447      NaN       NaN         NaN          True   \n",
       "1         1.38113      NaN       NaN         NaN          True   \n",
       "2         1.39780      NaN       NaN         NaN          True   \n",
       "3         1.41446      NaN       NaN         NaN          True   \n",
       "4         1.43113      NaN       NaN         NaN          True   \n",
       "\n",
       "       pupil_area_raw  cr_area_raw  eye_area_raw  cr_center_x  cr_center_y  \\\n",
       "frame                                                                        \n",
       "0                 NaN          NaN           NaN          NaN          NaN   \n",
       "1                 NaN          NaN           NaN          NaN          NaN   \n",
       "2                 NaN          NaN           NaN          NaN          NaN   \n",
       "3                 NaN          NaN           NaN          NaN          NaN   \n",
       "4                 NaN          NaN           NaN          NaN          NaN   \n",
       "\n",
       "       ...  eye_center_x  eye_center_y  eye_width  eye_height  eye_phi  \\\n",
       "frame  ...                                                               \n",
       "0      ...           NaN           NaN        NaN         NaN      NaN   \n",
       "1      ...           NaN           NaN        NaN         NaN      NaN   \n",
       "2      ...           NaN           NaN        NaN         NaN      NaN   \n",
       "3      ...           NaN           NaN        NaN         NaN      NaN   \n",
       "4      ...           NaN           NaN        NaN         NaN      NaN   \n",
       "\n",
       "       pupil_center_x  pupil_center_y  pupil_width  pupil_height  pupil_phi  \n",
       "frame                                                                        \n",
       "0                 NaN             NaN          NaN           NaN        NaN  \n",
       "1                 NaN             NaN          NaN           NaN        NaN  \n",
       "2                 NaN             NaN          NaN           NaN        NaN  \n",
       "3                 NaN             NaN          NaN           NaN        NaN  \n",
       "4                 NaN             NaN          NaN           NaN        NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_tracking.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "There seem to be several rows for which there are no valid data. We can use the <code>likely_blink</code> column to filter these out. This column indicates frames where the eye or pupil failed to fit or where the z-scored value of the eye or pupil area exceeds 3. In addition, two frames before and after each such likely_blink frame are also labeled likely_blink to avoid fits to partially closed or obstructed eye images.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>eye_area</th>\n",
       "      <th>pupil_area</th>\n",
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       "      <td>319.495616</td>\n",
       "      <td>250.370579</td>\n",
       "      <td>...</td>\n",
       "      <td>300.310986</td>\n",
       "      <td>243.292274</td>\n",
       "      <td>169.847920</td>\n",
       "      <td>149.460434</td>\n",
       "      <td>-0.097911</td>\n",
       "      <td>314.511686</td>\n",
       "      <td>228.197930</td>\n",
       "      <td>72.940553</td>\n",
       "      <td>67.705300</td>\n",
       "      <td>0.428495</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.71446</td>\n",
       "      <td>126.786150</td>\n",
       "      <td>80047.938510</td>\n",
       "      <td>17011.966515</td>\n",
       "      <td>False</td>\n",
       "      <td>17011.966515</td>\n",
       "      <td>126.786150</td>\n",
       "      <td>80047.938510</td>\n",
       "      <td>319.370721</td>\n",
       "      <td>250.517232</td>\n",
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       "      <th>21</th>\n",
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       "      <td>125.499820</td>\n",
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       "      <td>16980.668668</td>\n",
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       "      <td>17062.517640</td>\n",
       "      <td>False</td>\n",
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       "      <td>80325.325277</td>\n",
       "      <td>318.788323</td>\n",
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       "      <td>170.311250</td>\n",
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       "      <td>73.696459</td>\n",
       "      <td>67.493397</td>\n",
       "      <td>0.442226</td>\n",
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      ],
      "text/plain": [
       "       timestamps     cr_area      eye_area    pupil_area  likely_blink  \\\n",
       "frame                                                                     \n",
       "18        1.68114  124.496878  80242.785195  16745.289582         False   \n",
       "19        1.69781  123.635359  79751.038277  16714.291578         False   \n",
       "20        1.71446  126.786150  80047.938510  17011.966515         False   \n",
       "21        1.73113  125.499820  80455.812438  16980.668668         False   \n",
       "22        1.74780  125.322643  80325.325277  17062.517640         False   \n",
       "\n",
       "       pupil_area_raw  cr_area_raw  eye_area_raw  cr_center_x  cr_center_y  \\\n",
       "frame                                                                        \n",
       "18       16745.289582   124.496878  80242.785195   319.022720   250.040195   \n",
       "19       16714.291578   123.635359  79751.038277   319.495616   250.370579   \n",
       "20       17011.966515   126.786150  80047.938510   319.370721   250.517232   \n",
       "21       16980.668668   125.499820  80455.812438   319.460923   250.304993   \n",
       "22       17062.517640   125.322643  80325.325277   318.788323   249.441627   \n",
       "\n",
       "       ...  eye_center_x  eye_center_y   eye_width  eye_height   eye_phi  \\\n",
       "frame  ...                                                                 \n",
       "18     ...    299.845062    243.459515  170.130669  150.132084 -0.117730   \n",
       "19     ...    300.310986    243.292274  169.847920  149.460434 -0.097911   \n",
       "20     ...    300.324160    243.947900  169.934887  149.940078 -0.087952   \n",
       "21     ...    300.694063    243.883540  170.484281  150.218427 -0.088550   \n",
       "22     ...    300.010309    243.120696  170.311250  150.127165 -0.108505   \n",
       "\n",
       "       pupil_center_x  pupil_center_y  pupil_width  pupil_height  pupil_phi  \n",
       "frame                                                                        \n",
       "18         314.825135      228.266183    73.008159     68.378221   0.394571  \n",
       "19         314.511686      228.197930    72.940553     67.705300   0.428495  \n",
       "20         314.455601      228.205356    73.587208     68.166068   0.423260  \n",
       "21         314.077673      228.185581    73.519485     67.545130   0.460218  \n",
       "22         314.283247      227.043133    73.696459     67.493397   0.442226  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_tracking_noblinks = eye_tracking[~eye_tracking['likely_blink']]\n",
    "eye_tracking_noblinks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 5.1:</b>  Grab the pupil area data from <code>eye_tracking_noblinks</code> in the same window around our reward time as we did for the running. Plot it as we did the running.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d2696aba30>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First make a dataframe to store the eye tracking data in our time window\n",
    "trial_eye_tracking_data = eye_tracking_noblinks[(eye_tracking_noblinks['timestamps']>=reward_time-time_before)&\n",
    "                                         (eye_tracking_noblinks['timestamps'] < reward_time+time_after)]\n",
    "\n",
    "# Now plot the pupil_area column for this dataframe. Use the timestamps column for your x values\n",
    "fig, ax_pupil = plt.subplots()\n",
    "fig.set_size_inches(14,6)\n",
    "\n",
    "ax_pupil.plot(trial_eye_tracking_data['timestamps'], trial_eye_tracking_data['pupil_area'], 'g')\n",
    "ax_pupil.set_ylabel('pupil area\\n$(pixels^2)$')\n",
    "\n",
    "# now plot the reward\n",
    "ax_pupil.axvline(reward_time, color='b')\n",
    "ax_pupil.legend(['pupil_area', 'reward time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<b>Licking dataframe:</b> One entry for every detected lick onset time.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamps</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.72309</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.85921</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.01198</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.39186</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.52050</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamps  frame\n",
       "0    25.72309     91\n",
       "1    25.85921    100\n",
       "2    28.01198    229\n",
       "3    32.39186    491\n",
       "4    35.52050    679"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "licks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 5.3:</b> Now plot the licking data in this window. Plot the licks as magenta dots with 0 for the y coord. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d26f07bd90>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get licking aligned to this reward\n",
    "trial_licking = licks[(licks['timestamps']>=reward_time-time_before)&\n",
    "                      (licks['timestamps'] < reward_time+time_after)]\n",
    "\n",
    "# plot it\n",
    "fig, ax_licking = plt.subplots()\n",
    "fig.set_size_inches(14,6)\n",
    "\n",
    "ax_licking.plot(trial_licking['timestamps'], np.zeros(len(trial_licking['timestamps'])), 'mo')\n",
    "\n",
    "# now plot the reward\n",
    "ax_licking.axvline(reward_time, color='b')\n",
    "ax_licking.legend(['pupil_area', 'reward time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Below, we'll put all of these plots together, and add bars to denote the stimulus presentation times:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Single trial change-aligned running speed, pupil size, licks, and rewards')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the running\n",
    "fig, ax_running = plt.subplots()\n",
    "fig.set_size_inches(14,6)\n",
    "ax_running.plot(trial_running['timestamps'], trial_running['speed'], 'k')\n",
    "\n",
    "# now plot the reward\n",
    "rew_handle = ax_running.axvline(reward_time, color='b')\n",
    "\n",
    "# label our axes\n",
    "ax_running.set_xlabel('Time (seconds)')\n",
    "ax_running.set_ylabel('Running speed (cm/s)')\n",
    "\n",
    "# Plot the pupil area on a new axis with the same x-axis as the running data\n",
    "ax_pupil = ax_running.twinx()\n",
    "ax_pupil.plot(trial_eye_tracking_data['timestamps'], trial_eye_tracking_data['pupil_area'], 'g')\n",
    "ax_pupil.set_ylabel('pupil area\\n$(pixels^2)$')\n",
    "\n",
    "# format the pupil axis to match the plot color\n",
    "ax_pupil.yaxis.label.set_color('g')\n",
    "ax_pupil.spines['right'].set_color('g')\n",
    "ax_pupil.tick_params(axis='y', colors='g')\n",
    "\n",
    "# Plot the licking. Just use the running axis.\n",
    "lick_handle, = ax_running.plot(trial_licking['timestamps'], np.zeros(len(trial_licking['timestamps'])), 'mo')\n",
    "\n",
    "\n",
    "#Now add a legend to label the reward and licks\n",
    "ax_running.legend([rew_handle, lick_handle], ['reward', 'licks'])\n",
    "\n",
    "\n",
    "#Plot the image flashes as grey bars. \n",
    "#Get stimulus presentations around this reward\n",
    "behavior_presentations = stimulus_presentations[stimulus_presentations['active']]\n",
    "trial_stimuli = behavior_presentations[(behavior_presentations['start_time']>=reward_time-time_before)&\n",
    "                      (behavior_presentations['start_time'] < reward_time+time_after)]\n",
    "\n",
    "#Specify different colors for the 2 images shown in this window\n",
    "colors = ['0.3', '0.8']\n",
    "stimulus_colors = {stim: c for stim,c in zip(trial_stimuli['image_name'].unique(), colors)}\n",
    "for idx, stimulus in trial_stimuli.iterrows():\n",
    "    ax_running.axvspan(stimulus['start_time'], stimulus['stop_time'], color=stimulus_colors[stimulus['image_name']], alpha=0.5)\n",
    "    \n",
    "ax_running.set_title('Single trial change-aligned running speed, pupil size, licks, and rewards')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<h2> How does novelty impact visual responsiveness in VISp? </h2>\n",
    "    \n",
    "<p>\n",
    "    \n",
    "<h3> Plotting visual responses to novel and familiar images </h3>\n",
    "<p>    \n",
    "<p>Below, we'll try to answer whether units in VISp are modulated by stimulus novelty. First we'll need a way to identify which images are novel to the mouse.\n",
    "<p>\n",
    "<p>Image novelty depends on whether a mouse has previously encountered an image during training. Even in the novel image set, 2 images are shared with the training set and therefore familiar. Moreover, some mice train with image set <code>G</code> and some with image set <code>H</code>, meaning which images are novel depends on all these factors. This can be tricky to sort out, so we have provided a convenient function that will add columns to the stimulus presentations table that indicate which image set a particular image is from (unique to <code>G</code>, unique to <code>H</code> or shared across both image sets), along with whether it was novel to the mouse during that session.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getImageNovelty(image_name, sessionID, ecephys_sessions_table):\n",
    "    '''\n",
    "    Function to help annotate the stimulus_presentations table\n",
    "    to indicate the following info:\n",
    "        1) whether a given image was:\n",
    "            a) unique to the G set\n",
    "            b) unique to the H set\n",
    "            c) shared across G and H\n",
    "            d) omitted\n",
    "        2) whether the image was novel to the mouse\n",
    "    \n",
    "    Meant to be run on the image names of the stimulus_presentations table\n",
    "    \n",
    "    OUTPUT:\n",
    "        Tuple indicating (image_set, image_novelty) for this image\n",
    "\n",
    "    '''\n",
    "    session_id = session.metadata['ecephys_session_id']\n",
    "    is_novel_image_set = ecephys_sessions_table.loc[session_id]['experience_level'] == 'Novel'\n",
    "\n",
    "    IMAGE_GROUP_KEY={\n",
    "                'G' : ['im012_r', 'im036_r', 'im044_r', \n",
    "                    'im047_r', 'im078_r', 'im115_r'],\n",
    "                'H' : ['im005_r', 'im024_r', 'im034_r', \n",
    "                    'im087_r', 'im104_r', 'im114_r'],\n",
    "                'shared' : ['im083_r', 'im111_r'],\n",
    "                'omitted' : ['omitted']\n",
    "                }\n",
    "    \n",
    "    # First check that this image is one of the Natural Images used\n",
    "    image_in_image_set = any([np.isin(image_name, imset) \\\n",
    "                              for _,imset in IMAGE_GROUP_KEY.items()]) \n",
    "    if not image_in_image_set:\n",
    "        return np.nan\n",
    "\n",
    "    #Get the image set for this image\n",
    "    image_set_for_this_image = [name for name, image_set in IMAGE_GROUP_KEY.items()\\\n",
    "                                if image_name in image_set][0]\n",
    "    \n",
    "    #Get the image novelty for this image\n",
    "    if image_set_for_this_image == 'omitted':\n",
    "        novelty_for_this_image = np.nan\n",
    "    else:\n",
    "        novelty_for_this_image = (is_novel_image_set) and \\\n",
    "                            (np.isin(image_set_for_this_image, ['G', 'H']))\n",
    "    \n",
    "\n",
    "    return bool(novelty_for_this_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now let's apply that function to our stimulus_presentations table and add the new columns indicating image novelty\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/allensdk_38/lib/python3.8/site-packages/numpy/lib/arraysetops.py:608: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "is_novel = []\n",
    "for rowindex, row in stimulus_presentations.iterrows():\n",
    "    is_this_image_novel = getImageNovelty(row['image_name'], session_id, ecephys_sessions_table)\n",
    "    is_novel.append(is_this_image_novel)\n",
    "\n",
    "stimulus_presentations['is_novel'] = is_novel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<b>Note:</b> we could also do this by using pandas apply:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "is_novel = stimulus_presentations.apply(lambda row: getImageNovelty(row['image_name'], session_id, ecephys_sessions_table), axis=1)  \n",
    "stimulus_presentations['is_novel'] = is_novel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_novel</th>\n",
       "      <th>image_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stimulus_presentations_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>False</td>\n",
       "      <td>im111_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>False</td>\n",
       "      <td>im111_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>False</td>\n",
       "      <td>im111_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>False</td>\n",
       "      <td>im111_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>False</td>\n",
       "      <td>im111_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>True</td>\n",
       "      <td>im104_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>True</td>\n",
       "      <td>im104_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>True</td>\n",
       "      <td>im104_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>True</td>\n",
       "      <td>im104_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>True</td>\n",
       "      <td>im104_r</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          is_novel image_name\n",
       "stimulus_presentations_id                    \n",
       "120                          False    im111_r\n",
       "121                          False    im111_r\n",
       "122                          False    im111_r\n",
       "123                          False    im111_r\n",
       "124                          False    im111_r\n",
       "125                           True    im104_r\n",
       "126                           True    im104_r\n",
       "127                           True    im104_r\n",
       "128                           True    im104_r\n",
       "129                           True    im104_r"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stimulus_presentations[120:130][['is_novel', 'image_name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 6.1:</b> Grab the active behavior portion of this updated stimulus table. Call the new table <code>task_stim_table</code>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grab the portion of the stim table that describes the active behavior stimuli\n",
    "task_stim_table = stimulus_presentations[stimulus_presentations['active']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "This table stores all of the stimulus info we'll need to make our PSTHs for novel and familiar change stimuli. Now we'll need to get the unit spiking data and organize it into a useful format.  \n",
    "  \n",
    "<p>\n",
    "<p>For this (and other) analyses, organizing the data into a 3D array can make things much more convenient. This 3D array will have shape (neurons, trial time, trials) and will store the spike counts for every neuron and time bin in every trial. For our purposes, we will define a trial as an image presentation (ie, every flash in the stim table will be one 'trial' for our array). So our array will be of shape (neurons in V1, flash time, number of image flashes).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/8e/45/8e4554f8-029f-43dd-badd-32d77d48898a/tensor_diagram.png\"\n",
    "     width=\"400\" \n",
    "     height=\"500\" />\n",
    "     \n",
    "Williams et al. (2018). Neuron, 98(6): 1099-1115"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "To make this 3D array, first we'll need to get the spike times for VISp units during this experiment:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 6.2:</b> Filter the units dataframe for only good units. Use the following quality criteria:\n",
    "            <p> &emsp; 1) <code>quality</code> == 'good'\n",
    "            <p> &emsp; 2) <code>snr</code> > 1\n",
    "            <p> &emsp; 3) <code>isi_violations</code> < 1  \n",
    "            <p> Call this new dataframe <code>good_units</code>                                    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter units by the quality criteria\n",
    "good_units = units[(units['quality']=='good') &\n",
    "                   (units['snr']>1) &\n",
    "                   (units['isi_violations']<1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now let's grab the good units in VISp\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now grab the VISp units from this dataframe by filtering on the structure_acronym column\n",
    "visp_units = good_units[good_units['structure_acronym']=='VISp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "We've provided a convenient function to help make our 3D array. Let's look at it below:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_neuron_time_trials_array(units, spike_times, stim_table, \n",
    "                                   time_before, trial_duration,\n",
    "                                   bin_size=0.001):\n",
    "    '''\n",
    "    Function to make a 3D array with dimensions [neurons, time bins, trials] to store\n",
    "    the spike counts for stimulus presentation trials. \n",
    "    INPUTS:\n",
    "        units: dataframe with unit info (same form as session.units table)\n",
    "        stim_table: dataframe whose indices are trial ids and containing a\n",
    "            'start_time' column indicating when each trial began\n",
    "        time_before: seconds to take before each start_time in the stim_table\n",
    "        trial_duration: total time in seconds to take for each trial\n",
    "        bin_size: bin_size in seconds used to bin spike counts \n",
    "    OUTPUTS:\n",
    "        unit_array: 3D array storing spike counts. The value in [i,j,k] \n",
    "            is the spike count for neuron i at time bin j in the kth trial.\n",
    "        time_vector: vector storing the trial timestamps for the time bins\n",
    "    '''\n",
    "    # Get dimensions of output array\n",
    "    neuron_number = len(units)\n",
    "    trial_number = len(stim_table)\n",
    "    num_time_bins = int(trial_duration/bin_size)\n",
    "    \n",
    "    # Initialize array\n",
    "    unit_array = np.zeros((neuron_number, num_time_bins, trial_number))\n",
    "    \n",
    "    # Loop through units and trials and store spike counts for every time bin\n",
    "    for u_counter, (iu, unit) in enumerate(units.iterrows()):\n",
    "        \n",
    "        # grab spike times for this unit\n",
    "        unit_spike_times = spike_times[iu]\n",
    "        \n",
    "        # now loop through trials and make a PSTH for this unit for every trial\n",
    "        for t_counter, (it, trial) in enumerate(stim_table.iterrows()):\n",
    "            trial_start = trial.start_time - time_before\n",
    "            unit_array[u_counter, :, t_counter] = makePSTH(unit_spike_times, \n",
    "                                                            [trial_start], \n",
    "                                                            trial_duration, \n",
    "                                                            binSize=bin_size)[0]\n",
    "    \n",
    "    # Make the time vector that will label the time axis\n",
    "    time_vector = np.arange(num_time_bins)*bin_size - time_before\n",
    "    \n",
    "    return unit_array, time_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make 3D data array\n",
    "time_before_flash = 1.0\n",
    "trial_duration = 2.0\n",
    "bin_size = 0.001\n",
    "flash_stim_array, time = make_neuron_time_trials_array(visp_units, spike_times,\n",
    "                                                       task_stim_table, time_before_flash, \n",
    "                                                       trial_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 2000, 4804)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flash_stim_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<p>Now let's use our 3D array to find the change responses to novel and familiar images. We'll do this by: \n",
    "    <p>&emsp; 1) Finding the trial ids for our trials of interest, and  \n",
    "    <p>&emsp; 2) Using those ids to index the 'trials' dimension of our array\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 6.3:</b> Get the trial ids for the famliar and novel image changes. We'll need to:\n",
    "       <p>&emsp; 1) Filter the <code>task_stim_table</code> for changes (using <code>is_change</code>)\n",
    "       <p>&emsp; 2) Find the novel and familiar changes (using our new <code>is_novel</code> column). Store these in two new dataframes called <code>novel_change_flashes</code> and <code>familiar_change_flashes</code> \n",
    "       <p>&emsp; 3) Find the indices in our 3D array that correspond to the novel/familiar change stimuli\n",
    "           \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the task_stim_table for change stimuli\n",
    "change_flashes = task_stim_table[task_stim_table.is_change]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the novel_changes and familiar_changes dataframes\n",
    "novel_change_flashes = change_flashes[change_flashes.is_novel==True]\n",
    "familiar_change_flashes = change_flashes[change_flashes.is_novel==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find indices for novel and familiar change flashes in our 3D array\n",
    "novel_flash_ids = novel_change_flashes.index.values\n",
    "novel_array_indices = task_stim_table.index.get_indexer(novel_flash_ids)\n",
    "\n",
    "familiar_flash_ids = familiar_change_flashes.index.values\n",
    "familiar_array_indices = task_stim_table.index.get_indexer(familiar_flash_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "Now we can use these indices to plot the PSTHs across V1 for familiar and novel change stimuli!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f918a3bd8e0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2)\n",
    "fig.set_size_inches([10, 4])\n",
    "fig.suptitle('VISp')\n",
    "\n",
    "# Loop through our familiar and novel array indices\n",
    "for array_flash_indices in [familiar_array_indices, novel_array_indices]:\n",
    "    \n",
    "    # Use these indices to select the corresponding data from our data array\n",
    "    relevant_trials = flash_stim_array[:, :, array_flash_indices]\n",
    "    \n",
    "    # We can use numpy mean to average over units and trials (the 0th and 2nd dimension of our array)\n",
    "    mean_over_neurons_trials = np.mean(relevant_trials, axis=(0, 2))\n",
    "    \n",
    "    # Now plot. We're plotting twice to show two time scales.\n",
    "    xlims = ([-0.25, 0.5], [0, 0.1])\n",
    "    for ax, xlim in zip(axes, xlims):\n",
    "        ax.plot(time, mean_over_neurons_trials)\n",
    "        ax.set_xlim(xlim)\n",
    "        ax.set_xlabel('time from change (s)')\n",
    "        ax.set_ylabel('firing rate (Hz)')\n",
    "\n",
    "axes[1].set_title('inset')\n",
    "axes[0].legend(['Familiar', 'Novel'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "\n",
    "<h1> How does novelty impact mouse behavior? </h1>\n",
    "<p>    \n",
    "<p> It looks like novelty can have a pretty dramatic impact on VISp visual responses. What about the behavior of the mouse? Let's calculate hit rates for novel and familiar changes during this session.\n",
    "    \n",
    "<h2> Calculating hit rates for novel and familiar images </h2>\n",
    "<p>    \n",
    "<p> Let's use the trials table to calculate the hit rates for the novel and familiar images. First, we'll need to annotate the trials dataframe so we know which trials had novel and familiar change stimuli.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First, let's annotate the trials by the novelty of their change image using the getImageNovelty function\n",
    "is_novel = trials.apply(lambda row: getImageNovelty(row['change_image_name'], session_id, \n",
    "                                                    ecephys_sessions_table), axis=1)\n",
    "\n",
    "trials['is_novel_change_image'] = is_novel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 7.1:</b> Filter the trials table for <code>go</code> trials, since these are the ones with change stimuli. Call the new dataframe <code>go_trials</code>.\n",
    "           \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "go_trials = trials[trials['go']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 7.2:</b> Use the <code>groupby</code> method on the <code>change_image_name</code> column to:\n",
    "       <p> 1) count the total number of go trials for each image\n",
    "       <p> 2) count the total number of hits for each image\n",
    "    <p> You can accomplish both of these with one line. It will look like:\n",
    "    <p> <code>go_trials.groupby(?).agg(?)</code>\n",
    "    <p> What column name should go in the first '?'\n",
    "    <p> What function should go in the second '?'\n",
    "    <p> Store the results in a dataframe called <code>image_go_data</code>\n",
    "           \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>stimulus_change</th>\n",
       "      <th>change_time_no_display_delay</th>\n",
       "      <th>go</th>\n",
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       "      <th>aborted</th>\n",
       "      <th>auto_rewarded</th>\n",
       "      <th>change_frame</th>\n",
       "      <th>trial_length</th>\n",
       "      <th>is_novel_change_image</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change_image_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>im114_r</th>\n",
       "      <td>63861.33726</td>\n",
       "      <td>64176.40990</td>\n",
       "      <td>36</td>\n",
       "      <td>64022.77149</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    start_time    stop_time  stimulus_change  \\\n",
       "change_image_name                                              \n",
       "im005_r            66278.31651  66601.42743               37   \n",
       "im024_r            70498.11635  70821.67676               38   \n",
       "im034_r            61094.10692  61403.95666               36   \n",
       "im083_r            67113.28172  67418.37764               37   \n",
       "im087_r            66328.95456  66630.59603               36   \n",
       "im104_r            73963.47882  74310.99703               38   \n",
       "im111_r            68966.89556  69298.28168               37   \n",
       "im114_r            63861.33726  64176.40990               36   \n",
       "\n",
       "                   change_time_no_display_delay  go  catch  response_time  \\\n",
       "change_image_name                                                           \n",
       "im005_r                             66443.48397  37      0    42653.39982   \n",
       "im024_r                             70659.51601  38      0    42258.99858   \n",
       "im034_r                             61250.28807  36      0    37536.47135   \n",
       "im083_r                             67260.43538  37      0    10239.68140   \n",
       "im087_r                             66476.87455  36      0    38704.83252   \n",
       "im104_r                             74148.90177  38      0    53014.26438   \n",
       "im111_r                             69140.35637  37      0    18331.06582   \n",
       "im114_r                             64022.77149  36      0    29520.72484   \n",
       "\n",
       "                   reward_time  reward_volume  hit  false_alarm  miss  \\\n",
       "change_image_name                                                       \n",
       "im005_r            39849.35887          0.066   22            0    15   \n",
       "im024_r            36948.48343          0.066   22            0    16   \n",
       "im034_r            34718.01143          0.072   24            0    12   \n",
       "im083_r             7313.64286          0.015    5            0    32   \n",
       "im087_r            32105.18421          0.057   19            0    17   \n",
       "im104_r            49252.35657          0.075   25            0    13   \n",
       "im111_r            15800.14059          0.015    5            0    32   \n",
       "im114_r            24792.64106          0.051   17            0    19   \n",
       "\n",
       "                   correct_reject  aborted  auto_rewarded  change_frame  \\\n",
       "change_image_name                                                         \n",
       "im005_r                         0        0              0     3929475.0   \n",
       "im024_r                         0        0              0     4180774.0   \n",
       "im034_r                         0        0              0     3619610.0   \n",
       "im083_r                         0        0              0     3978451.0   \n",
       "im087_r                         0        0              0     3932931.0   \n",
       "im104_r                         0        0              0     4389965.0   \n",
       "im111_r                         0        0              0     4091158.0   \n",
       "im114_r                         0        0              0     3785808.0   \n",
       "\n",
       "                   trial_length  is_novel_change_image  \n",
       "change_image_name                                       \n",
       "im005_r               323.11092                     37  \n",
       "im024_r               323.56041                     38  \n",
       "im034_r               309.84974                     36  \n",
       "im083_r               305.09592                      0  \n",
       "im087_r               301.64147                     36  \n",
       "im104_r               347.51821                     38  \n",
       "im111_r               331.38612                      0  \n",
       "im114_r               315.07264                     36  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_go_data = go_trials.groupby('change_image_name').agg(sum)\n",
    "image_go_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['start_time', 'stop_time', 'stimulus_change',\n",
       "       'change_time_no_display_delay', 'go', 'catch', 'response_time',\n",
       "       'reward_time', 'reward_volume', 'hit', 'false_alarm', 'miss',\n",
       "       'correct_reject', 'aborted', 'auto_rewarded', 'change_frame',\n",
       "       'trial_length', 'is_novel_change_image'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_go_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "As you can see, many of these columns aren't very meaningful. Let's select only the <code>stimulus_change</code>, <code>hit</code> and <code>is_novel_change_image</code> columns to make this dataframe easier to work with:\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stimulus_change</th>\n",
       "      <th>hit</th>\n",
       "      <th>is_novel_change_image</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change_image_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>im005_r</th>\n",
       "      <td>37</td>\n",
       "      <td>22</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im024_r</th>\n",
       "      <td>38</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im034_r</th>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im083_r</th>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im087_r</th>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im104_r</th>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im111_r</th>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>im114_r</th>\n",
       "      <td>36</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   stimulus_change  hit  is_novel_change_image\n",
       "change_image_name                                             \n",
       "im005_r                         37   22                     37\n",
       "im024_r                         38   22                     38\n",
       "im034_r                         36   24                     36\n",
       "im083_r                         37    5                      0\n",
       "im087_r                         36   19                     36\n",
       "im104_r                         38   25                     38\n",
       "im111_r                         37    5                      0\n",
       "im114_r                         36   17                     36"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_go_data = image_go_data[['stimulus_change', 'hit', 'is_novel_change_image']]\n",
    "image_go_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "Let's clean this dataframe up a bit by setting all of the rows with <code>is_novel_change_image</code>>0 to <code>True</code> and <code>is_novel_change_image</code>==0 to <code>False</code>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7s/q3zz_qj910x07vwrdqkcgczr0000gp/T/ipykernel_59666/1593584136.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  image_go_data.loc[image_go_data['is_novel_change_image']>0, 'is_novel_change_image'] = True\n"
     ]
    }
   ],
   "source": [
    "image_go_data.loc[image_go_data['is_novel_change_image']>0, 'is_novel_change_image'] = True\n",
    "image_go_data.loc[image_go_data['is_novel_change_image']==0, 'is_novel_change_image'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #DFF0D8; border-radius: 3px; padding: 10px;\">\n",
    "    <p><b>Task 7.3:</b> Use this new table to calculate the hit rates for every image. Store these in a new column called <code>hit_rate</code>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7s/q3zz_qj910x07vwrdqkcgczr0000gp/T/ipykernel_59666/1506095693.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  image_go_data['hit_rate'] = hit_rates\n"
     ]
    }
   ],
   "source": [
    "hit_rates = image_go_data.hit.values/image_go_data.stimulus_change.values\n",
    "image_go_data['hit_rate'] = hit_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_010ed_row0_col3 {\n",
       "  background-color: #ff4444;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_010ed_row1_col3 {\n",
       "  background-color: #ff5454;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_010ed_row2_col3 {\n",
       "  background-color: #ff0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_010ed_row3_col3, #T_010ed_row6_col3 {\n",
       "  background-color: #0000ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_010ed_row4_col3 {\n",
       "  background-color: #ff8484;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_010ed_row5_col3 {\n",
       "  background-color: #ff0808;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_010ed_row7_col3 {\n",
       "  background-color: #ffbaba;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_010ed\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_010ed_level0_col0\" class=\"col_heading level0 col0\" >stimulus_change</th>\n",
       "      <th id=\"T_010ed_level0_col1\" class=\"col_heading level0 col1\" >hit</th>\n",
       "      <th id=\"T_010ed_level0_col2\" class=\"col_heading level0 col2\" >is_novel_change_image</th>\n",
       "      <th id=\"T_010ed_level0_col3\" class=\"col_heading level0 col3\" >hit_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >change_image_name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row0\" class=\"row_heading level0 row0\" >im005_r</th>\n",
       "      <td id=\"T_010ed_row0_col0\" class=\"data row0 col0\" >37</td>\n",
       "      <td id=\"T_010ed_row0_col1\" class=\"data row0 col1\" >22</td>\n",
       "      <td id=\"T_010ed_row0_col2\" class=\"data row0 col2\" >True</td>\n",
       "      <td id=\"T_010ed_row0_col3\" class=\"data row0 col3\" >0.594595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row1\" class=\"row_heading level0 row1\" >im024_r</th>\n",
       "      <td id=\"T_010ed_row1_col0\" class=\"data row1 col0\" >38</td>\n",
       "      <td id=\"T_010ed_row1_col1\" class=\"data row1 col1\" >22</td>\n",
       "      <td id=\"T_010ed_row1_col2\" class=\"data row1 col2\" >True</td>\n",
       "      <td id=\"T_010ed_row1_col3\" class=\"data row1 col3\" >0.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row2\" class=\"row_heading level0 row2\" >im034_r</th>\n",
       "      <td id=\"T_010ed_row2_col0\" class=\"data row2 col0\" >36</td>\n",
       "      <td id=\"T_010ed_row2_col1\" class=\"data row2 col1\" >24</td>\n",
       "      <td id=\"T_010ed_row2_col2\" class=\"data row2 col2\" >True</td>\n",
       "      <td id=\"T_010ed_row2_col3\" class=\"data row2 col3\" >0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row3\" class=\"row_heading level0 row3\" >im083_r</th>\n",
       "      <td id=\"T_010ed_row3_col0\" class=\"data row3 col0\" >37</td>\n",
       "      <td id=\"T_010ed_row3_col1\" class=\"data row3 col1\" >5</td>\n",
       "      <td id=\"T_010ed_row3_col2\" class=\"data row3 col2\" >False</td>\n",
       "      <td id=\"T_010ed_row3_col3\" class=\"data row3 col3\" >0.135135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row4\" class=\"row_heading level0 row4\" >im087_r</th>\n",
       "      <td id=\"T_010ed_row4_col0\" class=\"data row4 col0\" >36</td>\n",
       "      <td id=\"T_010ed_row4_col1\" class=\"data row4 col1\" >19</td>\n",
       "      <td id=\"T_010ed_row4_col2\" class=\"data row4 col2\" >True</td>\n",
       "      <td id=\"T_010ed_row4_col3\" class=\"data row4 col3\" >0.527778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row5\" class=\"row_heading level0 row5\" >im104_r</th>\n",
       "      <td id=\"T_010ed_row5_col0\" class=\"data row5 col0\" >38</td>\n",
       "      <td id=\"T_010ed_row5_col1\" class=\"data row5 col1\" >25</td>\n",
       "      <td id=\"T_010ed_row5_col2\" class=\"data row5 col2\" >True</td>\n",
       "      <td id=\"T_010ed_row5_col3\" class=\"data row5 col3\" >0.657895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row6\" class=\"row_heading level0 row6\" >im111_r</th>\n",
       "      <td id=\"T_010ed_row6_col0\" class=\"data row6 col0\" >37</td>\n",
       "      <td id=\"T_010ed_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "      <td id=\"T_010ed_row6_col2\" class=\"data row6 col2\" >False</td>\n",
       "      <td id=\"T_010ed_row6_col3\" class=\"data row6 col3\" >0.135135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_010ed_level0_row7\" class=\"row_heading level0 row7\" >im114_r</th>\n",
       "      <td id=\"T_010ed_row7_col0\" class=\"data row7 col0\" >36</td>\n",
       "      <td id=\"T_010ed_row7_col1\" class=\"data row7 col1\" >17</td>\n",
       "      <td id=\"T_010ed_row7_col2\" class=\"data row7 col2\" >True</td>\n",
       "      <td id=\"T_010ed_row7_col3\" class=\"data row7 col3\" >0.472222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9111ac8490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can display the results with cells color-coded by their hit rate to make it easier to see what's going on here\n",
    "display(image_go_data.style.background_gradient(cmap='bwr', subset='hit_rate'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border-left: 3px solid #000; padding: 1px; padding-left: 10px; background: #F0FAFF; \">\n",
    "Now let's plot this data. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(14, 6)\n",
    "gs = GridSpec(2, 8, figure=fig)\n",
    "\n",
    "hit_rate_ax = fig.add_subplot(gs[0, :])\n",
    "colors = ['r', 'b']\n",
    "image_order = []\n",
    "for isnovel, color in zip([True, False], colors):\n",
    "    rows = image_go_data[image_go_data['is_novel_change_image']==isnovel]\n",
    "    rows = rows.sort_values('hit_rate', ascending=False)\n",
    "    \n",
    "    hit_rates = rows['hit_rate']\n",
    "    hit_rate_ax.plot(hit_rates, color+'o-')\n",
    "    \n",
    "    image_order.extend(rows.index.values)\n",
    "    \n",
    "hit_rate_ax.set_ylabel('Hit Rate')\n",
    "hit_rate_ax.legend(['Novel', 'Familiar'])\n",
    "\n",
    "# Let's add the images themselves under each dot.    \n",
    "for ind, im in enumerate(image_order):\n",
    "    im_ax = fig.add_subplot(gs[1, ind])\n",
    "    im_ax.imshow(session.stimulus_templates['warped'][im], cmap='gray')\n",
    "    im_ax.axes.xaxis.set_visible(False)\n",
    "    im_ax.axes.yaxis.set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}