notebooks/Economo-2018-examples.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Economo et al., 2018 - A DataJoint example\n",
    "This notebook presents data and results associated with the following papers:\n",
    "\n",
    ">Michael N. Economo, Sarada Viswanathan, Bosiljka Tasic, Erhan Bas, Johan Winnubst, Vilas Menon, Lucas T. Graybuck,Thuc Nghi Nguyen, Kimberly A. Smith, Zizhen Yao, Lihua Wang, Charles R. Gerfen, Jayaram Chandrashekar, Hongkui Zeng, Loren L. Looger & Karel Svoboda. \"Distinct descending motor cortex\n",
    "pathways and their roles in movement\" (2018) Nature (https://doi.org/10.1038/s41586-018-0642-9)\n",
    "\n",
    "The study identified two types of pyramidal track (PT) neuron in the mouse motor cortex, referred to as PT<sub>upper</sub> and PT<sub>lower</sub>. Further investigation using extracellular recordings in the anterior lateral motor cortex (ALM) during a delay-response task, the study revealed specialized roles for the two neuron types: i) PT<sub>upper</sub> neurons are preferentially involed in motor planning, ii) PT<sub>lower</sub> neurons are more involved in movement execution.  \n",
    "\n",
    "A ***DataJoint*** data pipeline has been constructed for this study, with the presented data ingested into this pipeline. This notebook demonstrates the queries, processing, and reproduction of several figures from the paper. From the pipeline, export capability to ***NWB 2.0*** format is also available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data\n",
    "\n",
    "The dataset comprises of extracellular recordings and spike sorted results of the mouse's ALM during a delay-response task. The behavior data includes detailed description of the trial structure (e.g. trial timing, trial instruction, trial response, etc.) and the timing of the lick events (e.g. lick left, lick right onset). \n",
    "\n",
    "Original data is publically available at: doi: 10.25378/janelia.7007846\n",
    "\n",
    "The data in original MATLAB format (.mat) have been ingested into a DataJoint data pipeline presented below. \n",
    "\n",
    "Data are also exported into NWB 2.0 format. See NWB export code [here](../scripts/datajoint_to_nwb.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting root@127.0.0.1:3306\n"
     ]
    }
   ],
   "source": [
    "# import necessary Python packages including DataJoint\n",
    "\n",
    "from datetime import datetime\n",
    "import os\n",
    "os.chdir('..')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "\n",
    "import datajoint as dj\n",
    "from pipeline import (reference, subject, acquisition, analysis,\n",
    "                      extracellular, behavior, utilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The architecture of the data pipeline\n",
    "\n",
    "This data pipeline is comprised of three core components:\n",
    "+ **subject**: tables for meta information, including subjects and their genetic information, actions on the subjects.\n",
    "+ **acquisition**: tables to capture the workflow of the experiment, including Sessions, Behavior Trials, different experimental events (e.g. sample, delay, response periods)\n",
    "+ **extracellular**: tables to capture the extracellular data and clustering results (e.g. units and spike times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In DataJoint, related tables are organized together into a **schema**, and a data pipeline typically consists of tables across multiple schemas connected to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`subject` schema contains various tables representing information and meta-information about the experimental animals, as well as information about genetic backgrounds and virus injected for the experiment. The DataJoint *Diagram* below shows all tables found inside the `subject` schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "<g id=\"a_node5\"><a xlink:title=\"subject_id           \r",
       "------------------------------\r",
       "→ subject.Species\r",
       "→ reference.AnimalSource\r",
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       "------------------------------\r",
       "→ subject.Allele\r",
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       "</g>\n",
       "<!-- subject.Allele&#45;&gt;subject.AlleleAlias -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>subject.Allele-&gt;subject.AlleleAlias</title>\n",
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       "</g>\n",
       "<!-- reference.AnimalSource -->\n",
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   "source": [
    "dj.Diagram(subject) - 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, `Subject` table contains information about each and every mouse in the experiment, capturing information such as unique subject ID and animal sourcce."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "                                <p id=\"primary\">subject_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of the subject (e.g. ANM244028)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">species</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">animal_source</p>\n",
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       "                                <p id=\"nonprimary\">subject_description</p>\n",
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       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm369962</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369963</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369964</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm372793</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm372794</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm372795</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm372797</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 12</p></div>\n",
       "            "
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       "*subject_id    species        animal_source  sex     date_of_birth  subject_descri\n",
       "+------------+ +------------+ +------------+ +-----+ +------------+ +------------+\n",
       "anm369962      Mus musculus   N/A            U       None           None          \n",
       "anm369963      Mus musculus   N/A            U       None           None          \n",
       "anm369964      Mus musculus   N/A            U       None           None          \n",
       "anm372793      Mus musculus   N/A            U       None           None          \n",
       "anm372794      Mus musculus   N/A            U       None           None          \n",
       "anm372795      Mus musculus   N/A            U       None           None          \n",
       "anm372797      Mus musculus   N/A            U       None           None          \n",
       "   ...\n",
       " (Total: 12)"
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     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject.Subject()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acquisition and behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information pertaining to conducting the experiment as well as data and meta-data acquired during the experiment (except for electrophysiology) are found among three schemas: `acquisition`, `stimulation` and `behavior`. Below, tables across all three schemas are depicted in the DataJoint Diagram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "<g id=\"a_node10\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
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       "<g class=\"node\" id=\"node4\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
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       "</g>\n",
       "<!-- acquisition.Session.Experimenter -->\n",
       "<g class=\"node\" id=\"node5\"><title>acquisition.Session.Experimenter</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Experimenter\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"278,-137 116,-137 116,-118 278,-118 278,-137\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"197\" y=\"-125\">acquisition.Session.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node6\"><title>subject.Subject</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"subject_id           \r",
       "------------------------------\r",
       "→ subject.Species\r",
       "→ reference.AnimalSource\r",
       "sex=&quot;U&quot;              \r",
       "date_of_birth=null   \r",
       "subject_description=null \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"312.5,-287 217.5,-287 217.5,-252 312.5,-252 312.5,-287\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"225.5\" y=\"-267.4\">subject.Subject</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node11\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "session_id           \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"324.5,-216 205.5,-216 205.5,-181 324.5,-181 324.5,-216\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"213.5\" y=\"-196.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;acquisition.Session -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>subject.Subject-&gt;acquisition.Session</title>\n",
       "<path d=\"M265,-251.797C265,-240.949 265,-226.867 265,-216.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.LickTimes -->\n",
       "<g class=\"node\" id=\"node7\"><title>behavior.LickTimes</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "lick_left_times      \r",
       "lick_right_times     \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"415.5,-145 296.5,-145 296.5,-110 415.5,-110 415.5,-145\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"356\" y=\"-124.4\">behavior.LickTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node8\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"550,-145 434,-145 434,-110 550,-110 550,-145\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"492\" y=\"-124.4\">acquisition.TrialSet</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M492,-109.831C492,-98.2612 492,-83.3217 492,-74.0782\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.TrialResponse -->\n",
       "<g class=\"node\" id=\"node9\"><title>reference.TrialResponse</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"trial_response       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"689.5,-145 568.5,-145 568.5,-110 689.5,-110 689.5,-145\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"576.5\" y=\"-126\">reference.TrialResponse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialResponse&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>reference.TrialResponse-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M591.926,-109.993C565.934,-98.4194 532.234,-83.4146 511.37,-74.1245\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>acquisition.Session-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M248.539,-180.797C235.018,-167.077 216.4,-148.185 205.638,-137.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;behavior.LickTimes -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>acquisition.Session-&gt;behavior.LickTimes</title>\n",
       "<path d=\"M287.029,-180.797C301.336,-169.949 319.907,-155.867 334.174,-145.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
       "<path d=\"M319.372,-180.973C355.18,-170.088 401.881,-155.893 437.679,-145.012\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- reference.Experimenter -->\n",
       "<g class=\"node\" id=\"node12\"><title>reference.Experimenter</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"experimenter         \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"187.5,-216 70.5,-216 70.5,-181 187.5,-181 187.5,-216\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"78.5\" y=\"-197\">reference.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Experimenter&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>reference.Experimenter-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M145.461,-180.797C158.982,-167.077 177.6,-148.185 188.362,-137.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x189320ff160>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(acquisition) - 1 + dj.Diagram(behavior)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Session` tables contains information about individual experiment session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        \n",
       "        <style type=\"text/css\">\n",
       "            .Relation{\n",
       "                border-collapse:collapse;\n",
       "            }\n",
       "            .Relation th{\n",
       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
       "            }\n",
       "            .Relation td{\n",
       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
       "            }\n",
       "            .Relation tr:nth-child(odd){\n",
       "                background: #ffffff;\n",
       "            }\n",
       "            .Relation tr:nth-child(even){\n",
       "                background: #f3f1ff;\n",
       "            }\n",
       "            /* Tooltip container */\n",
       "            .djtooltip {\n",
       "            }\n",
       "            /* Tooltip text */\n",
       "            .djtooltip .djtooltiptext {\n",
       "                visibility: hidden;\n",
       "                width: 120px;\n",
       "                background-color: black;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
       "                /* Position the tooltip text - see examples below! */\n",
       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        \n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">subject_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of the subject (e.g. ANM244028)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_time</p>\n",
       "                                <span class=\"djtooltiptext\">session time</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">session_directory</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">session_note</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">species</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">animal_source</p>\n",
       "                                <span class=\"djtooltiptext\">source of the animal, Jax, Charles River etc.</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">sex</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">date_of_birth</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">subject_description</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-10 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-13 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-14 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-15 00:00:00</td>\n",
       "<td>2</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-16 00:00:00</td>\n",
       "<td>3</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-17 00:00:00</td>\n",
       "<td>4</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>None</td>\n",
       "<td>None</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 65</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    session_direct session_note   species        animal_source  sex     date_of_birth  subject_descri\n",
       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----+ +------------+ +------------+\n",
       "anm369962      2017-03-09 00: 0                                            Mus musculus   N/A            U       None           None          \n",
       "anm369962      2017-03-10 00: 1                                            Mus musculus   N/A            U       None           None          \n",
       "anm369962      2017-03-13 00: 0                                            Mus musculus   N/A            U       None           None          \n",
       "anm369962      2017-03-14 00: 1                                            Mus musculus   N/A            U       None           None          \n",
       "anm369962      2017-03-15 00: 2                                            Mus musculus   N/A            U       None           None          \n",
       "anm369962      2017-03-16 00: 3                                            Mus musculus   N/A            U       None           None          \n",
       "anm369962      2017-03-17 00: 4                                            Mus musculus   N/A            U       None           None          \n",
       "   ...\n",
       " (Total: 65)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition.Session * subject.Subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracellular"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information related to extracellular electrophysiology is captured in the `extracellular` schema, including: probe, probe insertion location, unit, spikes, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 376)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-376 545.5,-376 545.5,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node1\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "unit_cell_type       \r",
       "unit_quality=&quot;&quot;      \r",
       "unit_depth           \r",
       "spike_times          \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"302,-159 138,-159 138,-124 302,-124 302,-159\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"146\" y=\"-139.4\">extracellular.UnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node12\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"220\" cy=\"-75\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"220\" y=\"-71.9\">extracellular.TrialSegmentedUnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M220,-123.89C220,-112.692 220,-98.1749 220,-88.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node2\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"120,-151 0,-151 0,-132 120,-132 120,-151\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"8\" y=\"-140\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M81.2468,-131.935C115.419,-118.159 181.467,-91.5334 208.27,-80.7285\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.PSTH -->\n",
       "<g class=\"node\" id=\"node3\"><title>extracellular.PSTH</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ extracellular.TrialSegmentedUnitSpikeTimes\r",
       "------------------------------\r",
       "psth                 \r",
       "psth_time            \r",
       "\">\n",
       "<ellipse cx=\"220\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"220\" y=\"-9.9\">extracellular.PSTH</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Shank -->\n",
       "<g class=\"node\" id=\"node4\"><title>reference.Probe.Shank</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ reference.Probe\r",
       "shank_id             \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"452,-293 336,-293 336,-274 452,-274 452,-293\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"394\" y=\"-281\">reference.Probe.Shank</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel -->\n",
       "<g class=\"node\" id=\"node7\"><title>reference.Probe.Channel</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ reference.Probe\r",
       "channel_id           \r",
       "------------------------------\r",
       "→ reference.Probe.Shank\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"541.5,-222 418.5,-222 418.5,-203 541.5,-203 541.5,-222\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"426.5\" y=\"-211\">reference.Probe.Channel</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Shank&#45;&gt;reference.Probe.Channel -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>reference.Probe.Shank-&gt;reference.Probe.Channel</title>\n",
       "<path d=\"M404.892,-273.761C421.434,-260.489 452.707,-235.398 469.196,-222.168\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node5\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"118,-230 2,-230 2,-195 118,-195 118,-230\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M60,-194.797C60,-181.077 60,-162.185 60,-151.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe -->\n",
       "<g class=\"node\" id=\"node6\"><title>reference.Probe</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"probe_name           \r",
       "channel_counts       \r",
       "------------------------------\r",
       "probe_type=&quot;&quot;        \r",
       "probe_desc=&quot;&quot;        \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"436.5,-372 351.5,-372 351.5,-337 436.5,-337 436.5,-372\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"359.5\" y=\"-353\">reference.Probe</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;reference.Probe.Shank -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>reference.Probe-&gt;reference.Probe.Shank</title>\n",
       "<path d=\"M394,-336.797C394,-323.077 394,-304.185 394,-293.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;reference.Probe.Channel -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>reference.Probe-&gt;reference.Probe.Channel</title>\n",
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       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node9\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.BrainLocation\r",
       "insertion_depth      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"296.5,-230 139.5,-230 139.5,-195 296.5,-195 296.5,-230\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"147.5\" y=\"-210.4\">extracellular.ProbeInsertion</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>reference.Probe-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M351.356,-349.85C292.106,-343.794 189.594,-329.416 167,-301 148.944,-278.292 175.35,-248.506 196.404,-230.2\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>reference.Probe.Channel-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M447.858,-202.97C406.691,-192.045 334.448,-172.873 282.327,-159.041\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.BrainLocation -->\n",
       "<g class=\"node\" id=\"node8\"><title>reference.BrainLocation</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"brain_region         \r",
       "brain_subregion=&quot;N/A&quot; \r",
       "→ reference.CorticalLayer\r",
       "→ reference.Hemisphere\r",
       "------------------------------\r",
       "brain_location_full_name=&quot;N/A&quot; \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"318,-301 176,-301 176,-266 318,-266 318,-301\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"184\" y=\"-281.4\">reference.BrainLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>reference.BrainLocation-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M239.98,-265.797C235.42,-254.949 229.502,-240.867 224.955,-230.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M218.484,-194.797C218.799,-183.949 219.207,-169.867 219.52,-159.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node10\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "session_id           \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"119.5,-301 0.5,-301 0.5,-266 119.5,-266 119.5,-301\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"8.5\" y=\"-281.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
       "<path d=\"M60,-265.797C60,-254.949 60,-240.867 60,-230.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>acquisition.Session-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M97.8449,-265.973C122.768,-255.088 155.274,-240.893 180.19,-230.012\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node11\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"trial_seg_setting    \r",
       "------------------------------\r",
       "→ reference.ExperimentalEvent\r",
       "pre_stim_duration    \r",
       "post_stim_duration   \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"484,-159 320,-159 320,-124 484,-124 484,-159\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"328\" y=\"-140\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>analysis.TrialSegmentationSetting-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M355.13,-123.89C313.844,-109.258 256.566,-88.959 231.985,-80.2476\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes&#45;&gt;extracellular.PSTH -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>extracellular.TrialSegmentedUnitSpikeTimes-&gt;extracellular.PSTH</title>\n",
       "<path d=\"M220,-61.8581C220,-51.4484 220,-36.5283 220,-26.1238\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x18932078a58>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(extracellular) - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we can query and display the `extracellular.UnitSpikeTimes` table for all neurons, exclude neuron type of \"unidentified\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">subject_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of the subject (e.g. ANM244028)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_time</p>\n",
       "                                <span class=\"djtooltiptext\">session time</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">probe_name</p>\n",
       "                                <span class=\"djtooltiptext\">String naming probe model</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">channel_counts</p>\n",
       "                                <span class=\"djtooltiptext\">number of channels in the probe</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">brain_region</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">brain_subregion</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">cortical_layer</p>\n",
       "                                <span class=\"djtooltiptext\">layer within cortex</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">hemisphere</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">insertion_depth</p>\n",
       "                                <span class=\"djtooltiptext\">(um)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">unit_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">channel_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of a channel on the probe</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_cell_type</p>\n",
       "                                <span class=\"djtooltiptext\">depending on the animal (which cell-type being tagged)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_quality</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_depth</p>\n",
       "                                <span class=\"djtooltiptext\">(um)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spike_times</p>\n",
       "                                <span class=\"djtooltiptext\">(s) time of each spike, with respect to the start of session</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1225.00</td>\n",
       "<td>1001</td>\n",
       "<td>24</td>\n",
       "<td>PTlower</td>\n",
       "<td>Excellent</td>\n",
       "<td>1005.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1225.00</td>\n",
       "<td>1002</td>\n",
       "<td>45</td>\n",
       "<td>PTlower</td>\n",
       "<td>Good</td>\n",
       "<td>730.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1225.00</td>\n",
       "<td>1003</td>\n",
       "<td>55</td>\n",
       "<td>PTlower</td>\n",
       "<td>Good</td>\n",
       "<td>980.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-10 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>right</td>\n",
       "<td>1260.00</td>\n",
       "<td>1001</td>\n",
       "<td>48</td>\n",
       "<td>PTlower</td>\n",
       "<td>Excellent</td>\n",
       "<td>840.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-10 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>right</td>\n",
       "<td>1260.00</td>\n",
       "<td>1002</td>\n",
       "<td>50</td>\n",
       "<td>PTlower</td>\n",
       "<td>Excellent</td>\n",
       "<td>890.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-10 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>right</td>\n",
       "<td>1260.00</td>\n",
       "<td>1003</td>\n",
       "<td>49</td>\n",
       "<td>PTlower</td>\n",
       "<td>Good</td>\n",
       "<td>865.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-10 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>right</td>\n",
       "<td>1260.00</td>\n",
       "<td>1004</td>\n",
       "<td>52</td>\n",
       "<td>PTlower</td>\n",
       "<td>Excellent</td>\n",
       "<td>940.0</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 151</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *probe_name    *channel_count *brain_region  *brain_subregi *cortical_laye *hemisphere    *insertion_dep *unit_id    channel_id     unit_cell_type unit_quality   unit_depth     spike_time\n",
       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +--------+\n",
       "anm369962      2017-03-09 00: 0              H-129          64             ALM            N/A            5              left           1225.00        1001        24             PTlower        Excellent      1005.0         =BLOB=    \n",
       "anm369962      2017-03-09 00: 0              H-129          64             ALM            N/A            5              left           1225.00        1002        45             PTlower        Good           730.0          =BLOB=    \n",
       "anm369962      2017-03-09 00: 0              H-129          64             ALM            N/A            5              left           1225.00        1003        55             PTlower        Good           980.0          =BLOB=    \n",
       "anm369962      2017-03-10 00: 1              H-129          64             ALM            N/A            5              right          1260.00        1001        48             PTlower        Excellent      840.0          =BLOB=    \n",
       "anm369962      2017-03-10 00: 1              H-129          64             ALM            N/A            5              right          1260.00        1002        50             PTlower        Excellent      890.0          =BLOB=    \n",
       "anm369962      2017-03-10 00: 1              H-129          64             ALM            N/A            5              right          1260.00        1003        49             PTlower        Good           865.0          =BLOB=    \n",
       "anm369962      2017-03-10 00: 1              H-129          64             ALM            N/A            5              right          1260.00        1004        52             PTlower        Excellent      940.0          =BLOB=    \n",
       "   ...\n",
       " (Total: 151)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracellular.UnitSpikeTimes & 'unit_cell_type != \"unidentified\"'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The entirety of the ***DataJoint*** pipeline for this study is captured in the figure below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-447 1431,-447 1431,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- 11 -->\n",
       "<g class=\"node\" id=\"node1\"><title>11</title>\n",
       "<ellipse cx=\"856.5\" cy=\"-141.5\" fill=\"#ff8800\" fill-opacity=\"0.501961\" rx=\"2\" ry=\"2\" stroke=\"#ff8800\" stroke-opacity=\"0.501961\"/>\n",
       "<text fill=\"#ff8800\" font-family=\"arial\" font-size=\"1.00\" text-anchor=\"middle\" x=\"856.5\" y=\"-141.2\">11</text>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.EventTime -->\n",
       "<g class=\"node\" id=\"node6\"><title>acquisition.TrialSet.EventTime</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
       "→ reference.ExperimentalEvent.proj(trial_event=&quot;event&quot;)\r",
       "------------------------------\r",
       "event_time=null      \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"1062,-84.5 915,-84.5 915,-65.5 1062,-65.5 1062,-84.5\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"988.5\" y=\"-72.5\">acquisition.TrialSet.EventTime</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;acquisition.TrialSet.EventTime -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>11-&gt;acquisition.TrialSet.EventTime</title>\n",
       "<path d=\"M857.982,-139.776C868.944,-134.419 937.902,-100.724 970.92,-84.5901\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.RealignedEvent.RealignedEventTime -->\n",
       "<g class=\"node\" id=\"node2\"><title>analysis.RealignedEvent.RealignedEventTime</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ analysis.RealignedEvent\r",
       "→ acquisition.TrialSet.EventTime\r",
       "------------------------------\r",
       "realigned_event_time=null \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"931,-22.5 718,-22.5 718,-3.5 931,-3.5 931,-22.5\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"824.5\" y=\"-10.5\">analysis.RealignedEvent.RealignedEventTime</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent -->\n",
       "<g class=\"node\" id=\"node3\"><title>reference.ExperimentalEvent</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"event                \r",
       "------------------------------\r",
       "description          \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"772.5,-230 632.5,-230 632.5,-195 772.5,-195 772.5,-230\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"640.5\" y=\"-211\">reference.ExperimentalEvent</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;11 -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>reference.ExperimentalEvent-&gt;11</title>\n",
       "<path d=\"M739.387,-194.973C781.387,-176.155 845.478,-147.438 855.236,-143.066\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node7\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"trial_seg_setting    \r",
       "------------------------------\r",
       "→ reference.ExperimentalEvent\r",
       "pre_stim_duration    \r",
       "post_stim_duration   \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"784.5,-159 620.5,-159 620.5,-124 784.5,-124 784.5,-159\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"628.5\" y=\"-140\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;analysis.TrialSegmentationSetting -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>reference.ExperimentalEvent-&gt;analysis.TrialSegmentationSetting</title>\n",
       "<path d=\"M702.5,-194.797C702.5,-183.949 702.5,-169.867 702.5,-159.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.LickTimes -->\n",
       "<g class=\"node\" id=\"node4\"><title>behavior.LickTimes</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "lick_left_times      \r",
       "lick_right_times     \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"614,-230 495,-230 495,-195 614,-195 614,-230\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"554.5\" y=\"-209.4\">behavior.LickTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel -->\n",
       "<g class=\"node\" id=\"node5\"><title>reference.Probe.Channel</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ reference.Probe\r",
       "channel_id           \r",
       "------------------------------\r",
       "→ reference.Probe.Shank\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"123,-222 0,-222 0,-203 123,-203 123,-222\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"8\" y=\"-211\">reference.Probe.Channel</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node24\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node24\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "unit_cell_type       \r",
       "unit_quality=&quot;&quot;      \r",
       "unit_depth           \r",
       "spike_times          \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"315.5,-159 151.5,-159 151.5,-124 315.5,-124 315.5,-159\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"159.5\" y=\"-139.4\">extracellular.UnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>reference.Probe.Channel-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M82.9361,-202.901C110.222,-191.954 157.944,-172.81 192.357,-159.005\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.EventTime&#45;&gt;analysis.RealignedEvent.RealignedEventTime -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>acquisition.TrialSet.EventTime-&gt;analysis.RealignedEvent.RealignedEventTime</title>\n",
       "<path d=\"M965.003,-65.4035C933.826,-53.9975 879.108,-33.9785 847.954,-22.5806\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node23\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node23\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"610.5\" cy=\"-75\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"610.5\" y=\"-71.9\">extracellular.TrialSegmentedUnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>analysis.TrialSegmentationSetting-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M678.808,-123.89C660.067,-110.751 634.809,-93.0426 620.852,-83.258\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.RealignedEvent -->\n",
       "<g class=\"node\" id=\"node32\"><title>analysis.RealignedEvent</title>\n",
       "<g id=\"a_node32\"><a xlink:title=\"→ analysis.TrialSegmentationSetting\r",
       "→ acquisition.TrialSet.Trial\r",
       "\">\n",
       "<ellipse cx=\"824.5\" cy=\"-75\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"824.5\" y=\"-71.9\">analysis.RealignedEvent</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;analysis.RealignedEvent -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>analysis.TrialSegmentationSetting-&gt;analysis.RealignedEvent</title>\n",
       "<path d=\"M733.918,-123.89C759.899,-110.154 795.327,-91.4233 813.2,-81.974\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node8\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"1048.5,-151 928.5,-151 928.5,-132 1048.5,-132 1048.5,-151\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"936.5\" y=\"-140\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;acquisition.TrialSet.EventTime -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>acquisition.TrialSet.Trial-&gt;acquisition.TrialSet.EventTime</title>\n",
       "<path d=\"M988.5,-131.801C988.5,-119.422 988.5,-96.8639 988.5,-84.5609\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M938.305,-131.935C850.141,-116.891 672.171,-86.5233 623.214,-78.1695\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;analysis.RealignedEvent -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>acquisition.TrialSet.Trial-&gt;analysis.RealignedEvent</title>\n",
       "<path d=\"M966.722,-131.935C931.695,-118.159 863.996,-91.5334 836.523,-80.7285\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.TrialType -->\n",
       "<g class=\"node\" id=\"node9\"><title>reference.TrialType</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"trial_type           \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1342.5,-230 1244.5,-230 1244.5,-195 1342.5,-195 1342.5,-230\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1252.5\" y=\"-211\">reference.TrialType</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialType&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>reference.TrialType-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M1244.2,-197.229C1241.26,-196.455 1238.34,-195.706 1235.5,-195 1165.29,-177.574 1082.98,-160.802 1033.33,-151.079\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Subject.Allele -->\n",
       "<g class=\"node\" id=\"node10\"><title>subject.Subject.Allele</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"→ subject.Subject\r",
       "→ subject.Allele\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"1177,-293 1066,-293 1066,-274 1177,-274 1177,-293\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"1121.5\" y=\"-281\">subject.Subject.Allele</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node11\"><title>subject.Subject</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"subject_id           \r",
       "------------------------------\r",
       "→ subject.Species\r",
       "→ reference.AnimalSource\r",
       "sex=&quot;U&quot;              \r",
       "date_of_birth=null   \r",
       "subject_description=null \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1169,-372 1074,-372 1074,-337 1169,-337 1169,-372\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1082\" y=\"-352.4\">subject.Subject</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;subject.Subject.Allele -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>subject.Subject-&gt;subject.Subject.Allele</title>\n",
       "<path d=\"M1121.5,-336.797C1121.5,-323.077 1121.5,-304.185 1121.5,-293.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node21\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node21\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "session_id           \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1048,-301 929,-301 929,-266 1048,-266 1048,-301\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"937\" y=\"-281.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;acquisition.Session -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>subject.Subject-&gt;acquisition.Session</title>\n",
       "<path d=\"M1089.64,-336.973C1068.74,-326.128 1041.5,-311.997 1020.56,-301.132\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.AnimalSourceAlias -->\n",
       "<g class=\"node\" id=\"node12\"><title>reference.AnimalSourceAlias</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"animal_source_alias  \r",
       "------------------------------\r",
       "→ reference.AnimalSource\r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1422.5,-372 1280.5,-372 1280.5,-337 1422.5,-337 1422.5,-372\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1288.5\" y=\"-353\">reference.AnimalSourceAlias</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node13\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node13\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1046.5,-230 930.5,-230 930.5,-195 1046.5,-195 1046.5,-230\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"988.5\" y=\"-209.4\">acquisition.TrialSet</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M988.5,-194.797C988.5,-181.077 988.5,-162.185 988.5,-151.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe -->\n",
       "<g class=\"node\" id=\"node14\"><title>reference.Probe</title>\n",
       "<g id=\"a_node14\"><a xlink:title=\"probe_name           \r",
       "channel_counts       \r",
       "------------------------------\r",
       "probe_type=&quot;&quot;        \r",
       "probe_desc=&quot;&quot;        \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"190,-372 105,-372 105,-337 190,-337 190,-372\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"113\" y=\"-353\">reference.Probe</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;reference.Probe.Channel -->\n",
       "<g class=\"edge\" id=\"edge15\"><title>reference.Probe-&gt;reference.Probe.Channel</title>\n",
       "<path d=\"M117.815,-336.831C104.468,-327.874 89.6839,-315.649 80.5,-301 64.5638,-275.581 61.8573,-238.756 61.4903,-222.171\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe.Shank -->\n",
       "<g class=\"node\" id=\"node19\"><title>reference.Probe.Shank</title>\n",
       "<g id=\"a_node19\"><a xlink:title=\"→ reference.Probe\r",
       "shank_id             \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"205.5,-293 89.5,-293 89.5,-274 205.5,-274 205.5,-293\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"97.5\" y=\"-282\">reference.Probe.Shank</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;reference.Probe.Shank -->\n",
       "<g class=\"edge\" id=\"edge16\"><title>reference.Probe-&gt;reference.Probe.Shank</title>\n",
       "<path d=\"M147.5,-336.797C147.5,-323.077 147.5,-304.185 147.5,-293.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node31\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node31\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.BrainLocation\r",
       "insertion_depth      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"312,-230 155,-230 155,-195 312,-195 312,-230\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"163\" y=\"-210.4\">extracellular.ProbeInsertion</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge17\"><title>reference.Probe-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M177.185,-336.831C190.532,-327.874 205.316,-315.649 214.5,-301 228.207,-279.137 232.127,-248.836 233.188,-230.233\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Species -->\n",
       "<g class=\"node\" id=\"node15\"><title>subject.Species</title>\n",
       "<g id=\"a_node15\"><a xlink:title=\"species              \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1164.5,-443 1078.5,-443 1078.5,-408 1164.5,-408 1164.5,-443\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1086.5\" y=\"-424\">subject.Species</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Species&#45;&gt;subject.Subject -->\n",
       "<g class=\"edge\" id=\"edge18\"><title>subject.Species-&gt;subject.Subject</title>\n",
       "<path d=\"M1121.5,-407.797C1121.5,-396.949 1121.5,-382.867 1121.5,-372.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.CoordinateReference -->\n",
       "<g class=\"node\" id=\"node16\"><title>reference.CoordinateReference</title>\n",
       "<g id=\"a_node16\"><a xlink:title=\"coordinate_ref       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"571,-301 422,-301 422,-266 571,-266 571,-301\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"430\" y=\"-282\">reference.CoordinateReference</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation -->\n",
       "<g class=\"node\" id=\"node18\"><title>reference.ActionLocation</title>\n",
       "<g id=\"a_node18\"><a xlink:title=\"→ reference.BrainLocation\r",
       "→ reference.CoordinateReference\r",
       "coordinate_ap        \r",
       "coordinate_ml        \r",
       "coordinate_dv        \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"477,-230 330,-230 330,-195 477,-195 477,-230\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"338\" y=\"-210.4\">reference.ActionLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.CoordinateReference&#45;&gt;reference.ActionLocation -->\n",
       "<g class=\"edge\" id=\"edge19\"><title>reference.CoordinateReference-&gt;reference.ActionLocation</title>\n",
       "<path d=\"M473.987,-265.797C459.365,-254.949 440.386,-240.867 425.805,-230.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.PSTH -->\n",
       "<g class=\"node\" id=\"node17\"><title>extracellular.PSTH</title>\n",
       "<g id=\"a_node17\"><a xlink:title=\"→ extracellular.TrialSegmentedUnitSpikeTimes\r",
       "------------------------------\r",
       "psth                 \r",
       "psth_time            \r",
       "\">\n",
       "<ellipse cx=\"610.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"610.5\" y=\"-9.9\">extracellular.PSTH</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Shank&#45;&gt;reference.Probe.Channel -->\n",
       "<g class=\"edge\" id=\"edge20\"><title>reference.Probe.Shank-&gt;reference.Probe.Channel</title>\n",
       "<path d=\"M136.608,-273.761C120.066,-260.489 88.7933,-235.398 72.3035,-222.168\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.TrialResponse -->\n",
       "<g class=\"node\" id=\"node20\"><title>reference.TrialResponse</title>\n",
       "<g id=\"a_node20\"><a xlink:title=\"trial_response       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"912,-230 791,-230 791,-195 912,-195 912,-230\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"799\" y=\"-211\">reference.TrialResponse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialResponse&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>reference.TrialResponse-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M884.315,-194.973C911.704,-181.178 949.655,-162.064 971.384,-151.121\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;behavior.LickTimes -->\n",
       "<g class=\"edge\" id=\"edge22\"><title>acquisition.Session-&gt;behavior.LickTimes</title>\n",
       "<path d=\"M928.793,-275.621C856.281,-266.874 730.401,-250.425 623.5,-230 620.475,-229.422 617.384,-228.8 614.267,-228.147\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge23\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
       "<path d=\"M988.5,-265.797C988.5,-254.949 988.5,-240.867 988.5,-230.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session.Experimenter -->\n",
       "<g class=\"node\" id=\"node25\"><title>acquisition.Session.Experimenter</title>\n",
       "<g id=\"a_node25\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Experimenter\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"1226.5,-222 1064.5,-222 1064.5,-203 1226.5,-203 1226.5,-222\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"1145.5\" y=\"-210\">acquisition.Session.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge24\"><title>acquisition.Session-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M1026.11,-265.973C1057.49,-252.178 1100.98,-233.064 1125.89,-222.121\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge25\"><title>acquisition.Session-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M928.843,-279.996C811.777,-274.6 543.901,-259.873 320.5,-230 317.757,-229.633 314.97,-229.236 312.158,-228.816\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.AnimalSource -->\n",
       "<g class=\"node\" id=\"node22\"><title>reference.AnimalSource</title>\n",
       "<g id=\"a_node22\"><a xlink:title=\"animal_source        \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1356.5,-443 1236.5,-443 1236.5,-408 1356.5,-408 1356.5,-443\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1244.5\" y=\"-424\">reference.AnimalSource</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.AnimalSource&#45;&gt;subject.Subject -->\n",
       "<g class=\"edge\" id=\"edge26\"><title>reference.AnimalSource-&gt;subject.Subject</title>\n",
       "<path d=\"M1254.58,-407.973C1226.98,-397.088 1190.97,-382.893 1163.38,-372.012\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.AnimalSource&#45;&gt;reference.AnimalSourceAlias -->\n",
       "<g class=\"edge\" id=\"edge27\"><title>reference.AnimalSource-&gt;reference.AnimalSourceAlias</title>\n",
       "<path d=\"M1309.81,-407.797C1318.46,-396.949 1329.69,-382.867 1338.31,-372.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes&#45;&gt;extracellular.PSTH -->\n",
       "<g class=\"edge\" id=\"edge28\"><title>extracellular.TrialSegmentedUnitSpikeTimes-&gt;extracellular.PSTH</title>\n",
       "<path d=\"M610.5,-61.8581C610.5,-51.4484 610.5,-36.5283 610.5,-26.1238\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge29\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M315.768,-126.425C409.147,-110.449 554.167,-85.6378 597.716,-78.1873\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Allele -->\n",
       "<g class=\"node\" id=\"node26\"><title>subject.Allele</title>\n",
       "<g id=\"a_node26\"><a xlink:title=\"allele               \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1262,-372 1187,-372 1187,-337 1262,-337 1262,-372\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1195\" y=\"-353\">subject.Allele</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Allele&#45;&gt;subject.Subject.Allele -->\n",
       "<g class=\"edge\" id=\"edge30\"><title>subject.Allele-&gt;subject.Subject.Allele</title>\n",
       "<path d=\"M1199.83,-336.973C1179.24,-323.178 1150.7,-304.064 1134.37,-293.121\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.AlleleAlias -->\n",
       "<g class=\"node\" id=\"node27\"><title>subject.AlleleAlias</title>\n",
       "<g id=\"a_node27\"><a xlink:title=\"allele_alias         \r",
       "------------------------------\r",
       "→ subject.Allele\r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1292,-301 1195,-301 1195,-266 1292,-266 1292,-301\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1203\" y=\"-282\">subject.AlleleAlias</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Allele&#45;&gt;subject.AlleleAlias -->\n",
       "<g class=\"edge\" id=\"edge31\"><title>subject.Allele-&gt;subject.AlleleAlias</title>\n",
       "<path d=\"M1229.1,-336.797C1232.09,-325.949 1235.96,-311.867 1238.94,-301.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.CorticalLayer -->\n",
       "<g class=\"node\" id=\"node28\"><title>reference.CorticalLayer</title>\n",
       "<g id=\"a_node28\"><a xlink:title=\"cortical_layer       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"357.5,-372 241.5,-372 241.5,-337 357.5,-337 357.5,-372\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"249.5\" y=\"-353\">reference.CorticalLayer</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation -->\n",
       "<g class=\"node\" id=\"node30\"><title>reference.BrainLocation</title>\n",
       "<g id=\"a_node30\"><a xlink:title=\"brain_region         \r",
       "brain_subregion=&quot;N/A&quot; \r",
       "→ reference.CorticalLayer\r",
       "→ reference.Hemisphere\r",
       "------------------------------\r",
       "brain_location_full_name=&quot;N/A&quot; \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"403.5,-301 261.5,-301 261.5,-266 403.5,-266 403.5,-301\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"269.5\" y=\"-281.4\">reference.BrainLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.CorticalLayer&#45;&gt;reference.BrainLocation -->\n",
       "<g class=\"edge\" id=\"edge32\"><title>reference.CorticalLayer-&gt;reference.BrainLocation</title>\n",
       "<path d=\"M307.488,-336.797C312.677,-325.949 319.411,-311.867 324.585,-301.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Hemisphere -->\n",
       "<g class=\"node\" id=\"node29\"><title>reference.Hemisphere</title>\n",
       "<g id=\"a_node29\"><a xlink:title=\"hemisphere           \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"487,-372 376,-372 376,-337 487,-337 487,-372\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"384\" y=\"-353\">reference.Hemisphere</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Hemisphere&#45;&gt;reference.BrainLocation -->\n",
       "<g class=\"edge\" id=\"edge33\"><title>reference.Hemisphere-&gt;reference.BrainLocation</title>\n",
       "<path d=\"M407.787,-336.973C392.228,-326.128 371.952,-311.997 356.363,-301.132\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;reference.ActionLocation -->\n",
       "<g class=\"edge\" id=\"edge34\"><title>reference.BrainLocation-&gt;reference.ActionLocation</title>\n",
       "<path d=\"M349.687,-265.797C360.85,-254.949 375.34,-240.867 386.471,-230.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge35\"><title>reference.BrainLocation-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M308.787,-265.973C293.228,-255.128 272.952,-240.997 257.363,-230.132\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge36\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M233.5,-194.797C233.5,-183.949 233.5,-169.867 233.5,-159.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.RealignedEvent&#45;&gt;analysis.RealignedEvent.RealignedEventTime -->\n",
       "<g class=\"edge\" id=\"edge37\"><title>analysis.RealignedEvent-&gt;analysis.RealignedEvent.RealignedEventTime</title>\n",
       "<path d=\"M824.5,-61.8581C824.5,-50.2156 824.5,-32.9314 824.5,-22.6325\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Experimenter -->\n",
       "<g class=\"node\" id=\"node33\"><title>reference.Experimenter</title>\n",
       "<g id=\"a_node33\"><a xlink:title=\"experimenter         \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1427,-301 1310,-301 1310,-266 1427,-266 1427,-301\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1318\" y=\"-282\">reference.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Experimenter&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge38\"><title>reference.Experimenter-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M1315.09,-265.973C1270.37,-252.138 1208.37,-232.954 1173.05,-222.026\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x18931dd6128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_erd = (dj.Diagram(reference) + dj.ERD(subject)\n",
    "           + dj.ERD(acquisition) + dj.ERD(analysis) \n",
    "           + dj.ERD(behavior) + dj.ERD(extracellular))\n",
    "all_erd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduced analysis and figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate the availability and usage of the data in the pipeline, we will go through the steps to reproduce the ***Extended Data Figure 5***.\n",
    "\n",
    "The ***Extended Data Figure 5*** presented the inter-spike interval histograms and trial-averaged activity on lick-right and lick-left trials, time locked to the onset of `cue start` event (denotes the onset of the *response* period).  \n",
    "\n",
    "The following sections describe the steps taken to reproduce this figure:\n",
    "+ Query, pool the applicable subset of the data\n",
    "+ Conditioning/restructure the extracted data\n",
    "+ Build plotting functions\n",
    "+ Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the data of interest\n",
    "\n",
    "Here we will make several queries and extractions of the subset of data, based on several criteria\n",
    "\n",
    "#### Queries of unit spike times and inter-spike interval, categorized by:\n",
    "+ Cell type: PT<sub>upper</sub> vs. PT<sub>lower</sub>\n",
    "+ Trial type: left-lick vs. right-lick\n",
    "+ Hemisphere: left (contralateral to injection site) vs. right (ipsilateral to injection site)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 236)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-236 375.5,-236 375.5,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node1\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"234,-19 114,-19 114,-0 234,-0 234,-19\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"122\" y=\"-8\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialType -->\n",
       "<g class=\"node\" id=\"node2\"><title>reference.TrialType</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"trial_type           \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"98,-90 0,-90 0,-55 98,-55 98,-90\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"8\" y=\"-71\">reference.TrialType</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialType&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>reference.TrialType-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M82.8268,-54.9925C106.542,-43.4194 137.29,-28.4146 156.327,-19.1245\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node3\"><title>subject.Subject</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"subject_id           \r",
       "------------------------------\r",
       "→ subject.Species\r",
       "→ reference.AnimalSource\r",
       "sex=&quot;U&quot;              \r",
       "date_of_birth=null   \r",
       "subject_description=null \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"221.5,-232 126.5,-232 126.5,-197 221.5,-197 221.5,-232\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"134.5\" y=\"-212.4\">subject.Subject</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node6\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "session_id           \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"233.5,-161 114.5,-161 114.5,-126 233.5,-126 233.5,-161\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"122.5\" y=\"-141.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;acquisition.Session -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>subject.Subject-&gt;acquisition.Session</title>\n",
       "<path d=\"M174,-196.797C174,-185.949 174,-171.867 174,-161.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node4\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"232,-90 116,-90 116,-55 232,-55 232,-90\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"174\" y=\"-69.4\">acquisition.TrialSet</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
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       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x18931bf5828>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(acquisition.TrialSet.Trial) - 1 + acquisition.Session + subject.Subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trial-type selection: left-lick vs right-lick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "leftlick_trial = {'trial_type': 'lick left', 'trial_is_good': True, 'trial_response': 'correct'}\n",
    "rightlick_trial = {'trial_type': 'lick right', 'trial_is_good': True, 'trial_response': 'correct'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PT<sub>upper</sub> and PT<sub>lower</sub> neuron selection, per hemisphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limit selection of unit to types: \"Excellent\", \"Good\", and \"Single\"\n",
    "good_units_filt = ['unit_quality=\"Excellent\"', 'unit_quality=\"Good\"', 'unit_quality=\"Single\"']\n",
    "\n",
    "# query left-hemisphere PTupper and PTlower units\n",
    "lh_PTupper_units = (extracellular.UnitSpikeTimes & 'unit_cell_type=\"PTupper\"'\n",
    "                    & 'hemisphere=\"left\"' & good_units_filt)\n",
    "lh_PTlower_units = (extracellular.UnitSpikeTimes & 'unit_cell_type=\"PTlower\"'\n",
    "                    & 'hemisphere=\"left\"' & good_units_filt)\n",
    "\n",
    "# query right-hemisphere PTupper and PTlower units\n",
    "rh_PTupper_units = (extracellular.UnitSpikeTimes & 'unit_cell_type=\"PTupper\"'\n",
    "                    & 'hemisphere=\"right\"' & good_units_filt)\n",
    "rh_PTlower_units = (extracellular.UnitSpikeTimes & 'unit_cell_type=\"PTlower\"'\n",
    "                    & 'hemisphere=\"right\"' & good_units_filt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of units found for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left-ALM PTupper unit counts: 65\n",
      "Left-ALM PTlower unit counts: 46\n",
      "Right-ALM PTupper unit counts: 0\n",
      "Right-ALM PTlower unit counts: 32\n"
     ]
    }
   ],
   "source": [
    "print(f'Left-ALM PTupper unit counts: {len(lh_PTupper_units)}')\n",
    "print(f'Left-ALM PTlower unit counts: {len(lh_PTlower_units)}')\n",
    "print(f'Right-ALM PTupper unit counts: {len(rh_PTupper_units)}')\n",
    "print(f'Right-ALM PTlower unit counts: {len(rh_PTlower_units)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize some tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the queried table of all left-hemisphere, **PT<sub>upper</sub>** units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "                                <p id=\"primary\">subject_id</p>\n",
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       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_time</p>\n",
       "                                <span class=\"djtooltiptext\">session time</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">probe_name</p>\n",
       "                                <span class=\"djtooltiptext\">String naming probe model</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">channel_counts</p>\n",
       "                                <span class=\"djtooltiptext\">number of channels in the probe</span>\n",
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       "                                <p id=\"primary\">brain_region</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
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       "                                <p id=\"primary\">brain_subregion</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">cortical_layer</p>\n",
       "                                <span class=\"djtooltiptext\">layer within cortex</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">hemisphere</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">insertion_depth</p>\n",
       "                                <span class=\"djtooltiptext\">(um)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">unit_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
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       "                                <p id=\"nonprimary\">channel_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of a channel on the probe</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_cell_type</p>\n",
       "                                <span class=\"djtooltiptext\">depending on the animal (which cell-type being tagged)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_quality</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_depth</p>\n",
       "                                <span class=\"djtooltiptext\">(um)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spike_times</p>\n",
       "                                <span class=\"djtooltiptext\">(s) time of each spike, with respect to the start of session</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>H-129</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1100.00</td>\n",
       "<td>1001</td>\n",
       "<td>38</td>\n",
       "<td>PTupper</td>\n",
       "<td>Good</td>\n",
       "<td>430.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-05 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-116</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1100.00</td>\n",
       "<td>1001</td>\n",
       "<td>11</td>\n",
       "<td>PTupper</td>\n",
       "<td>Good</td>\n",
       "<td>555.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-05 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-116</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1100.00</td>\n",
       "<td>1002</td>\n",
       "<td>17</td>\n",
       "<td>PTupper</td>\n",
       "<td>Excellent</td>\n",
       "<td>705.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-05 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>H-116</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1100.00</td>\n",
       "<td>1003</td>\n",
       "<td>51</td>\n",
       "<td>PTupper</td>\n",
       "<td>Excellent</td>\n",
       "<td>755.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-08 00:00:00</td>\n",
       "<td>2</td>\n",
       "<td>H-116</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1150.00</td>\n",
       "<td>1001</td>\n",
       "<td>17</td>\n",
       "<td>PTupper</td>\n",
       "<td>Good</td>\n",
       "<td>755.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-11 00:00:00</td>\n",
       "<td>3</td>\n",
       "<td>H-116</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1100.00</td>\n",
       "<td>1001</td>\n",
       "<td>8</td>\n",
       "<td>PTupper</td>\n",
       "<td>Good</td>\n",
       "<td>480.0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-11 00:00:00</td>\n",
       "<td>3</td>\n",
       "<td>H-116</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>5</td>\n",
       "<td>left</td>\n",
       "<td>1100.00</td>\n",
       "<td>1002</td>\n",
       "<td>14</td>\n",
       "<td>PTupper</td>\n",
       "<td>Good</td>\n",
       "<td>630.0</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 65</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *probe_name    *channel_count *brain_region  *brain_subregi *cortical_laye *hemisphere    *insertion_dep *unit_id    channel_id     unit_cell_type unit_quality   unit_depth     spike_time\n",
       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +--------+\n",
       "anm372793      2017-05-04 00: 0              H-129          64             ALM            N/A            5              left           1100.00        1001        38             PTupper        Good           430.0          =BLOB=    \n",
       "anm372793      2017-05-05 00: 1              H-116          64             ALM            N/A            5              left           1100.00        1001        11             PTupper        Good           555.0          =BLOB=    \n",
       "anm372793      2017-05-05 00: 1              H-116          64             ALM            N/A            5              left           1100.00        1002        17             PTupper        Excellent      705.0          =BLOB=    \n",
       "anm372793      2017-05-05 00: 1              H-116          64             ALM            N/A            5              left           1100.00        1003        51             PTupper        Excellent      755.0          =BLOB=    \n",
       "anm372793      2017-05-08 00: 2              H-116          64             ALM            N/A            5              left           1150.00        1001        17             PTupper        Good           755.0          =BLOB=    \n",
       "anm372793      2017-05-11 00: 3              H-116          64             ALM            N/A            5              left           1100.00        1001        8              PTupper        Good           480.0          =BLOB=    \n",
       "anm372793      2017-05-11 00: 3              H-116          64             ALM            N/A            5              left           1100.00        1002        14             PTupper        Good           630.0          =BLOB=    \n",
       "   ...\n",
       " (Total: 65)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lh_PTupper_units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Queried table of all **left lick** trials with recording on left-hemisphere, **PT<sub>upper</sub>** units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "                                <p id=\"primary\">session_time</p>\n",
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       "                                <p id=\"nonprimary\">start_time</p>\n",
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       "                                <p id=\"nonprimary\">trial_response</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">trial_stim_present</p>\n",
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       "                <tbody> <tr> <td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>3</td>\n",
       "<td>355.459</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>4</td>\n",
       "<td>361.859</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>6</td>\n",
       "<td>374.659</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>10</td>\n",
       "<td>400.259</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>18</td>\n",
       "<td>451.458</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>19</td>\n",
       "<td>457.858</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td></tr><tr><td>anm372793</td>\n",
       "<td>2017-05-04 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>20</td>\n",
       "<td>464.258</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 4025</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *trial_id    start_time     trial_type     trial_response trial_stim_pre trial_is_good \n",
       "+------------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
       "anm372793      2017-05-04 00: 0              3            355.459        lick left      correct        0              1             \n",
       "anm372793      2017-05-04 00: 0              4            361.859        lick left      correct        0              1             \n",
       "anm372793      2017-05-04 00: 0              6            374.659        lick left      correct        0              1             \n",
       "anm372793      2017-05-04 00: 0              10           400.259        lick left      correct        0              1             \n",
       "anm372793      2017-05-04 00: 0              18           451.458        lick left      correct        0              1             \n",
       "anm372793      2017-05-04 00: 0              19           457.858        lick left      correct        0              1             \n",
       "anm372793      2017-05-04 00: 0              20           464.258        lick left      correct        0              1             \n",
       "   ...\n",
       " (Total: 4025)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition.TrialSet.Trial & lh_PTupper_units & leftlick_trial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the events of the first **left lick** trials of each session for all sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        \n",
       "        <style type=\"text/css\">\n",
       "            .Relation{\n",
       "                border-collapse:collapse;\n",
       "            }\n",
       "            .Relation th{\n",
       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
       "            }\n",
       "            .Relation td{\n",
       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
       "            }\n",
       "            .Relation tr:nth-child(odd){\n",
       "                background: #ffffff;\n",
       "            }\n",
       "            .Relation tr:nth-child(even){\n",
       "                background: #f3f1ff;\n",
       "            }\n",
       "            /* Tooltip container */\n",
       "            .djtooltip {\n",
       "            }\n",
       "            /* Tooltip text */\n",
       "            .djtooltip .djtooltiptext {\n",
       "                visibility: hidden;\n",
       "                width: 120px;\n",
       "                background-color: black;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
       "                /* Position the tooltip text - see examples below! */\n",
       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b>experimental paradigm event timing marker(s) for this trial, relative to trial start time</b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">subject_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of the subject (e.g. ANM244028)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_time</p>\n",
       "                                <span class=\"djtooltiptext\">session time</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">trial_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of this trial in this trial set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">trial_event</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">event_time</p>\n",
       "                                <span class=\"djtooltiptext\">(in second) event time with respect to this session's start time</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>cue_start</td>\n",
       "<td>6.37397</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>pole_in</td>\n",
       "<td>3.87349</td></tr><tr><td>anm369962</td>\n",
       "<td>2017-03-09 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>pole_out</td>\n",
       "<td>4.87365</td></tr><tr><td>anm369963</td>\n",
       "<td>2017-02-27 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>cue_start</td>\n",
       "<td>51.229</td></tr><tr><td>anm369963</td>\n",
       "<td>2017-02-27 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>pole_in</td>\n",
       "<td>48.7283</td></tr><tr><td>anm369963</td>\n",
       "<td>2017-02-27 00:00:00</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>pole_out</td>\n",
       "<td>49.7285</td></tr><tr><td>anm369964</td>\n",
       "<td>2017-03-20 00:00:00</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>cue_start</td>\n",
       "<td>6.96214</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 12</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *trial_id    *trial_event   event_time    \n",
       "+------------+ +------------+ +------------+ +----------+ +------------+ +------------+\n",
       "anm369962      2017-03-09 00: 0              1            cue_start      6.37397       \n",
       "anm369962      2017-03-09 00: 0              1            pole_in        3.87349       \n",
       "anm369962      2017-03-09 00: 0              1            pole_out       4.87365       \n",
       "anm369963      2017-02-27 00: 1              1            cue_start      51.229        \n",
       "anm369963      2017-02-27 00: 1              1            pole_in        48.7283       \n",
       "anm369963      2017-02-27 00: 1              1            pole_out       49.7285       \n",
       "anm369964      2017-03-20 00: 0              1            cue_start      6.96214       \n",
       "   ...\n",
       " (Total: 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition.TrialSet.EventTime & (acquisition.TrialSet.Trial & leftlick_trial & 'trial_id=1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract the trial-aligned spike sorted data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the queries specific to the trial of interest and neuron of interest, we can extract the sorted spike times data, per cell, trial-aligned to the *response* period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 243)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-243 306,-243 306,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node1\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "unit_cell_type       \r",
       "unit_quality=&quot;&quot;      \r",
       "unit_depth           \r",
       "spike_times          \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"302,-97 138,-97 138,-62 302,-62 302,-97\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node6\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"140\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
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       "</g>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node2\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"120,-89 0,-89 0,-70 120,-70 120,-89\" stroke=\"none\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
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       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node3\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"118,-168 2,-168 2,-133 118,-133 118,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
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       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node4\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.BrainLocation\r",
       "insertion_depth      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"295.5,-168 138.5,-168 138.5,-133 295.5,-133 295.5,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"217\" y=\"-147.4\">extracellular.ProbeInsertion</text>\n",
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       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
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       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node5\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "session_id           \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"197.5,-239 78.5,-239 78.5,-204 197.5,-204 197.5,-239\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
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       "<datajoint.diagram.Diagram at 0x18931d4a780>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(extracellular.UnitSpikeTimes) + acquisition.TrialSet.Trial + acquisition.Session + extracellular.TrialSegmentedUnitSpikeTimes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, select a particuclar setting defining how to segment out information per trial, including the definition of even for alignment\n",
    "\n",
    "here we choose segmentation setting to align to `cue-onset` event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_param_key = (analysis.TrialSegmentationSetting & {'event': 'cue_start'}).fetch1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting the trial-aligned unit spike times\n",
    "\n",
    "To this end, we now build a function to extract (`.fetch()`) from the data pipeline the spike-times per unit, with the above trial and neuron criteria applied\n",
    "\n",
    "Per unit, perform:\n",
    "+ extract spike-times and PSTH for all trials for left-lick and right-lick\n",
    "+ compute inter-spike intervals for all spikes in all trials\n",
    "+ exclude results with less than 50 correct trial-repsonse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# per unit, extract spike-times and psth\n",
    "correct_trial_count_thresh = 50  # 50 correct trial of each left/right type\n",
    "def get_psth_isi(unit_key):\n",
    "    l_spks, l_psths = (extracellular.TrialSegmentedUnitSpikeTimes * extracellular.PSTH & unit_key & seg_param_key \n",
    "                       & (acquisition.TrialSet.Trial & leftlick_trial)).fetch('segmented_spike_times', 'psth')\n",
    "    r_spks, r_psths = (extracellular.TrialSegmentedUnitSpikeTimes * extracellular.PSTH & unit_key & seg_param_key\n",
    "                       & (acquisition.TrialSet.Trial & rightlick_trial)).fetch('segmented_spike_times', 'psth')\n",
    "    \n",
    "    if len(l_spks) < correct_trial_count_thresh or len(r_spks) < correct_trial_count_thresh:\n",
    "        return None\n",
    "            \n",
    "    out = dict(isi=np.hstack(np.diff(spk) for spk in np.hstack([l_spks, r_spks])), \n",
    "               l_psth=np.nanmean(np.vstack(psth for psth in l_psths), axis=0),\n",
    "               r_psth=np.nanmean(np.vstack(psth for psth in r_psths), axis=0))\n",
    "    \n",
    "    # Query event times (e.g. sample-start, delay-start, etc.)\n",
    "    events, event_times = (analysis.RealignedEvent.RealignedEventTime & seg_param_key & unit_key &\n",
    "                          (acquisition.TrialSet.Trial & {'trial_response': 'correct'})).fetch(\n",
    "        'trial_event', 'realigned_event_time')\n",
    "    # Calculate mean event times across trials\n",
    "    out['mean_event_times'] = {name: np.nanmedian([t for n, t in zip(events, event_times) if n == name]) for name in set(events)}\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lh_PTupper_units_psth = [v for v in [get_psth_isi(u) for u in lh_PTupper_units] if v]\n",
    "lh_PTlower_units_psth = [v for v in [get_psth_isi(u) for u in lh_PTlower_units] if v]\n",
    "rh_PTupper_units_psth = [v for v in [get_psth_isi(u) for u in rh_PTupper_units] if v]\n",
    "rh_PTlower_units_psth = [v for v in [get_psth_isi(u) for u in rh_PTlower_units] if v]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quickly check how many units of each group passed the selection criteria above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left-ALM PTupper unit counts: 62\n",
      "Left-ALM PTlower unit counts: 46\n",
      "Right-ALM PTupper unit counts: 0\n",
      "Right-ALM PTlower unit counts: 32\n"
     ]
    }
   ],
   "source": [
    "print(f'Left-ALM PTupper unit counts: {len(lh_PTupper_units_psth)}')\n",
    "print(f'Left-ALM PTlower unit counts: {len(lh_PTlower_units_psth)}')\n",
    "print(f'Right-ALM PTupper unit counts: {len(rh_PTupper_units_psth)}')\n",
    "print(f'Right-ALM PTlower unit counts: {len(rh_PTlower_units_psth)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot PSTH and ISI\n",
    "\n",
    "With the extracted unit spike times and PSTH, we can now build a generic plotting function to reproduce the ***Extended Data Figure 5***: plot the inter-spike interval histogram, and the trial-averaged PSTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_list(arr, chunk_size):\n",
    "    for s in range(0, len(arr), chunk_size):\n",
    "        yield arr[s:s+chunk_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gaussian filter for PSTH\n",
    "sigma = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = np.arange(-.01, .01, .0005)\n",
    "def plot_psth_isi(isi_psth_dict, axs):\n",
    "    psth_time = np.linspace(-1 * float(seg_param_key['pre_stim_duration']), \n",
    "                            float(seg_param_key['post_stim_duration']),\n",
    "                            len(isi_psth_dict['l_psth']))\n",
    "    \n",
    "    isi_counts, isi_edges = np.histogram(np.hstack([isi_psth_dict['isi'], -isi_psth_dict['isi']]), \n",
    "                                         bins=len(edges), range=(edges[0], edges[-1]))\n",
    "    \n",
    "    axs[0].bar(isi_edges[:-1], isi_counts/len(isi_psth_dict['isi'])*100, width=0.0005)\n",
    "    axs[1].plot(psth_time, gaussian_filter1d(isi_psth_dict['l_psth'], sigma), 'r', linewidth=0.5)\n",
    "    axs[1].plot(psth_time, gaussian_filter1d(isi_psth_dict['r_psth'], sigma), 'b', linewidth=0.5)\n",
    "    # event markers\n",
    "    for v in isi_psth_dict['mean_event_times'].values():\n",
    "        axs[1].axvline(x=v, linestyle='--', linewidth=0.8, color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended Data Fig5a - PT<sub>upper</sub> - Left ALM\n",
    "\n",
    "Calling the `plot_psth_isi()` function for only Left ALM PT<sub>upper</sub> neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OAadrSkshorQAnGoG17VzNwDfMJotW1SZ3QfM3H03HDqkPhuNsHOn6ldLCe3bl538FEJ1qqWsswev6rEHiNT0/fcq+HGZpbbZ2cpyyAhpsgJN+DCD41Px1bvnUV6eeubjLjuXnBxlrHo8DTzGam6uGsBZuVIFWYqNVerodKpjw1iElQj47Tdvef81rwBTp8JLL6ntdetUGVBdyT171HWbKtWxCFoD/ivTqwr5PQlY4Pc50h3me5UQol5mADThR6vcLfxFDyjIp/ip/wBw552+ZTH+y2NKfl5LdIzghlP/W+PrLF26NAjShrjO++6Drl3h2mv5+PaVZOwtpq95I9GOAg44WrBpkyAyEnZdejcAwza/DMBrhTcS7Spi66yNAEyeN7LOgSc04U9tch67zwtqioXa6EEo9PF//1uq1nSvXcusla3Zvh127QImTlR6pYOCaRoZQkjvzOpB91i/dKj/9ixnEkanjS4WtVyksBCkSwJLsWEhwlXie+ZjYnAVWzHgArOZOAoockVhMoH9cCZL//GPMu65+cRRUuQiyqV6xXbMmB1qSlQgueoqSJ+1gge/UmlyPEteTetWkZOjnBuyspYCPkMWVMyFp5+Gd9+t5Q259FLfdmmpL+KgHzabMhIisPqMVU9HIqjRnTThSgWp+OrV82goSynMd3H//UvpbFUTvNu2qWXfycm+RzE93ed9MG6cMlZPO20p332nHAH6dLFyNj/zQMuPATULWz4bm388k5gY3yyrZ6nr/v1LA3ohNAWqY6yKAPsC3g0hxHXAAOB5v93tpJQDgGuA/wohOgc4T+etauIknNMXgOffTeKxRWcDEB0NRbPmw5w5eDOkWK3kPjiVqFgjP+7+jPxqd80VU6dODaLUQayzpMQ3qi2E+sf54w8+yh3Jz5uS2WTvTsvIHKbyKABHtmbTZf0X3tNP41cA7DkFbBg0mQUPLiHFmHPceldN06IuOY+DHdW5NnoQCn189tmpyvPg1lu9++69FzX6ZbfrtdyaxofDic2gZlZzUc9viimfhDgnWaRiKsihjUEFry0qgqJjVkyGp7AlpGFxFENaGi4ExMZiK7RhwYYNC9mk4MKIdDixYeFv079lyGkOGDsWgKGG5axbXkKkWRnGViKJsuaWEW3nlHf5GuXFo+ITgWnvDrJvuo/kZEhPVzruH2ApOlqFVahVd87phP+5Pcsuu6xsePxHH/UWKy11G6sHd/mMVbtdTWkFbcGsphFSLc+jYNgdLhckGAspyLazcOFU4ingvfdg+XL1GHbrBm/dso6MDPWsxsb6zo2Nhd9+U7rzv3mS3p1L+NMygMhWKYB65MvHS/LPLBUT4/Na8AXxnho4r1QToDrG6kHAP5tywJDfQohhwKPASCml99ZJKQ+733cDS1GubGWoz1QZTdHFtjHQZ1hzFl8/g8xNGRiK1XBQbCwUfr8CDhwgPR0VwfDYMQaxlqg4E2sPb/Vvm6rFokWLgi57UOq8917VAA8Zwug/nyIjqTvs2UMvuYnn1w4lzljMGws7M4B1AOzNS/ae2oOtvHCjivWQY0pj6poL6XNhS+6+4iCOAj2C3MSpLOcx1EPOYw+10YNQ6OPixYuIKqpgbc7evSqKhcsFb74Z9Gs3JmrS1jW1drGxfXfhdGCLUgGWhHsuYHTqCiYV/JcckpBOF605xLReH1FUBIUZxcCPlJ48EHNOBsUJLenMLmR0DLYiOxZsfJLjy0tpXbySLxjHhyt2Y7VK1ZsGEkzFHLrhESKlakesRBKZdYANnMK37gmpQ7t9nV+vsYqDvO0ZxMeD06l03NNhnj3bPRBd5LMtS0tVSo9q4Z4+2rRRqsBO/onHn3nGmzLWN7NaWnZmNS6u0c6shsOzGErC6fsFw+6wWiFR5lKweR9btyo9uPFGdczhTgH+/JIB3HYb3ujeoNx5zWbIylrEwYOwbZONTgvfoEtXA0ahJjVycuChh2DxYnj2WXWexeIL2GSxqCxVO3b4G7WLkMWN89mviuoYq2uBrkKIjkIICwFCfgsh+gJvoQzVDL/9SUKICPd2KiqH55ZgCa9pPFhiLfS+YQCZMoWYGKCoiFizlUdm9eVQttuvYfJk71qTuAT1aKamNpDAwSY/H1atgl9+4dvMM2i3aT52zCQ4szmpWRZxphL+0eljCoy+WaFzWMJ4PmNT6+Gc8cEtABx0qEAarc7tTrNUSWa6PeDlGhueRsz/FW4EkivEssYSBjmPG4pAXhWeYJ9Rj9wDwNjzs1iwAB5/3O39dOQIDByoks49VvPUVxpNg+BwYJMW5gx/iwiU5ZUjkmnBEaxEYu3ci1hXPme12EFhoYqYbxBgu/dB9tGes6ePZy8duWXeKC5deCcWbCRF+GYX81Zs9G4nRRQjLRHgcpEWlc8eR1si3LM1JUQRQSl92eAtv4RzvdtZWcqQNmPnbsd/iHP4PDAvuUQFO3I6lb24bZvv6xUXq1Qd1aK4GKKj6XOyAJOpjLH6Ivdy5plgv2S0mllds81nrL72mlrbarE02dklTbWoludRndm7l+Jd6SS6jlHw0Rw1yGQ0ej0MbDbl5HMWPxMfD5995lu1lZqqxlPsdkhrJsnMi6ANB5WLv9uZ9fbblc4UFqp0OMXFyiGvqEjF5HQ4oEMH1eRNn+4Tq6lOYFRprEopHcDfgIWoaJTHhfxGuf3GArPKpag5CVgnhPgDWAI8J6UMC2M1XDvEFdHY5A1ETLNorERij07A+sc24g5s4aOSMbR/8kYMONlPW2S//gA0b2ngmebtcWXVLJ3riy++WHWhGhKUOqOjIT0dOyYMOEkQeRzrcSYgyckRGEpLiLcfI88VB8APXMD/uIT32/wD48F98LzyrO9q2M369SAMgtRUyEzXa1abMIUNnfPYn9roQV10p/xA1e+/w4QJcM89LxJttMGDDzLzx1SGD1fqVVqKil5hs8HGjdodWNNoEE4HVpeFs1vuYBO9AciRiV5jtcRqIMpZSIRFUrp1NyXrt9AicSrFpgSaGXNYvyeFJLLZdsztQhgfUWbgs9DqySvzIjGOfPof+R8UFtIy0co+QycihTLuvjn3ZaIp60L7Nj53+4IC1ZE24eCobE5U+h4SLU/z2GNw883KE/+ss9R6VX/8J0c9TJ9etpPtpaTEuzDvYcdTcPXVEBGBo9DKB6hpq6L5S9XM6sJVPmP1zjvBaGSNHMiy93fBTz9V9/Zrmhb143k0YwYl73xCEjksYyi3T3rO665+zz3KmIyPh2Ki+fBDuO465fDz3XdqiXleHkj5IuYC1XwnkVNmgNZkcKm15nZYuFC5/Xo+Dx4MF18MX35JuQwbL2LLO0GNVQB3B6mblLJzBSG/h0kpm5dPUSOlXCml7COlPMX9HvLIo03BqGuqRKbEUEI05jbNaX1me+IoAKCj6QAJ5NFZ7CYT1UNt0c5Cp6Ozeeq15BotP0lPTw+63EGps2VL2LqVPBJIII9keYycL5XbyJ78ZHJIorntAHZTFBazix7v3E/0n6uJ9KwfatuW3f+ZjTUqkX791K6oWCPWwsCJ28OdcJ9FrYxAcjfG71FTaqMHddEdezmngX79YO1a2L8/naQ0c5n12lFRankbkZGqh/DNNzrnqqbRYJJ2SpxmLI5i1qMGbHOznaSRgQOzst/s+UQsmINt+WpKZs1j/7F8broJEoeoDCvNOUqX5ipQkrllMyZu801l5huS+PDWlUSxE6fNyQ57e5Z8X8or+0aRKdKItKvz5i+JJnpgbx5FraV7kGmB5cXBQ4lvcTTLwLntfvEGXgIVkPuyy+Caa9Tnbt0Cx0g6eFCtabXby3a473g4DhmpjNWfOB8A+emnnJSaiQnV3pUQRfS+reQeLiaCUqw/rgBg6YISVtn68dWm7ipcqqZJU0EqvnrxPJq7vQclxZKEi05nNadRuPgb1f5Yrby07wqM2ZlEGawUEsuAvk6eOHsp9oNHiYxUxmpWFnQzrYC8PN65bT0J5HnXtH70ETz5tIGnnlKByywWOOccdcwzEFSslqqXS0Gcjj3/BDZWNeFDY+4UG+JjeST+NXLb9CaHJO8IbprrCAnmYswmyU5Dd8ZYviGuTYI3Slf5iGiV8fzzz1ddqIbUuc6ff1ZrhDIyKCSWOApIJpvx44Fu3TliT2X8ZUVcdEYB67M78cWXBlKuuUgl2vJY6i1b0vykZK661TfdFBFtpLSkBpmjw4DG/PxWh6b8/WqjB8HSR48abN8On3zyPIltYo8zVn/80f3hzjuV75Q2VjWNBYeDUqcJi9FJ7xkPAJBjjSIJlYbJaoVIRyGWlHhK41Ip+WM78DxZWZCQaKBz6xIisZIQ66S1+ShSqK7dJ21U1o58VwxX/OcMXLyI0+Yk2lBK7lHlbrzMOYTIGCNvo5aaRI44F8OZpwMwjSmYsXnFjEEZtUnk4Mgr4qRd81h5aJY3TYcHkwk+/VS5KI4fr4xVs7lsVimnU5VbulR5Ufzyi9r/xmdJHDR1AMAQpSxc++Cz2F+ahsUtSzHRxC2cxY7DMURipbTAxvPcz7k5szE7rdgxB0zTo2laBErFFyzPI/+0MZ50Mx5ycmDU5+MoKXKRmKBc479d+ZEakUlNhTlzMKxbTYslM2nBEdbuTMKwdDFP9JtHv36qSFYWxJn/Bbm53Dx9ACac7FZZo3jjDRjAWq68UrkAS6lcfv37wseOQUqKbx05QILxH9jym6YLfJM1VptypzEQjWKmKiaGqVm3Ep0cgcSAtVUnAJqPOh2HMFNiN7M69RKGDnEhWjT3npazbldDSRwUjv7wB65lP8OxYxQSSyyFJJPNH3/AgTPHcXDCw0x/uZTU2W8THWtg9Gjl1khUlC9QxFlnET1iKC+84Ks3ItqIrSS83YAbxXMZAk607xsMPBH3FyzwRRGdN091av1zndtskNgpWU3ZuPGE8xcC1RPYtUsbq5rGg82Gw2XEHGlUKZhQUYE9xmpJCUSV5hIxdjS2UklJt1O8qUcHDQKXFGygL6/+0A2by0SpywzA6Zb1ABQQh8UCEgMum4MYkxVXoc9laeDfBrP2vId45BG3Dp3rXqeakMA/ov/jLRdtVAauGTur5CCakYmzqOKZnObuZry0VHWqhwxBTQ2VlOBwqIipHkN3yBDf+NOVB1QyyUiLMjjzXbFEU0ys21guJhqB5G3r9coNePY8/mFRUWgMTjtvcZvKFaLR1BL/ZSjbVueyb68vCUqyO/7lmxtOJzneQYTRjtFaqKKGuvPJGA4dIP6v1SzlXCgogKefZmiHfSQnQ9ukQtWXM5l8jdvSpZhManPYnndYyyCmTYMtW1Tb+Ouv8PLLPpmOpLuIiVEeCh9+CBdcAImmIuyFpTRFmqyxqglTzGYm3GhmIGt45SuVrrd1axgwIo3bJhbz94wp/NH5CjCZuP6++wDI/25Ztau//vrrgy5yXet858/B7Lj4Hhg8mKJb7iX1lDZEpcYAcNVVYEmIUq6L5TEafT14IcrmBgAs0aawmFnVhlntaGz3rTZ6UJNzzjsPvvpKrcXZulXtu+wy+L//8xmvkyZBZub1JLZPgA8+OK6O009HGan79vl06vnnyy/s0WjCin6pykoTnkhHQE5ESxI7JLFtm4r4aXzkISytUnnz557sv/4x7r5b6dZ118G02/bwGn8jMd6FzWXC5lK93oiSXGbMgEJiMZvBwlhcGZnssbYiwy/egRBwNKYzZjNs3gwfzBDeA4+KZwCY3HY+iRHKMJ3Ih3RiN2lk0Irzqvx+paWqKduyBbVQ9dgxXC513bvu8pXb+dRnAKwtUq7NUZGq/Wu2dj5RlHiN1c8Z742avIeOfHnoTEps6juXOtX7jlcXNNmck5rQU+pn8xU/8Ry5f+6HGTPKpEV666+hpCQ6KXWauR58VixgxInLY2L97W/KtQDgpZeI6dyCS047xvVdu8JFF6n9nsHXkhKMGWoat5XxKLt3SxwONYjrHsciKQmili1ECBXLoV07+GGh5MyUAdgKtLGq0QSFngNjWPObmT6nx/L++8pYPe10wWNTowHVOQUYceutjExeTn6WrZLayjJixIigy1vXOgusJjaM+gefXr+QlSdNouOpCZScdh6TJqmOOV27qvCJgXj77QrrjYjYQ30lAAAgAElEQVQyhIWxqqmYBoggHDJqowc1OSc/H377TW3b/FQ+JwfGjFHbjz8OpaUjiI8o2yB7Oha9eqF6wJmZSqf+/FMZtYePy7ammDu3TIf2xhvLdlI8eFwUw5nqPFdVlQlGypfaeFJUVj6YaWiCcY9Cwcgu7riTfVVmv127IKFlFInTHqZbN+jcGZgyhYikaNalt2HS3bGcf/4I0tKU5+GY0Q5ufCCVohIDpdKCLTYJgIjCY/TvD6VEIARIRuBETWWunq6Ubdky5fJ49KjynE1Ph5decgu2dStYrSQlOJk482JSDTnYMNOWg3zERJpzFDPDjl9g7ofNBl9/7fvsMkcw+f54b59/82bfsRnz0zBhp4tJ+TZGRfu6qNEUExOrPj/LI0gElzGXdQxkNlfSvo1az1rgUG4W3djRaFPY1BeNtS2qL+w2CcOGURyVzLHbHyXnoecoPJRXxu09IV4ybdDXjABfItXISJKShTcuC8nJvhzAb72lZl/z8xnRu7evomjV/40tOkpBUnuIjSVq6mPYbGrgqGNHZSuPGwfNmklml14MqHHZvkcWwPr19G91KqUF1e8vNya0saqpf4TwNso33qhSkN57r5oI6d0bTj1VFRs/fjz/umwNG/Ylce+9VKvHOH78+KCLW9c6zQ4rz70exxdfwHvvqeCGxcXw7rvuArfe6sv0XJ5bbqmw3ohYE7bS+h85rm6HUDeCPprCPamNHlT3nN9+U4NWnnQXq1f7js2aBcOHq+2WLQHGYzSUfe49g9ZJSapRp7hYpcE65RQVRnH/fmX1Osu5zU+YALm53o/z50N2tk8Gz/aQIdX6GhpN7fAkJHVPnXTqBDt3GTBffXmZYpZ4X0jdKVPGU1LiDlzUpw9R0/5JaanAhgVXWksAIgsyiYiA0ubtAbCKm3FGRHM/z/Oh/VoAzj5bOSNs3qz60seOQY8e7ou0aAFOJ0cyjERFQV/LZszuIEd88glpZLCR58ouRi1HYSE89xy0aaM+7zd25J0v4pk+/Xh1/Dh9GAZcmFAHIiN9em7Gjrl7R+9nK5F0PjWOayNmAZCUrDr1x1y+KMjOnAD5rzSaatIiKg/276cks5B1DOAT+1jSTm6O2eALbBkTK3iwzwLGA14/3latePL85UzgY/XZM5iza5cvrVJODuMXLICxY5Xeu5etxOUfoigyWeme26X4q898I6jTpkHx7iMqwY3NRlERJPwwC37/nTd3fkBRXuMMulkV2lgNQEN1Kht7Z7a2REQoWy0mRnVY/Yl/4h62ubqyepVsFD1GKdW6As9M0Lvvwh+HUrj0MsHGjTB6tJpN/eGHul/LEm2m1Bo+M6sn6vOrqRsffAD9+6sIiWvWqH2PPebbHjZMRT3cuVP9T5x88vF1TJigOr65ubB3L2Q/8Cz88QdMncqEff9i77gpanR74UJ1wvffw8UX86q4i4/7/1ct/MnNJS3N53L82GNljWaNJmSUz+sC3jWp/lgSo8scf/FFn7e7EPD4I05c+HI9RmAlIgJsFjXj0/tkA05p8K6F9dC5s+oXWyzwySfK2WfePL/rWtT48utvm30XvPBC0r57HzsmpT8V4PGS8DgPddztSykTyMZ1ChMms/oCn2/ty7cv7+Gmm+BKvqZ9XA7Zf/8XX34JizmPl34/lxlHlPdGUoqBVDLJIpUZM+Dla1eTe6RpBpvRhBaPs02Jy0Jm73MpJYKDtCG9NJkSqwGjXRmPP0SOpE0LO5x2murIekZNU1IwxEYjJkxwV+Se4f/4YzUVCqrRy8xUa8EKCrzXjs7aT0RyrFIY9/4rT97pPR5VmEmxwz25sXkzR0lTeXImT8ZgL6UgN7zjmNQWbaxqwgaDQTWSHnr37k1SqpG1B1vQsZW70anE3chzTrApX6eUyqvQ7//Fy0knwcMPq7V3a9aoidGNR5ohDIK9e5UbB3Bc9MTaEBFrprQB22JtnAaHxnIfa6NbVZ2zdKlyLGjeXHVq/R0MTj0VLr0UBgxQfYDOndX+vqf2Oq4eg0G9srNVipuU56eoadabb2ZbbD8yCpVrYMn+TGUdjxjBFT/fQ25cWzbntOKve6ZzaPp3pB3dSMZR1VNp3VrZths31vhrazQ15o47qi5jSvEFDevVqzc331zWqH3qX+qDp7NtTopTM6vuiZlTT+1NkTOSzm/cX6ZeTyAkk0n1oSMi4JJL3Af9l6hceSUMHary0kRHk3rJYK5snl+xmz0+YzVQyuV//1vljOzRtpDbeQMApzRiNvo63KPv7kB0NJiuvxaDy0FSvJPBgyEdNXtsSox1yy5om1LM7/TlqqsgNs5AYXbTdInUhBbPZEIMRaTNeQszdnIMKRyxqzzG0j0aNHSYGUuUCW6+md6XXw433KBOjI9Xo0dTpyqviezAAYl7Q5mgTADXrb6TF0YvV/sLC+nX/CAcOKAWrQIxD/wftzFdRYDasoU0MpXimkx07TOAwoKmuU5bG6uasOKZZ3zbM2bMID4eNma1ovNBd5CllSsrDZoww2MNbtgA//1vUGTy1ukmO1t1ZD2dZ3+2bfMFIR08WL0fzY+ipEQZsf5hxuuKJcbcIG7AmqaHzaZmJh2OijM+lNeD6lDZOTt2qKCjERGqs3zgAJx1lpoZfeUVZaB+9x1cIub7Os7AjOnTA85EgRoESvR4Aa5eDWlpNOscz2FzB0qx0Pa+MXDTTQDMKboQe4u2RJx6Eq+vHcRXn9lolrmZeX9fzMyZygvr5Zd9M7me+/L007BnYyGfvnj0eAE0mlry2mtVlxHJSd7tDz+cEaCALwjfE08AR49isfiM1RkzZlDgiuHMoaYyzagnLkxA3ffMBHmYO1flpYmJQQj4Ys3S8skey+AxVlu2VOmP/WmeYufDDyHWXOrNoWrEQXznNECl8EhLU96TdOyoBDSZaN4cjtK8TF2TJsHb37dnJ12IiICYeANF2U0z2IwmtHz/bSnn8RMWbBgNLqyWBIp79OcD+3UANDNm83Tqy2pw1aGe2xmff+7zOpg0CV54Adq2hcsvVz7wnlElz7pWYIbns9/Mh+Hk3piT41Qnc/9+1q9DNZYXXwyjRhE9/yum8phqNK9T8lBQAJGRvPDyJxTk13Of8LLLKtV/XK6gBH3QxqomrPAfxB06dCgA//wnLNndga8u/5Ssc64kd/Fv5B/IC6gAnnPo21ctQAsC3jrdeAxOv8EwFizwBYc5cqTs+VN5FJdL5cTyeIkEg4hYM6XaWNUEgWeegTffVDMd3uAq5SivB9WhsnO6dYMuXdTY08knKyP1xRehfXu4c+ifXgUb8uwlnHqyCy68UNV5/vlqzUAAZszwFmPiRNXRTUgxc7n9S3IumYDRUcpR0niYZ0gxZJO5Kx/hcnJ0v5W1GyNgyBBe3nQ+48dDpLmsO5X18vHcOK6EVatg7487efm+fQw5w4XDodb7jR0rdWpHTWhx59O44QYYNqxi3erRwz0zajaXmVkdOnQohREpxKYpd2KP966nuTzttACVJSUF2Olj6LXXqvXhFXDNNWrix2w+Puh9/jE7bVq5SLIUeSOnfnvvMgZflOi9dIsWbmMVkC4JJhMREXDrON96VCnV0r8BA8BKFCYTxMSbKMqt3BNLownErSX/pQN7STIXYTQZyB9/K0UOC6ezEoDbHa/y6L8T8B8JKtPWRUb6Rk3791czrJ7Z1Xz3c9upE0ObN1eBlfwiDANw993w/vuwe7ca5VmvUlAxd656b9NGBWryRBDOz4eSEibeeRkFVrNX34PeHr355vH7Dh/2DWh9+SVZItW7ZM9qhTtvtSmDto6RubWxqglbityd1X/8A1Zk9eCBFaNoRhYXXAgv3bkbbrutwnMAWLQIb5bl2bN9SdxqKYeHv/5SM0KRkbB2LVxxBcyZA59/ro57xHrySTiHJfydl7xh+oOJMlaDXKnmhOHii9WALajZj4wM1eZkZKigKAcOlG1fyutBdajoHE8mmfbtlRvwW2+pmSBv5P9Jk3xRlZKSVIO9ahUsXEhRfr4vsWo5oqJ8QVuKi9WSoJJSA48/Dj9f/y7R9nz6xe1gYdxVNO+WwPScsWT2PJtZXM3n4hq+WNGG23iTQawmbe8aDqIW0Q+NWcc3K1KZ8UUU2zeWct7fTyUroTNb/rSz+Hsbo09LZ8k3+d71rhpNSEhRbogffFCxbm3cqDwCBg1SnyMifKtnioqK+POvCBJSVCAY/xgRI0YoD9+aUlRS4gsaE4CLR0jat3NhsfiM1etP2QBACdFEvzqNRFeO11g9+8nzefZZ9T8UE6MGnF55RZ0nnS7vGprXPk+pVK6YRHOTDTajCS09HhzJS9zLtTHfEBcH+Y5oCkvNPGBUI7mWPj0QsTFqBMatXEVFRSp32uDBvkBL/iQkKCUTAn76CV5+maIWLSijGB6MRtWYffut2l62rKzrxbx50L07nHkm/PyzarCdTmz2Ypbs6cAZZ6hBnrZtg3hTNm3ypeoAb4f36dIH1WhSt24cXbWHZmQx65eWlFolBaMnMPNrMyQnc6zPOXWKzq2NVU2joEvkAfJLLSSQyzpXf/75bV/VgV248PgoKJ7e6q23qlb7X/9SC9u//x6eeqrOsmRnw+TJKojShg3KUM3K8q3JadNGDaY98QQsOekOuOcebrpJeYMEE0ukAZtDq7Cm5rz6qvIGOPVUyVcfFPDss6r9fPtt+OYbycN3FtKuHWz/rbDmlVdjBHXLFtWmt2sH774riV70LTeNdXe+b7lFWc8rVqjPsbGq51pQoAzYbdvcCRsDc8YZ6v2rr9R7Xp7qH4wdC+mW9hwuiOf3gq5YIlWnd+EiI+++CxIDO3dI3nxwL13ZQcyODbR+7Cbu4DUmXHSUa7NfpQ9/svNABABFpgRyiiIYdaWRNoV/kWAsIC+v5rdLowkm5ZeJG41lo+62bx/8gVMA1q0LvP/WW7EdysS8fDFxvy0jNbqIZ879kY0Db2Jw7CbEo4+QtH2V11i1WJTHZLNmyli121UqSosF7GmtA0dYC0BMgkkbq5racdJJxFOAxVZIz57Kc6aoxMBpHY6wZQuMbbZYGZOvvAL+Ee9jY1UgBv91K/54vP3OO08FZDCZVL61iiJujhyp3n/7DW6+WW2PHatmbVNT1eyrHwYDHMyPZ88e5Zl7+HAtJzT/+EMtufvuO1/aij59lFE9a5YyTm+4AbtN8sTmserzjh3e3M138QpHvltLwcJfiI20Q0oKnbZ8h8yqfb5z3dPVhC2rVq3ybs+5bjZ5RSbatJKcGalcIob//izMnEn2t8tZvhxs+9L55aelvqRup5+u/kwef1yNav38M3zxhToWKNpDNeQAFXF0zBg1u3rvvTC4wxH27IEOHaBTR8mZg+zMnu0ufPXV8NJL9OnjTqkRRAwGnfNcUzs8I66nph1mzE3K997T1xzb9Te+/Vp18jKvvcd7Thk9yMo6vtIbb1TvvXqpB7O4mFXffRfw+lu2qEHaZ5+FogKXCpPdrp1ag7N2rc+FasIE1dvu0UONMkdEsMrhgClTKvxu55yjxrEGDlSfs7N9Ez+lpTBokFKa885THeG//lITuVJC5y4Cpk1jbPNl9D/8HTzxBK9NXEepOY7bb7HzwH/beK9jtavRc6OthKWcy86SNo3GWPVPpRSs4F7BqCtUMjWWIGbV4dgItU6tfLtUGaefTo3PqS6rVq1Sg8ADB/pcHAsKfOtk3nkHu8uI5ZX/kPD5dNJiimgdX0Dv7bNZdc2rMG4ciQnQb0QLoGyQtZgYn4dkdDSUdOrl8/OvgpjkCIrym2ZkVH909ooQcdVV3GX9N/feqyYliooEseuWctJJ0Co6V/nYJia6c0f56VZkZOBQ3gFYtWqVGjly11EhSUmqzM6dMHOmGnHy0Lq1dyZk1apVHMmNxFri6xh61FDKsnZ1QJxO5ep76qlq1nbTJtVYO50qn1b79qpPO2oUx2yxZM9ZRoo5D44cwXrRKDJ3F3Dr8H1kk0zOU69SSCyx9hxYs4YIYSNjR+0bSG2sasKWBx54wLvd4827GTxY8PP8QlY0uxzZsxcLs/qzacZatqfHcvbZcOfAVYw84wY18gS+EIeghpkOHFCKd+gQPPKI0uJqWHv+ctjt6r/CaFQpNX7/Hb7dewq7d8PJJ0vWXzCFoUe+oF10lvLbr2Btnabm1Ffj2OQbYWBY8Vxmczm9986jn1CLrR8Y+RcAYs9uru31OxGilAeyHuQvtbuMHtCqlbL8CgvdkVxQC0bXrYOtW1VjnZzMAyNGHHft+fNh0Q+SCYnf0fx/7/OunKQGmLKzVeAWq1VtP/igyqMxZowa2h45Er75hgdA+QZWwr59Khr3mDEqPUZWlop3MW4cLFokWLlSfXYHUTyOyw5Np/eBBcrNKzqavGIzcUlmJtydzF13qTI9eoDFIvmNfnSNVov/8tKLj69MowkiyfM/AcrpYxWsVEvtanROdSlTp2ft2r//DdOne3vKNpcZc8+uNF8yk7WRZ6syqanK7eHzz3lo12Qmzx/N00+XnfW1WHwBmjIzj8/LGohlS1WbHp0UoQbCNJraMGsW5gVzGTZMOfYUF0N0nDuNQ3z8cQFIaqNbNT4nUFTPTp3g/vu99aUXxNDa6kt1s2mTes/IUCvjKqVtW19YcpNJ9ZNfeknNqu7e7ZvscTgYwDq2zdlCKlmQk0O/9W+zemcKQwcWYSOCLZucFBKLMyuHj5hAm/gCDu2offuojdVK0J3jhmX58uXebZNJxVNK7paqfPU3bSL9sOTzv63k7llDmGz+gLczL+evLct8FaSmqlkeUCNSWVlqBPjKK5Uhe/hwYJeicmuB/OX480/VFwe1fKjr3y8jjQxWroQ5GUNIzNiOuOVm+PVXVX8VnWqNpiGIvWYkl2e+w0FzR544dwWJIo9/fXUS53bcyzl9sll6sCvZl1zPqmPd2H35fYBbD955RxmkLVooay8uTrk9eXIx3X47V8ctQAKkpbG8XJTA7GzlIfX1bIH4ZQVMmsQNJ/8Oo0ap4euEBOXutHy5yn7+5ZfKhcEzUj18OMtPPbXK7+fpR7z3nhJxyhS4/nq1zCY+3jfT5M+SJX4fjEZfJLX+/bn5yhxPf4ALLlCd/9WroSDXRbdt8/iroA0//3sVuRsPVOv+azR1xb9dCuU51arzySfVjOeSJSoKf3GxerVrpwo5HUR9/j78+CPRjnzo2ROvOxKQnKIs1MceK1t327aqalBrcO+5hyo5e6iqKyY5gqJCbaxq6sCFFxIXp1zSv/jCL+XgjBm+mApuGlwfU1NZvnw53Q072EZ3jrw6i6lTfW3djh1lU0MeR14epKf7vJpatlSTLR07qk7vs8+SY0zlf6ZRJG37lXjy+f2LbbR2HuDDjf2wGmJYlNOfkQ+eBMC1fMYhWrOVnkzkI+LaJ3HsoF6zqqkHhBDDhRDbhBA7hRAV++GFkqgoNTwkBC1aCo7Z41lT1Jv7Uz4AwGWJgrfeIu+XTWrh6MaNakh23jw1tDR5ssohc/rpyvd+0yYVrcLhULOsUqpZI4CHHsLnz6t4+WU14QOoRX/z5iGAXie5IyilpakZp5EjVT0tWoT2foRk8ZGmMVBnfUxNZdrfDnL2vf1J73IWXHEFi6f+ymlTzuHFV81Ef/cF+9//kRf2j/Gd8/bbbLjldZ43PKTWyyxaBFddhTzrbHaeORFeeIGfi/rx6aNb+e1oa7ZkuQdrfv8dtmwhJQXuPPM3Dj/+pjJGd+xQBqnRCKecAh99pPyDPQ3mmDEqKIU/NUhSHBenlt907eoNpFoh55xTboe7I82kSTSbeDHNmqmPl17q6wBYoozQrRsGA3Ton0LO3kbiB6wJOmHRPjYU99wDH36oopjee6+aAn3iCdWu3n47+57+mBhTqVpQnp0N558Pd90Fzz1XabWpqWocC9R7oImliohJjSo/7qw5gQi2Po7xawYxm8Ov75WeDsB6Vz8MPy+jee42HnkE4qIdFBVKduxQ8zwVRgheuZKclj05GNlFfe7cmZ3/mkn6il18yRhOmvEgyd2bcZPzbVLJIrF/FzaZ+9Er5Qg3bLyPpCSwyFJiYgVXX62quJpZ3upPP9NI9pHa5z2ulrFa1Y8uhIgQQnzhPr5aCNHB79jD7v3bhBAX1VpSTYMihDACrwMjgJ7AeCFEz1Be84033qiyzPTpquPY6rm72D/wSi4a9zmTVt1Ci/N7IREgBMXR7tnY0aOVMTlqlBquffRRrEQw96Y5ypWiY0c17ZKfz873f+bIYRfFV17HG//3f3DwIEdX7uKUghVMe6pUGbVz56pEcJGRat1Au3Zq3d7Ro/Cf/yi//5q0rvXECd2paiIESx97v3gTSRcNIvLT95Tr3rBhRJ3andNHKxf6VtcPw9iiGaf0cfLGlVfCFVfw1Z/d+CLzPJy79uIaei4b+0/kri7z6frLDAr6nk2XgUm8/lN3hhh+wWx8lWmtX4GXX2bD/70NwIW/PEHLs9wNYpcuSjc9jBypFqdVQnX+F/ypgW1bJ5J6tiTnsG/k+Pvvv6e7+m69a9puahoX4do+BuOcatUZH68GZgsK1IDywIEqLH5kJLhcxD52j1pDExWlesupqRXnyAoSUYkRlJT4DAqtjycOJ6Q+mky++s46S02aXH89zxXfxd23lrB/Pwxoc4SMxZtYdPts5B9/qrKTJ8NVV8HFFzM3bRJt09fgSkjinpJnuOL18xh2geDOuA/5a5uBKVMgpXsq666axsdfWHjXPpGbfhgHwOJZx5g7W/npv/++8iA2mSS7trhT+1xg4VhG7deQB1itUxa/H/0C4CCwVggxV0rpH45xEpAjpewihBgHTAPGuh+OcUAvoBXwoxCim5Syya5697jz7n2ugmhgjZdBwE4p5W4AIcRMYBRQcVjOOrJ161YGeeLvV8Ivv4BwjiZm3GW8FzmTxMQzMRrh2mvVaNj996tBsCuueIfvT1Ht6PDh3bFe+DGb407jh92dyej6Ix9+Gcnfbirhs68tbJ7Uiv6dx/F7p2e5aNJgdvQ4zNKC/jyT8gJE/qSis2zcqBajn3aaWojXvbsvUd1994XqtpThcKaJQ+vSaT2gZbXKV1OfNeFP8PTRbPZFIyqH0QjTLl3OFe/G8sFzS1n66EISp+Twf52TME1y0LUXpKcnEBenHAmaNYNRo8xs2wb5BYIzBu/g48yJYD2LKcv6kv7NapqPnAd1GJSu7v9CfRPTPJbNfxk5uimT1JOSueOOO1i0aBGdO3fejOosVavdbBDhNXUlbNvHup5TozpjY5XBajSq/FRXXKE6wqBy0IEvb1WIMRgFew8YOLYjm8ROCVofTyxOSH3cunUrg/7nXlK4ZQvMns2E517n/sfNjD1zLwMcM5nxbiT/OXodU1fMpNkF+bgWJeCMbIXrqmtYcrg3b45ZzJPNtvPeR6n06aMcFFetimLj/5SDVFaWgYR33iXWbcX16KEcKQyGdtBHuf3HxKjUspMnC9q1i+CDD2DgkAg++3sM1+7PI6FdQo2/W5XGKtX70UcB/3RvfwW8JoQQ7v0zpZSlwB4hxE53fb/WWFJNQ9Ma8F+QdRAY7F9ACDEZmAzQzrNWpQ68+uqrTJw4scpyQuCNlNKv36ssXz6R6Gi1ZPToUeUGmJ+v4rbcfbdK9WS1QtQb13F+FNxrhNzcCxjxgiQ3T/D+NElKqqCwQOJ0Cc4YLLnrs3u4MM5Js+TBarT46FGfm2DfvurVADz2fmeiUwLnnKyAev8T14SEetPHvs+MYcsd2QwYncWUsdCuXRJOJ1xzjZokiY5Wy7xjYpRuOZ2+wEUO+SaL100iI6MvN78OKSmDq75gFVT3f6G+EQKeeKctkfEW1qxZQ5cuXejUqROABKrdbkqpY3w3QsK2fazrOTWu0+PKcOed6t3jLvnPf6r3uLigXr8ynninHRFxWh9PQE5IfXz11VeZ6AnrP306fPklcUJwbOhGcostpLa/hrbbDNzUI5LDm0ch7DaM1zyNIdKC0QhnxRlIS+vG9u3wyIsqOKH/SrYLL1QxG0CpeVWa4QlafMMN6v3xN1phjjZXWL4yqmOsVvmj+5eRUjqEEHlAinv/qnLntqaRUVnwo+rOoDaBAEqB5kLKPKpSyreBtwEGDBjQIH/wQvi8CFu18i0/jY2t/DwVOFjQuo16B4iNU+9RsQa69i1XQbDz0NSSdoOrN6PqR3X0uVHhr1u10cdG6gVRf/oYE0NUtxiiooW38SkfRdcT9Dqq3LiJwaA870+UOGOdz1M36NCaQ7Qtm5G9Ju1mgLxAmjCnUbSP9UoYrOnrdF4HAA6t0Pp4gqH10RNkAYg67RQ8TfPJ7pVpaZ0q7hR7cjWXD7kiRN3UutvwTrU+tzrGapU/eiVlqnNumREOoFAIsS3AeamE4Z+GmFbh/rCU159ysqcC7QOXBNSfu/+/fRvgcEWF169fnyWE2FcX+YBUIUSN76EIfiNZKzmCLQPVf54q+x2r1Mlw18eKdK6KYxXKW1l99YnWx9DKEWwZqPz5TwLi33vvvX34fsfqtptlCzWAPtZEJ4Ktj1WdW1/6qvUxtHIEWwYasT6Wf6Zr84zXRS/q0l+tL1m1PoZOhhAQrP6ql+oYq9X50T1lDgohTEACkF3Nc8uMcFSEEGKdlHJANeQNCxqpvB0qKbIW6CqE6AgcQq1FvqaiwlLKZhUdq6FMDX4Pw0GOIMpQpU5qfWx4tD6GtxxVySCEOB34p5TyIvfnh6l+u1kGrY8Nj9bH8JZD62PdaKTydqikyAmpj+EgQ6jkqE40YO+PLoSwoH70ueXKzAU8jtdXAYvdfv5zgXHuKGsdga7AmuCIrqlPpJQO4G/AQmAr8KWUcnPDSqWpBdXRZ02Yo/Ux7KlLu6lpZGh9DHu0Pp5AaH1selQ5s+r23ff86EbgfSnlZiHEU8A6KeVc4D3gY3cApWzUHwHucl+iFrE7gKKH9h4AAAYjSURBVDuaciTgpo6Ucj4wv6Hl0NSeivS5gcXS1AKtj+FLXdpNTeNE62P4ovXxxEPrY9OiOm7AAX90KeUTfttWYEz589zHpgJT6yCjh0rdLsIQLW/dCReZwkGOoMkQpD/xcLgnNUHLW3fCRaZwkKNKGerSboZCnjBDy1t3wkWmcJBD62Pd0PLWnXCQKRxkgBDIIbSXg0aj0Wg0Go1Go9Fowo3qrFnVaDQajUaj0Wg0Go2mXgkrY1UIkSyEWCSE2OF+T6qg3ER3mR1CiIl++6cKIQ4IIQpDLOdwIcQ2IcROIcSUAMcjhBBfuI+vFkJ08Dv2sHv/NiHERaGUs67yCiFShBBLhBCFQojX6kHO54UQfwkh/hRCzBFCJFZQbq8QYqMQYoMQYl2Qrl3r3zRYCCHauu/3ViHEZiHE3QHKnCOEyHN/9w1CiCcC1RUkebQ+hpG8Wh/LHNf6qPWxQeXV+ljmuNZHrY8NKq/WxzLHm54+SinD5gX8G5ji3p4CTAtQJhnY7X5Pcm8nuY+dBrQECkMooxHYBXQCLMAfQM9yZf4PmO7eHgd84d7u6S4fAXR012MM8T2ti7wxwBDgNuC1evj9LwRM7u1pgX5/97G9QGo43KMgf/+WQD/3dhywPYAc5wDzQv1buK+l9TG85NX6WMU9CvL31/pYj7+d1scqZdX6qPWxPp9vrY+Vy6r1sR71MaxmVoFRwIfu7Q+B0QHKXAQsklJmSylzgEXAcAAp5SopZXqIZRwE7JRS7pZS2oCZbrn98f8eXwHnCyGEe/9MKWWplHIPsNNdX1jKK6UsklKuAKwhlhEAKeUPUoUcB1iFygFaH9TlNw0aUsp0KeVv7u0CVMj11sG8Rg3R+hhG8mp9LIPWR62PDSqv1scyaH3U+tig8mp9LEOT08dwM1abe5TX/Z4WoExr4IDf54PU7x9Wda7vLeN+mPOAlGqeG2zqIm9DchOwoIJjEvhBCLFeCDE5CNcKu3vkdtvoC6wOcPh0IcQfQogFQoheoZIBrY+hIOyetWqi9VHrY3XQ+lg/aH3U+lgdtD7WD1ofQ6yP1UpdE0yEED8CLQIcerS6VQTYV58hjatz/YrKNITsdZE36FT2+0spv3WXeRSVl/fTCqo5U0p5WAiRBiwSQvwlpfy5LmIF2NeQ9ygW+Bq4R0qZX+7wb0B7KWWhEOJi4Bugax2upfWx8nODTbg9a1ofq0DrY43Q+lgHtD5WjdbHGqH1sQ5ofaya+tLHejdWpZTDKjomhDgqhGgppUwXQrQEMgIUO4jyg/bQBlgaVCEr5yDQttz1D1dQ5qAQwgQkoJJMV+fcYFMXeYNOZb8/qGAEwKXA+VLKgMolpTzsfs8QQsxBuUXURfnD5h4JIcwoxf9USjm7/HH/PwMp5XwhxBtCiFQpZVZtrqf1UetjZce1Pmp9rCFaH+uA1sfK0fpYY7Q+1gGtj5VTn/oYbm7Ac4GJ7u2JwLcByiwELhRCJAkVfe1C9776Yi3QVQjRUQhhQS1enluujP/3uApY7H6Q5wLjhIrU1RE1wrAmjOWtV4QQw4GHgJFSyuIKysQIIeI826jff1MdLx0W98i9puA9YKuU8sUKyrTwrD0QQgxC6fCxYMrhh9bH8JK3XtH6qPWxFmh9DBFaH7U+1gKtjyFC62M966MMccSsmrxQPtU/ATvc78nu/QOAd/3K3YRa7L0TuNFv/79RIwou9/s/QyTnxajIV7tQ7gAAT6EeWoBIYJZbvjVAJ79zH3Wftw0YUU/3tS7y7kWNyBS672nPEMq5E+Vnv8H98kQzawXMd293QkU/+wPY7Pk+DXmPgvj9h6BcNf70uwcXo6Lb3eYu8zf39/4Dtaj/jBD+Hlofw09erY9V3KMgfn+tj/X/fGt9rFhOrY9aH7U+an08IfVRuCvUaDQajUaj0Wg0Go0mbAg3N2CNRqPRaDQajUaj0Wi0sarRaDQajUaj0Wg0mvBDG6sajUaj0Wg0Go1Gowk7tLGq0Wg0Go1Go9FoNJqwQxurGo1Go9FoNBqNRqMJO7SxqtFoNBqNRqPRaDSasEMbqxqNRqPRaDQajUajCTu0sarRaDQajUaj0Wg0mrDj/wEeYZyudUytyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vXw7AxIm+kcdVn7+jG12Nrxk92r1dWT1Yvnw5cu8+bHlFmE3KKA0MdDeqL7wADRuqRhmHg3/1S2HGhQthwwZCQyHXalCt8IwZOBxgty/XRqmmfiFlcZD1sKEOtR5vXh7s2QMWC1arii37/HN1+u7dAMtZwnC1Iz//1Do9G027XW1brSxPSMBmUT3WcHI4eSCLSJFdougnn8Cat7fCu+8Wi+fio9gnWL/eqX+hocByQkMpM6b0m28qdzuqgzLc6cvy7JuOWmbka+fyIovKq0+j8cTb9tFuhyefVGEoNht89ZUql59f9kxpWZhMp45DGY2uQaDldOrkjk2tT3hpAtQupUdWyhppOVt81+ta7Gh52GwqG6dFJQjk3nvh1lu9N0rPVryMYfNsdAGOSSmvLrdSjcYHyD17sbVoRsNAC1s3R/Hxx+5jFgtERTmXfbHbwWBAXH8d/Oc/hF53PrkFzhGqRx5hmuVhvvlGdZL//lut3daldH5pTaUor/3w59mL8mSrc7FrQoAze+e6FVYKlm/GPGKQimPJyiLPHs7Ro3D//e7TAWYxSW3k5anFf11s3QodOhSntU7Kj6aJ0YTIzQUhKMxQjWvYyCGcPGIh0uiRCltKzA4bhYZAWLIE7HY6/PAGBw7A2LGwIv0hhv7pvJwjFCgiONRwSkypzQYmgwN/mt+48sorufLKKxFClHCndx2XUg6pjuv6sw75K/XtnrkmW9LS1OeHH4aFC5VXvd2ujNIzzZS6aNasZOKzxc5b5fImbNMG6kh+zgrhP/8kmrMKl1Hapg3ADIKCVIfVW/fdGTNm+FQeX9dXnXgRwzZEStnYY00vbZBqTotrlqQyeqC8BmcgDFBQIGkRkk6fW7ty7Jj7nOD0eDXhAkrJXVOoGRlqptTmHI0KCCA0FK64Qs2Y/vSTirHRVJy67ipXGfz9O+cVBXDPPZBPIPlrN6mdAQFgtWK1qrgx18Cs0kkPfczLY+/Vj1GsWH36uBtSoNX81zAUWIlPD2FGXByFdyk3orBmkaQctRJhtvL99/DFF4DFgnnB5+RaDRxMDoMDBzh4EG6/HT77DJJoyskUqYzSkBBgBgFBJY3S//wH5s6FEEcOJCay7PPqd8r57LNqv4RGUy6nax9PnoTu3dV2Soo7LlwZqDNcau71TOn118Pjj7s/u5IBKu+hGcTEaKNUo/EZhYVgNjno0AGCg5OK1zD0liRXRggf4ev6NJq6gsmk9BHK0IPjx2Hz5lPKTJkCBw+qTnTr1pCQkIRVhJJrgUZheSXOvXigJCY/3j3JYza7FzB1OAgJgdzCANULDwyEOXPIyUkqdl3S2bk19YUfP0lnzhywY6LA7uEWZLWSl6cGZl0dzWuvBUgihpM8yauQl0fnH6fDl1/y+fQkttCb2A6REBjI779DoUPV1yLzbxLybRTa1VRrWMNAUpZvJzIwn3/+cboFZ2djduTzf4uG0PXkb3x2ZCAAu9emFYuUfNCC6cAeZEgokIQ5LKBETOnu3ZCa4iAYK8ydy+W3NarGO6cmiseOrdZLaDSnpbx+Yna2+n3u3Qs33wzPPgtXXw2dO7uMySRiYyvmvlsel10G3bqp+rRRqtH4CJsNzP9sBbud1q2nc+GFav9XX6mA7oKCkkHepZk+vcyEspXG1/VpNHUFs9ltlJ6iB7/+Cv/9b8l9q1fzwQfw2/RNbNqk7NbJk6ezyHIpGZmC6LBCLu2aDMDGkMFY8xxYRDihocpNn4AAFTznvHhoKGy7bYayPgMC4J57WLdO66Om/hGSsK9421pY0ii1euQh6tHDtTUdGwEEUuBe9Pepp/jm8Q38L+B2UtON2E2B/PZbyeu8eiyBVQwGICw2mBQaERVcoFyCpeSzD/JZjHJ5jjJkM3bHo6pgdhaXNlcyZu5P4ZejnTEu/gGYTkDjBiViSu12sP24lCDy3f6K1chVV6n3hQth/Xp1/YSEar+sz/D3WfzqoL595/L6iXFx7nW4N2yA779Xy7lkZsKrr0JExHQ6dnQn+KwKHTtCWtp0wsOVMVzf0Eapplaw5anU8gGikJAQiPzhU0DlfEhOVuu1ffjh6eu4WjulajRVJiDAbZQCcN557m1XIhXXtOU//8DgwQQEwB/v/01Oltp/5Ig6nJFtIDLcQX4BxDSwExxmJC9XYiGcsDCYPZuSRqkQCAGdYtKci5w6XSY8RqTqwrIwGo032DEysec6bjN8QUGWR+KiCy4gL1cSZ07FSBFGm9tCtaKmVnKfm0YABUxiJhJIDmnD1X0TaHFio1q6xYO8okAmoRIYRbSM4mSDTuSaIli0CGReHrte+IqjtAYg2eGe4TQZJYYilSCpgEAApLObGBTkzL7rcMBTT2E2g2VfIkHk88Qn57gvbrGoKSMn115bdo6mU3AlmCiF67u5jPY1Hx/gx5e2cfgw9OwJm/4oM7m8RlPtJCXBRRep7RMn1PuhQ/DBByqp9iWXKOef4GA45xzo1cv7mNLTIUSpNrse4ZdpZSozslJfEgCVhzfJnsrb74/3xLZxGwYBRlnE2LFjSX/gUPExIdSIUl5e+eXHjBnLo4/6Tp6x2i9Ic5ZiNrtTy48dOxYeeMB90JW5oWNHNWLUvTt38BGWY2nM4w4uTM8HgggWo8mTkJFtJDJCkp8vSPx1L4d6xWPDQhoNSrrvpqcrt4itW90zpOnpqgUHejUYXGyXSqnclGJiauyW1Hvq8uxFXZY9p8O5PLR9HLvoQkHGeUhUCnUA61//8H3heEK6tuXbXZ3YygvAWAoJQBpMNFj8KUWYWMj1DGU5B43n0dUWT5L9/FOu01v04IS5iNzCAMLDIZ8gNmd3ZOtxOL49n2Qex0QhndnDP3QvLpdmi8BgV671ebiTKvUy9KJ7h3yOnQhg4m0Wpv2+iJDrXiW9MIJgrHyVq6Yxf/4ZhoxoQED/vrBuHaBmjtLSVOKW0xIbq4LxwsNL7J46VWUyde1+++f2AIyPfIa0tJd5atAfLC+61LsHoNFUAVc/cdEi6N9fGZ6Jieq3fewYLFigHIvuuEOlTvjyS1XurrvGEhcHH33kGznOO2+sT2Zd/RE9U6qpFQoTTxIZYyZ/4w6Gt26tdjpHV/MnPoTRqCZpPNdhWrnSPToUHT3cp/IMH+6D+vbo5Xk1dQ9P991iPXD5BbmM0pQUFQcqBF9yMyEONauRk64KhjqU/31uvpHISLAWGDBbszFTSFFmLvtlexot/lgFormM0rAw5du0YIHK3pCXVzxD2qX5hcW6X1io+qsaTV0nZ8IjhJNDEPlc+8s9GJAsK7qUo7TEejyVUHLp1ttMk5Acxnf9E1zLwVx2GTYCcWCkGQlEkcm6tE4036EM9GHnZ5W4TmDRQKIbqR5rRATYCMCWpUZ5LQVmHBiRCIKxMj7ky+Jye9NjKbAZmD3km2Kj1EQhH4UspnGsgyKbg/V/GdgfcS7Ba5eRHt6KIPIxGpTHxF13OjhEW5amub0toqMh45k3yr8pUrpTkzZrRm4ubF7k9suNiVFGbXi4O7h8cNuj2P63EIAAu/WUKjWa6sDVPk6dCgcOOLPJAw0aqDCWjh3VWEzphJ3jxg33qcdPt27DsdnqZ74FbZRqfMvpsncuWQLLlgFgKzIQ1SiQ3H8OM+aBB3iDR/hsmXIjsm7YgUnYSV20jk6d3MWvuw6ynG3v66+PKVH1jh1VE3vMmDFnPulMnHPOmc/RaPwMT6O0WA8iI+Hrr5XPXEiIMiA/+gjCwogik+EswUgRj0xR0xeZ/JvO7MZaYCQmshBroRFycogjibcbTKEJyRjuuhNmzlSzovPnq2u89x6MGQMvvQRr16rrPf88CzY9Wy8XBq8qrhgtz9nCM30+G/H2HtX0fToQH0g0GQRSwLE8NdJyufV72nOAlPe/JfaBW6BdO+wh4ZgMkp4425T+/YvraMlxosgEIJIsugTsJ3lbYvHx1sbjfCF/Iipa9YLDw5UrriWgAQDCqLp9dkycPziEW4UySptzHIACu5F7+22hgCAefEDSqJHg7g5xmIOM2Aok7RtmcID2BG75k8yTNtbTnxy7MmBDkg+xnZ5stnUrliciAnLmLYT331fpekuzZQvMn88h2nC7ZSb7fj7AnSNTS5TPzpKEr3Y/q4tj99AD1egHUvfdd+uSDvuzbNWNq30MDlZGqcv7p3Nn+P131Uyerpyv+PTTMRQV1c/QFm2UanyLc4bljf84yJ02s+SxdeuQ6/7k44/BZrERFQ25iZkgBBbCScxWGv2pvA3ju2+RufZv1Vl2GrquDGcuPGNK58ypzi/lO26/vez9DgcMHVqjomg0gLIRiw1Az9W6n3kG8vJ4/I+RqrV9+GGkQxIR5iCLSOwe0R8hWFmDyuAZElCk4r+efZZg8rkq4nd3nceOqQuazaqjPWyY+9jx42roNzYWIe3Fa7SdLuFZjbFzp5otriW86QiWd05tGWA1yZm+u78QGm4kEBsBoqjE/k6h8WyjF41eexg2baIoJAJTbiYm7Hz+uXtGBqDNjf0wmIxsmLKE0OcepW1MDgGB7t7pq58qP9mAAJg4EUJD4Uh6JDlmZZQ6MBCNiul+57dutC7YS4sWkneHfA+AtSigeF2a56cILPkmMBiIjZGkpJtoG5LMnJSRCCT5BNGjaSopRQ1VWYLZSD8sR1LVoBYQHibJJhJOnGDbhgJmTdyhQso3blQCnzwJO3aQQTR/y66kHC/gEG1h/35YuJDIvESyr7qZkPz04u+46khrClHx5w2p/iRLGo2LRYvU7P3YsXDbbWpd4f79VU7A8ozS6kAIbZRWK75qOP2tEaoKdbkz8foT6eQ8+TIAtiOJyvleCDq9cx8PPggnUwVRDQzkEkq3w4fVeXbl8zDbejt/Wnsxh3tISID9j8wurjfXovwVWrbsRuPG7uvFx7v7tJWhW7duZz7JB3zyiXovPQuUmeluo0tTOrOiv+BNJ7j0qy5SL76PlCWUw9P2DAx0JiLZv5+OjZrxK5eoA2pBNKb/OQDCw/mWa8kJb0rzoFSyiOQKlgBwfsgOruEAMaSx9vFFBAVK8otMKpgMVO/YFaf600/qvbBQtaiegaL9+yvFCA6meZbAZoPmzeHNNyv2VYuKisPZKkZZ+fVfeUW9P/88rF5diUo1GjdOlSpOIrSMobQwJTLy/1qzj44qC8rixdiNAZiSE+kG3HKLyuw5/XVJNuHEdI8jb/go+t3QCmPrFjSMsrM+uwvLW44H4OLBBq7mIAUFKtu1S8Wy85URl2sPoluEexHhsKH96dwZru6m8joUOEzQXsVthoaq/EPdunUjJMJE3lc/EVRkYWDD3QBs4jzG3+sObEukGTN4BDtGVox+n4QVu4kIKSInKAYCAjj3w3t5471QkuML4fzz1X9SWhps387ksLcIIp+98aFYCGfz57tZPXUtkSf2knU4jdjcw0zjcb67cT6WAvc1m5trb7DI19SldqXOtoVVoFu3bowa5TY+hVBZdm+5RX0uFQ5dopwvadKkG0ajdt/VaM7IkcwoNmyAYJmLhTCem2wnrk2gWutQCPanNyQuDg6mRXHNxRk8GPIB84D3znmLXlFH2D5YdV5/41J6GpR7znZ6UlgI/fpB7tU3gd3O/ffPIzrafV2XP/8pyZEcDrXGzBmYN29e1b64F/8Oxae88w7Dhkm2b1cfk5NV5jaXkT18uJoVjo9Xny/VORw0VWX+fGVgnTjBhk/2cNNNcMt5e9m42sqJE06jdO5cJkefz304PRwCA3G88CIAc7f0YhTfstI4jFamBLLb9WEJajXvEaZlzHNe5qLQbYSGCe6I+0XtcM2KjhgBvXurfRkZ0KqV2g4PVwZqZia0bVucpvPeka9RUAD79kGjRirLprccOqRGsCtMy5ZqrYm33nLvmzxZvXv6OGs0lUBKd2LrgvZdGRG4gnMXPo3FHoKtUHDVU8XrwFAUFoUxN4t5cXGACueOjBKEY2HYMLhgbEfo0gUAkW/FgGSIXM7L12yiaVP44dn7yc9XA05BQcqwLLKr7p7EQLvrenHrrepaMT9/yrJlAqZPZ83vEoEsViCzWTWh8+bNA7MZg82KEOAoKMSOGkTu1C9S1WNyt7Mh5PEGj7DtxUWEm/PJDo2DgAAaylRnbEWvAAAgAElEQVTyCGH1I4uYzMvs225l5jdxJGWF8ItlAOu4iAfebE0bDvHzmnC+O3kREWYr2YYoBJLHmc41nfeQlWsi0qwSMkmtl5oaYt68eTRurAaXnn5aJYwfN061UeB25y2rnC+54YZ5mM3KKPYcYK4PaKNU41N+OdCOuXMhRObSgQO89IqRdBpyL7NJyInAbCgiNlbNvnTpUEiv5qkMAu65ZB+bBz5EsC2LthxE4CAsWGnbSWJJS5U0agT/OzYALBaeeGJQsVFqtyujNCrKHXNazM6dMHDgGeUeNGhQ1b54bm75/0hOXJ0E1q+nIM9OrmpTufhi+OMPaNIEDh+GpUtVPpj+/VW1vkghrjnLSUmBlBRsq/9kx5sriY+HddvD+ORdC6EyB+t3S2HFCsZ+/zbBOBOH2O205ggAT/IaAFOzJtGs6BhZwU1UIA3wbPZjDHJlIkpLIyDExJvtPFz3DQb1I37vveJzOOTOto3JpOJL27ZVCzTGxPDaH8+Qm6tid6xWuOCCU7+SlDBkiLIVH3nEvT8trWRiJJeenRYp1YXi42HVKhX77urs/vADSMmJPZkcWu8/szJn0wyFr6jNeyaEc63N5s0Z2nIv85MvIfKCc+hn2sKhQ2CIVsYdP//MgAuKuI5vGdSuHaASp0REAKtXc955cP317nplQQG88w5kZzP5u74ADFq9mqFD3TM6oaEwYIAkCCsG7Fx3HXz2WSkBTSZ69xHMZiIAb3jkJho0aBCYTMpgTUjgu+T+vMyzbN6sUimYTPBeD+XR1Jst7KQrSxmOvWkLwlf/SLY5hqeXDKQB6VhN4by+ojc76EGn3iH8+l0m+wtalhDl0R7LOfbbAUIS9hNjTyGrQVto3aY4b8MK0xXExRSyvuc90LtPlZ+NRuMNgwYNokkTSF67jxZL5riX5y0s5OgeK2Lqy+WW8yXz5qn+b1hYuSsp1Vm0UarxKTm2QOSKFeQEuXuFgeTzPddwPCuCUEcOsTFqyjAi2giBgeSCmjlZvJiQ/HTyCOGEoRl5AZH8+dBXvMpTTJroIDYW3i+6k8wjmVjzLIy4ws4EwwfkZBRhsVD2YsIGg7Ja9+yBH38sV+5cr3qupyEzU6Vgs9vV9I7HtVasUMZysd2akUGoIZ/cXDXKZXJ6Onbu7O4H792rOhLJycpY1Wgqy333oX6XRiPBY0YyYfsk/vwTQoPsZJ/IY/aIxVgPJZG6+QiJ+QGE41wF3G7nOKqzmIaKGdts6UzczZcoPVu0iCnRalYxt6WzU5mfr2ZHXQsTulIEBgerHzTADTe4/Rg9efFF+OsvuPpqimQ+WRlqUCotTelDQyUC8+aphL3PPqtyI/3yi9veBaVrkZHuzw0aODcWLSr/JqWlqQtkZUFoKI6fl8K+feQQRs49j8KCBSz/KoOnbz1WdwLYNf7J8eMYf/mZsEgjxsYxLN3WhPfeg5tvdh4fPpwL3r+Di1hHrnNxzrffdhqiF19coqprroEXxh6Cf/1LLbHkJDc3l9mzSy7DsmaNIHXW1xzsM5p//ats0UJDYfiNSnkefljtO3DA2T4640w5eJBCh9ru00cNABkMcMmypwBoyyGOnnsdABn5QUQEF/HQiSd4dWU/9tIZqyOIw7QlwmTFRCGBgYLZKaNKyBF5YVdyCSWLSFra9nNX6mvYGzdVM7gFBbS27qbIGEiUI91prWs0PubIETU4+cknsH07/PILuTk5tG0jGbb/XQZsfdudEmHpUlp2DlGNUhmc0r/87js1UDx7ttuF7siRMkaKyiY4OJdnnlGu+WVFndRltFGq8SkWq5GfDnclPt9tlBYQRDJN2HS4IYEU0PyPBbzZ/l1MYUFqCLh7dxVz2q8fDQqTeYQ3aDT8XMwGO4Y3/0MWkRRs+pvYGEmuDGXv05/gyMiiYVAuHSOSyUzMo0kTFUuWna4yQowf77p4gZql2bHDHdBZHWRkqI74VVfB9u04Pv6ErN2J/L6ykEfuSGP8eHjt/niCc0/CkiVEmixkZamvv2uXmiFt2tS9QPjquQeIbeggfl8ezRoXnf7aGk15TJ7M7NlgK5BgNOKQ7r/8nTktOXTAQdxn08jPtbMvdgAnaEIYzgbUGfg8Nm45jSPyi8sFdO2gPBIaNuT5wmfU4oSoz8XuAPn5ymJcu1ZtBwWpHm/LlsooLQ9n5gaTSZCZrjIc7d0Ll1+uVpFZs0atAbdggUrLHxSkjG7PGO2sLIhMP8TGz/dh/2e3OpaaCiNHKv9+VwYlzyWcDh1SPsL79yPjmtLhoyfhiSd433QfLxU8BoDjeALm7FTYtq3iz0Gj8cS1ZoTJBF26EBtbxjqel11WvBkUdOoyE6CWW2n1yj1qUNczaVg5hE4cS+vN35z+pPnzS3x0TtZCq1bIVq3hhhvIiWzGN/PVCGpwsNK/qGjBr7/CgpaP43C69mbkBmI6rrwizmMjrVpJbE6Ddn/UedzCF3Qs+Js/TQN4c4bqnD8X9TYRvdqSTQRZRNK8ofrvSbBGq9GmrCz4738xBRiwDxysZm81mqqQmXnqvrFjVbbou+4idcU21fAcPkyXlhYeCHyfbrEp3HCVs8PmmXXzn39UDoKJE0vu9+S662DhQvj3v9Ug59Sp0KaNuqbDoYLIvaBxYxX6VSvY7dUS1KqNUo1vycrCaHD/UC+M3lW8ff9v15FME+JObufBhp8Vd17Xb9miGhurleDcVB4NmQ3ffMMDUxvTjASsBJOUYKfJ1+8A8PSGqzmWvYfgjasxR4aQuXEfjUnm8GE4/yIT+fkeixTn5qrOsNHoTuMpJUyZUkLs9evXV/y7JiWpxC02mxpOjohQ06L797PT2J2oLk0ZMkwg4o+TlwdH5q8nrCgTLr+cCJnNvA/dsTAWi5pFPXlSff7o9/Z0am5h+3vraJrxT8Vl02gADAaaBZ4kIS2I4xZ3EPZWegGQkWIjGCuHchuzqMMjwHrCQx280+190oOa0jwmn2OWaCxFgXw84U+GDlUruDgcqCnIX3+F4cOV/iQlKR0LCFCjKw0awEUXKSMwMFDpoZceCd8+8h8yM9T/SNu2yj33ggvcgzbpzkSc4eFqBunpp9XnWbNUnzUi+QDn39YRS/cLCA8pcvvzLl+uDGVQGSosFjZsgOR/Tqpzpk/n/d0XcyinESxejDAZKUIlVbHkCY5ZY/nvtsGVfRoajfesWFG5dolKtmdnqk8IihwGjKGBFEozV/zLnWzIZHLOll4CxkP7ycmBG2+EfcmRzAx9gjlj19KRfWzZIri3y+90aJTJX6ltuD1mMYWTHmT8eMGDDwneeQdeCH6NiIZmcggnq3FHom3JACQ5GivrPC8PHniAb34MpN2MST79npqzlDZtAHj8IRu/L81TfbrwcEhI4Ov2TxH76Dg4eZL1N96IzFQrRnDjjWo5I2eWaVxxo927w7Rpyn3n+HHo3p31nknyioqUwhxzJhu7916V6R5UPEqrVm5j9o8/1LvD4R5Mxa3fjRurJrZWePRRZVj7GG2UanxL//44TCrLX9s4K790eZijD7sTh3RiD9FkKHe5wEAoKOCxxx5TfwozZ6phnxMnIDCQm+8JpxmJ5LXozBbOpcGRzTQxp/JrWi/gMYLH3YA5xEzm3Y8yweJeH/XgX+mYjXZWDJ2mGrA//mD2j82VA35GBnTvzmevxpcQ+9FHH6v4d92zB15/Hb78Em66SflSFRXB4sVkL1TrsbZ0HMFCGLYNWzhEW8JN+dC7N82LjtB1+Vs894yD7dvVwFhhoepUu7igTQobjzel+b5fazyYvSKZZiuShc9X51SVinwvb+s50z2rjXi2/x2/iCBrBn+tsnDOXHfgZdvwVGY8l0meOZJgrGxKacm0dQOBxwgffC7/988EGu78neuHZNBQppJrNWDv158mTVRb7UrC5crc8thjj6msKAUFbqPUFWM9ebJqPUNCSvrVnob//vY5GZnufPdms3JvTFb9UxwJiZhMkoEDIel4kdK71av5z38kjz8OwSYbRorIDmxETp6JTJzXzcpyj/w0aYJMOsErUyV/rLIpI9Xh4L3FzQEovOc+Cm0OEgpjmdj2F+7jXVKtoSw/0b1Sz0KjqSiPPVaJdqkK5c5UX7apIRFRBvbtK5lCwTNEHKOR7GyVNXv23wP4+ItA7v5kAHcNOkCDBvD0W43p0TeAiRNh8Hs3YQ8KLfbAve8+IC+PqMaBpLfpi+zbDxEYwM+LJYmGFmqgy/kf0qWLM0eDRlNJNm9WaUdSM1Uq28T3f2TbnW/zU+B1rE7vTv6BeEbvUcn+Mm+8h0e//wF7cppK7/744zBgAIwerWY8b7vNbSFOmaLirq68Eo4d4zFXu5eRoQZvu3VT2yNGqFlTUC74I0eqkZ2WLeH771X9P/+sYrs8JlJc+thcNVXF0TI+ZcoU1SldsAA+/xwSElQIzPvvq+MWi3uNw4ICZTj7YHFxbZRqfEtcHOlFETQIK6BdUythpnxaXtqev+YfpD37ubBHLhFkK8PTYACHgzVr1rhnVUJCSubVLizE9Kma9mzHQbZ1HwtAACsIfOoRzF07kkE0sUVJxUVe+L80ouxpDF3xBJkpNlL/dQdf/t6cwlV/wLFjOHbuYqztw+Lzd+2Cjz9eU/Hv6gpkdfXQn30W4uJg3TrSaMgIfqINh8kRERwpbMZRQ1siYsxsuf4VXt44DKsjgOg/fyZ8yiN0L9pKt24q1GDj5B+QVwyn+7RbWH8sjqYkknuydFphjebMTP7lYkLJ5cbNTxBptPD6rTs4cQIisuN56IUoti88QAh57D0ZjRASWIMpyESLFqp8bLNAFgaP5Z9VaYSHq7EXODWMa80ap/64XHXz8ty91jvuUAVCQ1W8tRf8dXg3WZaS/opxcbDnd5VoKCvDQT/Hejp2hKRv/oCMDJIG38Tow9PIygKrIYxWHCUpWmUo/QNnZyEzU7nyFhVBYiJ3dVzNph8TGfXZtXwS+yg4HIy4JZp2gfEEvP8O+5sN5qvcq3jvkHKNDDDaMZh0s6mpGYr1qobKnam+3EHDCevamqioksdd/xcusrKUynfoAK3bKn0ZvGqKOndIJxYuDlEDsKNG8dQzRu6806OwxULbLkGM+3coo0ZBh1Uf0L2HUDHlI0cWr1vuQiLq59oY+FcyM3+SxRcUFsKIwRb+NbyQFhxn3rAv+K1oIP9LHcp45jJ4/WsMyl3Mi08r95ydE2fyRV4McxY3VSMiLVooz7ivv4Z331X92dmzVZKDu++G115Tht2MGaxxubq2bKlmSM85R7WRP/2kFjqdPh1eflktMXHrrcpn/t57laAvvaRGfeLj4fffYd481qxaBaisvy++6F5xzSekpCjj8uWXlRF6001Kxm++UbJMmgSjRql2ftAg5a7crp1aRqpv3ypfvt63rqUVyZ8Uyx9lE0JcIYTYK4Q4IIR4sqLlO3SAAkcAqZuPsWypQynliBHEDWiHJbAh7yztwPUJb6uFnYKDYfDgkhUkJZX8bDJBVBSLom5jAH/QOO8wqV0upis7EXm5BAw8n+d4kcisY/zECPL6DCA2fisNW4XTgDT+O3Yzr5y8m7WHm3Fx/BewdCkpqPzdJxIdYLdzItFBQb4k+YQs2bY5HOqPpKCg5OrlNhtceinyRDIyJhaZlg42GzIiUvkVxsRw4vXPaDH0HFpynMKoRhy2xJL63Nu0Dkvj3L6Cl4ev5QRNiNu1kjbfzWDB/j6MvMpOs5gCGv3wAfTsSe87+zC++1+Ek0POvlL3RXNWUFV9HDGskHntVEbAmSFPMHRMTIn1fSNHDCDmmYlsOdmSsEDlTh6fHszNN0PvnnYatIuG0aPp0j+S0aPVwO9pKSxUM6XlZaMuKzCuDERoCPedu14FlO7cCfHxtLds48+PdrGr/3iuabUNk8NGm6YFDA5az/blyTQlibCuaqmZk/Zo+rKJKSfu4Y3Ap7mKxawMvJLje/NIOWThhUtWkZQfRRJxBGCjD5tZ9KPgRLLguKEV6SFqCHpn5EVMbqQSG40dC+O7/0WzsDLij+ox/uK54A9UVR/rOg5p8EqFbTal/tOmFXtGlktUVKn1HffsIbxhAI8+qsazOnc10rw5rFyJcpssLYDJhLRWx1SRxt+pij6azXBswlTubLKEmIBs7lhxKw/E/o+Zc4NJMzYiNdlOYlRXrr81mAULlB2WmhPIzpRG7in6yy4rmQq7e3cV292woVorxuXi07SpSmddWKiM0qFD3bkVBg9WrrA9eiijdNgweO45NXGTlAQffghPPqlmKwcNUlnPitP+wmOPeSTzqwpZWSr85pJLlAfUhReq7zZ3Lisz+yjL12xmuf0S7N9+j+PaUTBrFjsG3U/Bt4vhyBEWBtysDOwq4JVReqYHL4QIFEIscB7fIIRoXSWpNLWCEMIIvAsMB7oAY4QQXSpSxy23qKQkomMHlRrMaXSGh4PF3IDQuAgCmsYof/smTWDePGZ5+qy6Mvx50rw5/xKLldvA3r00tCXxHhIcDsyxUZwTdpyBtpWM2P4qwSMuZVbqaAYNBgwGXmAKSXF9aMExGpzXDpYtIw4VGf5B66nQqxfHxzxGgW02H76ezuWXe9jFrVqp5SGCgtSfS2Ki2n/gAOlHcxj8yjB6fvowfeZO4gPuwmAU5NkD4eqrOWGNJKxnO9pOvJyMDDh23IB4/jniW6kZmz4TLyCRpjRJ2qLqHz8eQkNJSjURF5EL06YRsPZXnvztcsK7tyHnoHspiqVLl9KpUyeAblof6y++0MfoVpH0/PtzAK5Nn0uv4XGnXuelF7mq8UZybWYWR2Ux961cXnoJDCYjvfsI5VZvNp9SzpNiHTYa1WDO/Plq0KmSzJo1i7davqFmRrp1gylT6HFHH9Y5LqBF0WFeaPcpi7iaW1v+znMdF/DugSsY1fRPjDt3sPXZbwkMFNzIApZwJd0KNgEwpGAxLWc9SeeZ9zFl7RCakkSTwEzyCaI/fxIaqmZjExKglwq5ZV98MF3yt3LkiMqTdt+CgTSJKnAHt6L18WzBF/pYUUq0jTVQ7kz1uZYdPhPJycp+vPZa97I0XtO+fZm7hShzNyGhgrxUtyeR1sezgyrro5QEhJp5ZvhmFk1YzLSbt3P3tPace1sXiooEDRsZeftt5Tk7apT6Tf/3v7NKLDfmFcHBzHrjDWU9Xn65ytZ3883lJ0HypEkT6NpVzVr26qVCxv76i1mtWxef4uqeFuPKnQLKxVhK1Xd95RW11uBTT7mXqUhIUHkhPvxQjQ6NGQPt2mG/ZSwXFa1i16Y8ckbdzjDDChbdv4zXWr/HmJAfaBWQRMNRg/gzqTWTe/xIxMCe3DXWxuNpj3P5LC++12kowwIoiceDHwrEA38JIRZJKXd5nDYeyJBSthdC3ARMA26skmSa2qAfcEBKeQhACDEfGAnsOm2pUixdeuq+sLDydXD37t3069ev/ApjYtTo0E03qVbuySfZfd119Pu//yN2N+QMGA5GoUaaevTgwDiIiQmmQVwQ5/w6k9f3TeLHYTP5/sLXmfd2T2Y8l8FDoR8w6alYvjs5gJV5F3JF1Mckrh5LemYn3r8vgd7NUpApF8Lr+2DITOSKFfDgamTzFrBvH7sDHiD9hIGk4HY4COCr2ElwEmY9n0yzdkEcWa4GvBo1asbtVndmxWbNVAhBxy4mbE1b03rue9C/qerxFhSQ3W8oAWt/VT4ZP/0E48YRNXAcSz7dSKfbwW63M2nSJJYvX067du12ov6ItT7WT6qsj7GxQHAwb5z/FTy2sNye3Y83z+eN/IacFMOI7dQAzCqxUNeu3l2nWIefeEK5S8SdavxWhN27d9MvPl6NODdtCocPY0ASbsgltFkUxMcTOfIS+PF7iIujWWAgn6zoj3jjD0IOfM+8UZ0IfPJzpDkfgpbRr8lRjPkW/szqitmRT8fmgn3xodh79iFgdyDT32nETyEqI/+IEfDQQ8oT6cQJwc29j0Mrp2Dt2pEeZuCXJ1Zy+dtXaX08u/BJ+1gRztg2+rjcmep76y3vjFLX8k01Qbu2Dua/fIDxcxpqfTy7qJo+2u1qMqBlS3oDvYUAepY45dpr1bvRqHIa7dix23nZirG7oIB+J06oZV8mT65cMPTWre764uLo99VXShmlpMHxdrx+q6Bz02yVcKlnDxWqYhGw50MoLIITBdiff47C5m0pWjGXXfltaXBgI20uacX3f8Ryzdw0Co3BJKWa+fIrE02awIQHgsnJUc36yFeH0q8fLP4VZs8O4fXXlSfD2x+GUVgI+/ebGHsnrJkSz28z8rjk4d4V/454YZTi3YMfCUxxbi8EZgohhJT11NG//tIMOO7xOR44v/RJQogJwASAlq71Cc+AwQBffFH2sXfeeYdx48advoJbb1XvsbEwdy7v9O3LuGnTuDQO+vYt2dF2pbB/5VUB3MdliRAX9zptLXB8yLO0Py8abP/m6SsLSU25gSlRRkbf/QofTHsAozGFlOxwHMYGcO37iFyLiiYvnIA4ekQtIH55OzqGhXBnaD4BLUIpKgKrtRfNm8OuXUEEBirvjJgYJcell7pl+/hj9/aq/c3dHo4TJsDttxMaHq4Mh+efVy4cQnB5kaRppLIONm7cSPv27Wnbti2ABLQ+1l+qrI/336/eH14/+vRXmjGDR4C+fTcwLkAlKps503tBi3W41DqKleWdd95h3MqVKu4mMFC9x8ZyNA9E+Fcq7qVJE+Xe26QJOBxqGdTnHoWDBwlp2rTETO2GfdFIYyzWhHgsViOx3RuSmQVWa2caNwajcTTXFinv+7AwpYKbN4PdLsD4UwnZnnq3BSc3Knd+rY9nFdXWPpaHV22jD8udqb4qOD9UGyNfuYB9y48CWh/PMqqmjyaT8obzkt694e67q6iPjRurONQq8s7+/Yzr0EE1VELw4MMG9h8LJCcvFIa9rpKB5eYqF8WUFOW9FBeHwQBmowOTwcHF9mxyDFeRX2jk5RjlERwQAH2D4J5JagY2OZnicJ9XXlGTrkLA+c67vNgj2qJzZ/Xe+/N+xG9JobJ4Y5R68+CLz5FSFgkhsoCGQD1b1rXeU9Y0yil/1FLKOcAcgL59+9bqH7nBwClJF0rTtKl6Dw+HLhc5l8UICKNZN/XDBUAImg9RWlVyjsdVuRnO61Cq5lMzifbo4bXoJUPugoLUyxPnrJbRJOh1vXJpSkhIoEXJrBJaH+svdU4ffUpkpDtbrzNoJsQ1wOxS6nPOObVc8cKKHkREIICQjiG41C46Wr1cmEwlP0PZIbBxLUzEtVD/BWeTPrriPY+8NsLnddYRzm599FNMwWa6XK3bx7OQs1cfDQZlJXvQ4ZSk8K5shI1L7Tc4XyUDUUsnKwNK5J+A8l3oPQlvGs45TcPPfGI5eGOUevPgvfpxeI5YABYhxN5Sp8RQTX8MYtrpP1eBKsvsQ1m8qT8GtzNaaeIBz59mcyDxdHVv3rw5VQhxtCryATFCiArfQ+GNhlSzDNXAmX5P0UDE3Llzj+J+jjWqj774vXpTRyWvU2F9rG79O8P1/E0fK60HWh9rRx9rmgrqi1/IfDq0PlavHD7mrNRHf+qvVjdaH6tXDh9Tkd+Td9PSUsrTvoD+wC8en58Cnip1zi9Af+e2ySmkOFPdZVxrU0XL1Parrsl8Onmdz+4Q0AYIALYDXc+Ge+gPMngjh9bH+iWzv+mjv9y/uiKH1sf6JbPWx7oth9bH+iWz1sezTw5vsu/+BXQQQrQRQgQANwGLSp2zCHA5Wl8P/CqdEmvqDlLKIuA+1J/2buArKeXO2pVKUwqtj2cJWh/rBFofzxK0PtYJtD6eJWh9rJ+c0X1XKp9714M3Ah9JKXcKIV5EWcmLgLnAZ0KIA0A66o9AUweRUv4M/FzbcmjKRuvj2YXWR/9G6+PZhdZH/0br49mF1sf6hzcxpWU+eCnlcx7b+cANPpBnjg/qqGnqmsz+KK8/yOQPMoAXcmh9PC11TWZ/k9df5Kkzcmh9PC11TWZ/k9df5Kkzcmh9PC11TWZ/k9df5Km3cgjttaDRaDQajUaj0Wg0mtrCm5hSjUaj0Wg0Go1Go9FoqoUaN0qFEA2EEMuFEPud79HlnDfOec5+IcQ4j/1ThRDHhRCWapbzCiHEXiHEASHEk2UcDxRCLHAe3yCEaO1x7Cnn/r1CiMurU05fyCyEaCiE+E0IYRFCzKxmGacLIfYIIXYIIb4TQpS5yqgQ4ogQ4m8hxDYhxCYfXr/Sz9WHMrRw3u/dQoidQogHyjhnsBAiy/n9twkhniurLh/IovXRz2TW+ljiuNbHss/T+lgD8takLjqvp/VR62Nl5NT6WD1yan2saX2shRTCrwNPOrefBKaVcU4DVKrnBqh1pw4B0c5jFwBxgKUaZTQCB4G2uFNNdyl1zr+B95zbNwELnNtdnOcHolJVHwSMNXBfqyJzKDAAuBeYWc1yDgNMzu1pZT1/57EjQIy/3CMfyxEH9HFuhwP7ypBjMPBTDfxutD76n8xaH89wj3wsh9bHGnputaGPdUUXndfT+qj1sSZ/31ofTy+r1sca1sfacN8dCXzi3P4EuKaMcy4Hlksp06WUGcBy4AoAKeV6KWVSNcvYDzggpTwkpbQB851ye+L5PRYClwkhhHP/fCllgZTyMHDAWV91U2mZpZS5Usq1QH51CymlXCZVKm+A9agFj2uKqjxXnyGlTJJSbnFu56DSmTfz5TUqgNZHP5NZ62MJtD6eitbHGpK3JnURtD6C1sdKoPWxmtD6WPP6WBtGaWOXkjrfG5VxTjPguMfneGr2T8mb6xef4/zRZgENvSxbHVRF5triTmBJOccksEwIsVkIMcFH1/O7e+R0t+gNbCjjcH8hxHYhxBIhRNdqEkHrY/Xgd781L9D6qPXRG+qaPvrd78xLtD5qffQGrY81g9bHGtBHr5aEqShCiBVAkzIOTfa2ijL21WSaYG+uX945tXjkxwcAAAL7SURBVCV7VWT2rSCnef5Syh+c50wGioAvyqnmIillohCiEbBcCLFHSvl7VUUrY1+t3CMAIUQY8A3woJQyu9ThLUArKaVFCHEl8D3QoZLX0fp4+rLVgd/81rQ+eofWR6+pa/rob78zrY9eoPXRa7Q+VgGtj95RU/pYLUaplHJIeceEEMlCiDgpZZIQIg5IKeO0eJSPsovmwCqfCnl64oEWpa6fWM458UIIExCJWojZm7LVQVVk9imne/6gkgIAVwGXSSnLVCApZaLzPUUI8R3KlaGqSu4390gIYUYp+BdSym9LH/dUeinlz0KIWUKIGCllakWvpfVR6+Ppjmt91PpYQeqaPvrN7wy0PnqD1scKofWxCmh9PDM1qY+14b67CBjn3B4H/FDGOb8Aw4QQ0UJlOxvm3FdT/AV0EEK0EUIEoAKIF5U6x/N7XA/86vzBLgJuEiorVhvUaMFGP5e5xhBCXAE8AVwtpcwr55xQIUS4axv1/P/xweX94h45ff7nArullDPKOaeJKzZACNEPpatpvpTDidZH/5O5xtD6qPWxEtQ1ffSL35k3aH3U+lgJtD5WE1ofa0EfZTVnryr9Qvk7rwT2O98bOPf3BT70OO9OVND1AeAOj/2vo0YHHM73KdUk55WoLFMHUdP4AC+ifpwAQcDXTvk2Am09yk52ltsLDK/Be1sVmY+gRlgszvvapZpkPIDygd/mfLkyhzUFfnZut0VlGtsO7HR9l9q+Rz6UYQDKxWKHx324EpVR7l7nOfc5v/t2VID9hdX0PLQ+VtNL66PWx0rIovXR/+StEV10Xkvro9ZHrY9aH89afRTOCjUajUaj0Wg0Go1Go6lxasN9V6PRaDQajUaj0Wg0GkAbpRqNRqPRaDQajUajqUW0UarRaDQajUaj0Wg0mlpDG6UajUaj0Wg0Go1Go6k1tFGq0Wg0Go1Go9FoNJpaQxulGo1Go9FoNBqNRqOpNbRRqtFoNBqNRqPRaDSaWkMbpRqNRqPRaDQajUajqTX+H/YvB6CRI2vHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8mDNHCou//XblpSwaGEqpCKVUlPMzcB6wsfK96hZ/Djn057bViIwMWLECrcuJ9gEmq4fcXtmMDNFrUVGiBF3DOjBwoONDVhbEx6OLi0VBBgejc60UEgo7d4ox++CDbqO0Z0+Rs4AAceTm54vstmsno0QHD7rrDgcFHfXn5OVJ5zYpqQbXxAOrFTZvls+e+t6JxVK+UWpodOQCZXWfTzLTd+4eRDGB5BcHEhICFuUwFLOzRSi7dYNdu7Al7yK6eSg2exXCAA4eFL2WluYySomIgGefdWfsjI4WJ6wneXmlnE0uMjPlGK+95s1PPIKgIOjVq/T34mJISCgdDh++YQUkJZG1J9uV+yw8HLp0gVNOkdkE77wj0YuZmbByJSilS1u2XmCMUkNZjhpmdrS5T7//7v588yWphMx+T9wsDq6+GsjLR+dKx/P665fy/PMQsH0rnCGnK2vIgsj34cPyf3HxxUtdy20Eioay2WSCumP+y5BrW/LP3S+6psaAhCKcsvMjIoKLyCGS12eLcdqaD8nPMolVDMc2S53eoOru50zVFxlZeXhSUZF4iJ3hEy1awJYtotWcIYQxMe7jNQ6aA78qpdYBK4FvtdblTGrwH5yGYH0Yg43WCAXRgy+9JHKQlwfh4cx/+yDjbjvSKn3Y+jhL77/fvSAyUozS334jPyia5K3ukNp9p17Bj0NlvtvnXEbfyDdIKYgjL9dOeKiNtJRiiI/HbrNLWPw114jzp6BALL6ICHllZ4tRardLGydMgJYtWXrffUf9ac88I2WKQXavaUmJrCzpuzvLU5SdkRMXZ4zSY5gaZ6YvLoZkOhFMMZ/2mMjAgXBiZyv3D/xVZLOgQPRTairZe7JIaBvKXcPePvqBMzPFMH35ZZdRurRfP3c2aydl49zXroUxY2D8ePe6khJx4DrndB88CFqX/l+oBl99JV1lkL8irSUwwm6XEVTP6a7h5MEzz7Bj+gJXRH9UlPifhwxx/QipiLFuF5vW24gPs1I8e65XbXNijNJy8IVSbKiKtSphZkeb+3Tqqe7P4eSRs3w9944v4amn3MsDi/MpyRet9csvz8rCuDhYvpyFC7SnI9hZ6on4eEhbthmrFdb/6T6YLTBEFO2WLXD55S7h35cZQa6KOqJ9O4taExFYSCaxPHvgRgDSeRN7fgVz2Ox2yZFtMDRynn32We/2W75c6hzGxpY/ytm/P4wdK+FLP/wgMfgg7tknnxQvslKi0KOieDY2Fj79tPwJ6Q0MrfUOrXVvx6u7M6TQcAzyxx9w770s7T4OnZVNhi2a/LH/5vDyLe66aM7kKsBTsz8jSBWDxUJJRIz0Cnv3ZtNPB7n8pB3SiQXWNh/KkJXPcRzJfMFIFmcuptXTtzMv9xyiohXN9q3l8nHNGcN7NH3jGXY/8wEEB7N3dQo6KBgCAzkc0ZZ3skbKnNKsLAk/jIiAHTt49pdfyv89HgUNlRIVDPDdd0fWPqwuzqhFkM70nXdKAhaADz6Qv5qnny7tBDcYylLRIIozOhcgM8vC1VfDZWOieGHFaaKHQLwiv/9Ox/+NxxJk4ZtVZYzSkpLScaunnipynJMDv/0m4X0RETyblOROWOakrFEa4ujHvvEG9Oghy3JzJVmZc9+ePSEri2fnzy//xx7FEzRihBildrvMcAPo0EHCc8PCSmfV/Y3T+DGtN/f9NoKZM2VZVBQMHQodOzpOtz+V2FjodXEi6zcq2jQtIO/CUZW24WgYo9TgwpdhZj98kAJaEx5iYzudeCn5Ep58UpxPAEHZ6ZRoKTK6Y8diWRgno5bDBhfTuTO8GX4PNzODvXtldZMmkH5QvMP/rJ/jOldJYCg7kjW0bCkLHGnDIiz5pNniCCNPtNeSJaxeDXYshFkKySeMiDAJ17CynMLcCgT6vvvgkku8uQwGQ4Ni8eLFVd/4vvtEaRYVsXjnTnc5ifIynSQlwYwZIpuLF4un6f33Reb37oXnn5ce7X//C/PmSTu2bBHvscHQSBjx8RUAnL19Bvt2l9Bv9etoFJa/N/DD9Y7SL2+95ZKhxX9vJipAogc67F4iMlFQwOFDJcSrwyJDwIVf3gTATo6joGN3DpVIeNDv+mQCAqWb9+PSAH5rdimHi6NJTASKijgjfDUp+bEQEkJqWDsmNX1eZLSgQHqgAKGhLC6bftOBfdUatg6+g8svl754erqE9fkiPcOhQ9IU56hrQQEsekSM4+uug8jdm1ixwnfhwoYGRZUy00PFgyiqpJiz4qV7m5UlTpXffpN1PdKWoNMPUxQUAY8+yslt9nL+4EK27lha+uB5efDIIzIFpVMn8ZAMH+5eHxQE4eEszshwG5aTJsn76aeXPlZ+vmS8joqSkD4Q4zY5WZy3zogGm43FzvVlcY7g5OdXGLEUEFB6usCiRfK3ctJJpXdZycl8saEz+Tb3ZNuoKNmmfXt4tfdbBOoS1zWz2S20isohr1n78ttWRYxRavCkxmFmzmmd517XElJTCS6x8gbu4t9ZZ0vof2BqCsU2S+n5Z87A9fx8GDOGUZbPeJZHCbBosFqJ3LOZXC1hDBr3JJPAQOh4RV/o3JlU4hm1XBIehaf/w4MbryUCK2zYgPWcCxkzBnJsEYRbCsgjnDAlo6MWNAW5R2YYnDEDFr68xR0+UR327ZM/KZvN9ykJDYb6Zu5cmTezdavMcXMObZSdAFZYKDFCAF27ykSUrl1llMeZuQzcHqt773XvW9MYQIPBj/gquSeFt9xJACVkHComTBVQQCjBvbpy7vx/w969FBHE/9pOI0QVUlQEsYG5/B3ej73FLfj22tn87/f+pB2y05Q0PuPyI87RpUUOBTaZA1pEcKkO6M7UKB5r/S4Aj8/rT7PuzdmdGcMHe8+mIDKe0JZNSA1vT/5HX7j2mTix4t/zyruRdF02gw0bZIAnO1tEOicHrryy6tdl3z53su1HHpH3QYNE7RYWOmzkPZtIzo7HGcwx8/duQPnzTQ2Nnppnpg8KYkmqjEg60xqccIJUX9lED9ZOX0aruf9jBF8QaLEzqF8hFIqO+uEHR5mU/HzRfW+84U4mBtC7N9quZcA1PNwdHwti0TmZNUtKEw4a5I4wSk+X97w8EaS0NOkbOw3qkpKKvT6HD8u6L76Q+eMO0tJkSjlAgMVe6j9BKXH+KDTjevzMyJHudWmHbNw9YLnre0gI5OdrOneGO05YSm5+aROyaVAWeSHeJUJ20qCN0vqc91Jf1Obv9UWYWanySQcPinB4YF0hdUGDzj+XYpsF+5MTOeMMRxWM2Fh5z8+HJUuIyd1HAqmwfTtERtJ+xmN8Ff4vPnhuDwMYxVP/ESEOCbQREmQjPyiaPML5I60jLFhABFb+SO1IkCrhxU1DOeQx7SA9PwIrEYQpmajShbHljpRu3AhbOZ6dgZ2pUunArVvdbqiVK2HKFBkF8szYVB7r1jU4w/VYk72q0NCvyYsvvnj0jfLyZL5MbKzE/ixYwItTpohSLc95ExoqcnHppWJkdusmmnDUKOkFHDokIz4ZGbB6Ndx5p7sd1ShFYTD4M1pDj+jdpCeeRLxK551PwmiiMtl80X+IsYhz9vSLYgihiPH7HiRIF9Eq6CGCLDa675PpIxd+MIrxm8byrz2TaXHpKXzQ5WnaRBx2DcI8fn8+aR0H8PbpksmkmCDXgOeECZIZt3D09QA886yiWYJid2YM1/1yMzlnX0xUFIx/IJjPk9yZUCZMgBcrKEHRpPggl7Vf5fInZ6cXExEh0Y+OwCcOHXL7m8rDbpcprr/+Kt8nTXInOAoLg3yr6NPm639Ao3jsMVn3Hd6XxagLyuqChq4bvMFH16AD8DtwvFJqr1LqJnyYmX7PHplVAjBtGnTuDM9e/ie7aU86TfmKEbx90TyIiWFoqAyqnHsu/DJ8ElxxhTykjomaJQSwgOGwbBlJSTIYuis9iofC22O3g23I0NL9vBtugGuvheXLxaPjnPt2wgnSsP795XtGhliEjvIUL3rWLfUkPFz0s81WahrNY4OW8Pjj0LNbMV1z19CvvGKP33/PuC8G8/nn7kWphdFce+IGOQZPk5mpaZH+t/yGefPIsgay8v453ISENbdR+3lx9RFpaKpFgzZKDf6HpwNnz3p3FoKHB/wIQAEyWTQwPJgSu4XfNsbw6acpopG+/FI2Xr3aPb8GJAc1EDT/C3o+P4ZrTt9NHlm0ipHaMSnF8RQWB3DhXxP5J7AjYUElkJgoI6SA0nZSipqQTlMAnmQCB4ub8AUjicDKIPUbdg5SaD2yA2y3gwU7T+eMxxXGn5ws892c2GySNa2wUBI1paTIcmecxCuvVJz8Ze5ceOwxOPFEOPlkWbZsWWOr0Wjwc7SWQf0U57NbEdOmSYaEe+5xO18ef5wUm00MzrJZOp3btG0rmj8/X2R96lQZMb3nHvEA9+ghSvmkk4AqtMNgaGAoBdcmfM893w2jaVQxL++4mGX5/Zg6vxubDopu+nWtWJCT2kzHSgSh9mReumolrSwpROLWIYMsv9E0MYoth5pwpfVdmsru9D0tjNTMYMKPD6R7+xwOD76C6GhZ178/jBwJBYUytPj00yLKe7LFosyJbUt0tIhwWV9QikN/ffwxfPaZe/muHXZi4yysWiWZN9esDyIiQvrQTh/zyJESsV8eqamSydNigbTFf1GUlo3FIn1ygPXrIWWXIwFhaChtA1I4sac0rvm1YpTmL/q5vEMbGg87tdYttdZBWus2WuuZvsxM36YNLFzo/t69O1z2TF82WXq6ljWPtELTpuR0iAfgkrDvSNmWLX01jwQomcRyLR9ARARJSZLo+qFpTbkiYyIBAfDI3jtKG6Xt28v+CQlS8sWpP196SUoaOusjrlgBZ54pn3NySKkow31kpBijSpXqjIdsXU96OnRuW8jZYSu46SbHitdfl/fsbNHPYWF4jr4coAWtoyT7UWeSSCxJ5jN9uRjMVitZ1iDi4uB6ZnEzMxgU8Revra5ZtTNjlBp8imdyg8lvxqLQnH1aES2D5EH/LOhqACa+1QKUwpKRDkyD6dPdBcAvuqh0spTzzpP3wEDo2xdSU9nIIgKWLwPgXavECf10uA/jSl4mvDCTyx/pTCjini0MjiLXFk4OovCv4UPG8ia7aU+mLYrf9KnssnxJwYKfjvg9NpsYpXatXFnLSEqSqsNOd256uoRfPPywuIVzc2WZc0b5vn3ulGdlueIKXPFIzj+DM84QI9X1z9GwOBYiGBrb7ysshLPOgmnTplW+obOWKMhzfd55cMYZsp/zebfZJGPg22/DaafJtlpLT9U5pOJMle/EmVfewbRp08j7dY07Nm/GDDNqamjwdNA7mPNLC/7ObsOlfMnp6leO62DnsC2GS4aJvho74hCJU8cRGAgrMr/h1Ocu5No7omjdUTq/u5v14xH7M6jgIHbkNOOF8Cdc6jI+XkL1pv31Fxt2RpFWGM1VV8m6Hj3E51NUJD6ili0lL+DsJKmZlp0tc+ref198wp5+0WnLJYQvKUnCcj/+WJY/seRswtf+xjTuR2uR1ZhoTW6u9LVfflmOGRbm8i2X4s8/pR8dFgZXTenDz18edvWlX31V/jKW/mJhAz1YVtgfW0AQqXsLKSaQXqMkfHfS7A4+ujsGg5QY7dQJXre7E445p5Es3/Mp6al2Tsz/XQZYevaUeHVHhq8L+YYocli73sKWLVI+pVgFAaJXM6PbycFBvDYhIe4pL6mpMpDx4YcyXOuMTrjDYchecIFsl53tksdSvPOOu054QQGehVdDKWD5cmgeK9MFXNnB7r5b+trO5GohIbBkCUsRAzglsjMJG2RAaVjCXzy4/l8EJG1xhSBn5AUTG5zHoBUv8BZjCc2redU6vzFKa9qRbWydxLI0lN/nyDEEwGvLeqEvvJjw2GBaWmQe+pPFj5Xa3ppeQHAQMvJ41lmlD3bCCTIy062bY2OrdF4PHgQUlmzpxC665kMGsYy2wQfYQC/CS7L4ZmEgnW4/j0ub/Ex8ZAG5RcHkPCYp8xPXz+fhaU35nMtZY5Vj6/AICn9d6RrZ0Vr611pLrL3drrC84zCanR3l/HxJA2izicHsrPCdk8P6FueRkx/o9lZZLJWH5z72mIRxDB3KEs5iV1Er+ZPJps3AAAAgAElEQVQxGOoAq9Wd18SFZ9jDrFnyHhwsyvOWW6Rn+/zzcOGFsm7AACknMWqUDI3ccotkG927VwzO0aNLFQAvRXw8rt4zknji6ml93XNKn3hCHD4GQwOmU/Fmeh1fwPgWn7KNLiy6fjZBwRauuSeBoEjHiEuTJnTvobjyonyK7QGExoSQWRxJh84yitLuiesZ3nQVhUWKxx8D8vJcpRzi4919XqUkKOGssyR6PiYGLrtMjM0JE0QlRURAq06S4vbbbyUaHySH2bvvHqmytJbpc//6F66Y3GiyicZdSyLhh48pOJSN3S6BEMOHS5nUk/sU4spaCGC18vHH0DtmFxH5aQBsWe8uy3bHi514f0YhhzOgB5vYaO3AzA7P8ldGIoHYZHQJ2GVvV7ObYjCUISAAAvFwgj73nOtjt8Q8igmCQaeLZyckBDp3Rlvz+IOBRHZuRZ8+4tiJi4PgYPekZ93bIyJu8mTZICREdONZZ+EKa3DWEgbxzpx9tpyrqKjiSdR33CFCbbW6a6Qi3WXnAE2TsHxKsvNkwOPNN0WHb98Os2fLMdLTYdQozuQXvvwSrIVBqO+/YyPdSTj9eE5mJfTu7TplVl4QMYd3QufOKKA4K8+r6+2J3xilhsZBeGjpCdiFljDy8yHWls6c4Gtcyy+7DKYtP5UfDvbknusdndrmzd07xsXBkiVw//0u4SI4WLxLBw/SgUFYcrN55uZdtLxjBJnEujRoeEAh8fHQpk8zvoy9kROPy+HDfWeTa4lmVJvfCGgaS/v7LieGTFfC3qzcG8SD5CiG9vXXcNdd4u0tDIggKMRC8TKHd8kZkmi1wpw54mIOCXHHK+XkcK9tKr9/n+MupPb9927juhx0ahqjPr8Svv+ed9VN/Fw4kIYyw7Qyh0lDcaZUhcocZw39d+YeyCWiOIPrrrvOvbBNG8kAMX26zH2x2+UZnzlTRvjT0sRTfPfdst9998mcmObNJY393LkyybxlSxkSad/e3estS3S0KGkHQ4ZcJ6IYHy+KskmT0iH9BkMDpE/+ctb9nMWjp/zE33Qn/J3pbNoE9z8USOvWYvBde32giNUdxcRwCcEhikOH4OKL4d//Rmqj7NlDUREEhyhYt44+feT4XbvCe+/hkuNx46QP2aWLuw1O/+gFF4gv9J57ZPkHH0iAjpPiYjE+Q0LgOo+O6GWXybt9u2TkncBT3MzbDGURa0Y+QxvLPvIO5cjUF4umWfIK/vwTuqktbF0o+yQnw9sJj5CYCFEp2whP3Q3Av6dLQ88/cT8kJzNqYndSUxV89RVjAj+iXckOmiEGLE2bsj50ABMGfue7G2QwOAhsL4bhW72nu7Jc9w7ujwU7h2guQhQSIoYikGuTEZnQCDGrgoLEfpTgIJFHrR0BgcnJ6LG3ytSV7Gx0bBy2H5ZI9JDW0tddtszdmPnzxWscHw9r1nBdL/ecb/73P4lzb9tWdKTVKg6jsDAK8jXPj1nPRCYAEPneqyhbsWTpffRR6eA6a8Mo5e5rIwUniouBq66iO3/LoElcHPz4o2sbhSZo0kRXBFTvPd9wXsftNbruxig1+JTwue+X+m61i1EaUphNk/ulJmiXpmnMnAl5xcGszT+e4c5Z15GR8MknEqKbn+/yhJYK5XWMlJ5NKC2t23l0XCYJHSI4RALZdsn2EE0OTZs65Cs1lfB4CRPckxnJiwmTITYWpSAAG61byyDQ7P/24Sbe4eAuMUqLi2HFgnSsVsg7fSihHVtTEOgYSnJW8XamAt+0SYTbGZqYm0uQxY7t7XdkpAjEwN66VXLZg3TawVUsK+fHlXyb5Og5aDv2iChOtKyX0acGlgDJ0PDI/ecwEflpDPdMZ5+SIhEMGyTRAQ88ILJ40kni7fXIiFBqv7g4UY6XXSaZdC0WXNW3q8gppwwXhXj88fDNNxKCYYxSQ0Nn4ECIj6fZF2+y7WHJghsgldF4+WWJohs0SL5HNQvFos9FKRlIGTFCKiYBEBbGv/7lMBB79WLFCne0H7jlcfr0I+uFBgZKJHxCgoyqOmuBBgWJmC5eLMEPS5eKOo6IgHOP6yKd5JISV781ddUunuNhgs47BwuaRQyn7xePk3bAxhvzWhMWYiPYUkJL63bWrIGOEQfpOvYM5syBfdvzmZV/JWrlHxAQQHhxFhHB7lHSeWvl/yJ4TzL7bQlw9tlMy78LyxUOi3j+fGjdmp4759W8IKrBUA65BUGcfz6owkJXqZVO3S9hZNQPjP9PKBQVkXHZzTyZez8lh7Nds09KdABxcdJt27JFonFB5FFrCSCyJx4nM7qmT4egIBYkH8999+GaC6o1ZHQfVLpBkZESmhsZyfDOnd3Lf/pJDMXt22UQ5OuvJS8LkNnzdA7+6K7qWESwjJouWeLO8rt/vxQWvvHGUqdTSuxQPvkEzjlH/iAyMkS/OzKa7qSDeMscf2LN/z2KU9rspSb4pVFqspZVn/KuUX1ct/C8NNfnrmG7KCAEux0CC3IZfIGEJ3Vtkuqy3/bq1tz7/H3uAyQkyKiJZ7q+IreyIjQUMjL4Kf4rhli/hrg44uMhPraEkyM2ARBsy+ekk5AMY1u20KO3hRBVyPa9YUT++bMrxthKBE2ayBTWyW/dCcCK5XbIzydA2flzV1NmzIDHlw7mwOFg8iMc2vgvqQHHr79KKOO2bXJM54T3nByCA218wmj57tlb+OADeOghd1pwR5rvg0FtaEEKJQSgUVibJbLe3hPrjoPumlUGQy2ReyCXyKAiRo92PLPOsNnXX5cRUYAXX5Tnd/VqkdFTTnHt79oPpNdbQ0fKM8+MFn9NQgJcf72E7hqj1NDQmTfP1YHr/NwNlW4a3SyEfdwPSGCCswShk169So+ALvIo3lZKHsvgNEqdODP3Dh4sU8EHDxabb5OoU5o1g15vLJP43uxsKC7mhVPn0uLG84m8Y4x0aJ1ZQ5s3p1VEFgpN+LZ1XFoyl94JKSQnwzt7JTfEjTdqMpIPExSo0YsWwW+/El1wiIev3EFf1rB51JMEXDDcNR0m9rQe8n8THy9z2D/+WKzmkBC5KKYmjKEWyMmRyIHWV5wqwgZ8vvVZEg6up0e7bCJUHk2uv5jntl/B5N/PdEUclJQo2rYV+/Haa52z0kQeHWMQZGc7xjDat6fwr01c+P6Vkh9TKdCaXbvcIgWOKauRkehsSTg22jNFbliYGKRBQbLT/PlkLviNWz86gweTx/KPTUZ8+0Zt40be4Q5ec+/rjO7r3Vv+CJwDLg7ed44xNWsmc2pAnMz33gtbt9Jm4dtiBINEDT7/vBdXujR+aZQaGi5hHVvyNRfzD225NXYOgaFBMi8zz+qKlw+xuEuvZKpYVEmxJFCB8jN4OrMqgAhtYaEc6+BBSEggMBA2Hm7NdyvjKNl7gOzjTiQjwzE406oV995Vwvy4Mbz9RRMiLAUuJdafVTRtKn8OKkCWffp1KAWjb8Ay7ysAruYjAJbu70xBRFMxkJ/1qJRjtcKqVY5CTw7lmJNDcICdDxwhGy6hBUliNGUKAJsmfg4pKcxo8TgHssOJibXQMXA39OlDdp78e33R9BYuO9t0xg01ZN26UmE3Zck9kEtEiEdJpKwseaa3bhWnyKuvyvJSNZ9qD7vdkQcpIUE6nmPHukPhDT7DOHz9l+gYhY1Anx/XmY/MidMo1VrUqlON3XmniF9kJJTYLaJvN26EESOIWy7PTGRrh3d5yRKZ1pKUxNiQ97CfPZjww3t5qOX7XNhlG4U5hXSK2M+YiM+wWhVJn6wGFFFhNhSa4IJsHh2XyWo1gK6dbRKn3LKlDNs6a8UkJ8vcujIGtwkkMtQG06bBM8/A8ImnuKeWBQSiURAYSEKIGGnFOojH11xK8+YyO6WkRN4zMyVE/ptvxJ8yY4botPDw0jn/UmnmOmfeE5P5IuAK0tPFEP3j+WXcfbejMkxUFF1Sfz2yoS1aSPjuypXw0UdQXMyMoHG8tf0cPuA69ulWzORG1tz4Gm3mvEQrUtzRBdddJ/XCmzeX9zlzZHnZyKYXXpAw399+cy/r0sVdSwfEc+YDjFFq8C1du3Fx85W0ZS8BBVYCw4Np1Qpi8g+4jNL+Cbtdm9/Z7FN67NyJa3LnqadKSINn1pWePSlFYSE9OnRwp7DGoUiPP56A1i349u8OfPWVx/YJCUTfKoosIN+dSntZ//tISBDHdc8eUkT5kwUx/JHSjjcWSvKEV4PvBaBPdDLFMfHs+2ip7HyG1GKakzNcarE6MzxNngzp6XSP+ofHeFqWeYQXZYcmsK3/v8ghkmkTcighgFsPPMnk1JuIslj5p6Q1wRHBpP0l4b0bLL35IvX0ql17g6Eili93K5xyyEzJJzpjNz2aNZPMfFlZ7nTxFoskUUhOFg9pOfRwyI+LpKQaNbdVqx6EhcHqPc2xp6bD+PHw+OPuFPmGY45jzYCOiABNz6NvWA5HyKMHt9wiJYOdOFXX2WeX3q59e4nUf/ppaBPTToZ3lvwE337rKrcWcZyjsx4cLP8TUVEyxNq3L0Ojf6dLzhrCYoLJKw5i231vccgqscJJvx4gRbegS9g/FBNEYKtm0LYtastmOaEz4/6QIe4GOZPAlMHfB0qPtee2sXDHHUc+WwkJPcRgCwykfVjpxHuvvSYBfiUlIjseyeS54IIe3HyzLOvYUSISnMlxU3+U6TE7d8Ljb7TmsmvDXcFJPxYO4r//lVPm2CPYrjvx4YfQIyHBHc0UHS0jpc2ayZ9Gejpf9XuacIeM7iKRiP7dZejWaTg6Y/ZvuUWSEwL06SOGZ2rqkamyW7cWK9pz+LaWaFBGqTfC7ZmcpD7/GKqSJKUmv89fiDm7r9RXAqJ6JtL2uCC++AK6FfwFUVHMnrSTB54W5ZIYlcYj3ecxC9yeqKgo8cB41EpyLXeSl8eshx+WUKhyCA93T90EICiIno9dQteulJ5/8scf3H23fHzvvVmuxUWRTfguTea5RtrEG5bdsS/vbT+NNjeeBxs28P4NS2DyZEYVOuIbQkMptllk3t3OneiISAKQhEgFhQqFJrdjb1YV9WbEzheIJodtlm4MYxFBqoQFRUOICciheUw+wfYCliA9hLCuJrOgoeYk/xPEV/uPrB/mrLF9+Stn0nJob1678npRPJdeKu7d664TZQZw3HEVHn+WMzuvE2cKUC8ZM2YWYWFw4WUh3NR8vgzX7Nt3ZAxjdTBDKoYGxyzv9iorjx706uXuh4KI+YED0lH2ZPBgidgfPhxuPPnBUklQQpB6MQFB5XQhmzSBc84hMX0NYSF2AjetQ2NBNYsnlWa8yh28xa2k2BPoEpvK/sD2tLpjhHR8PeORDY0Gf+unesv5588iaOyNcM01nP3ZHQQESABSXJw89rm54ptJSChdmt4pj5mZUlTis89km19+gQkvxwFiV774omzvLOTgzKHZujXsS7FwVbe1km8o9HUR5IULJYngoUMuo9SelcOmvy1cF/QJACUEwemD3NNwQJISgljdTz1V+kfGx1dcwrAOaFBGqcH/ufdeRIo2bWLM0ht44Ikw8Tbl5EBUFKMe6uAqApx8xcPQvDlnxsSUqqkEHPndsxxEcjJnPvBAhW0IDnZXsHASHu4uK+pCKRIT5eOZgwe7Fn968CzX58AWoognTIDdxa0AePSDroy5wQIPPghAGlK5vOWL94sw//ADRLhrMDoHnLZnxkOzZvydJgb4Dnt7fmQIRTqYBx+UsOa2TfP585+mrKUPf5x6D4R61NgxGLxkTVI0I765CatV8hsk/ZVL8syldOlk57LL4PYTfqb3+a0Z/PgjssPGjWIAvvaa1N89Cmc6C3v7iEmTziQsTBy6s/YPpcSm5D/EOXGnumRluWumGgwNhAEDvJOr6sijxVI68b2T8HDJ5gswa9UdZNsiiJz0GEycSD6il4qLj9wPkKGi3buld/3xxyx4ahXExdGEwwzoIfPWnpwcxnFP38DuTudUNw+awVAvzJx5pqQDCQ6G6GjGjZPEZBkZ7sS3H34oPtlAj8h7pzxmZkq94EGDpN7vwoXumWunnCL950GD3CXqp0yRai3x8ZKHaOCtkh779dX3i5E5apQkIhw4UNoUFkZKSCJTpsCrP51AN/7mhgGbKIyMF6cuiGA/+qh7Oo+fhRr4fsKCwQBwwgmUetSdtTw9sLRpBaGhWL/88ujHcyYRAoiJweqZCKkMKSnVlzOrR4bfmZsGulckJqL2y9yAoEA7D4X9j19X3Y3FAnv2ykmakYYOvJT0PIch2r49hLeROI2I7dx7rzR/e4dzCTzhZNehwxOi4BAEWmxMnRrABS1CSCsMZldqE8acu4+TclbxTagjXFJrv/vzaMh4em13Tb6gHltSNxy0ysSxPXtg7Z925j61gxu3TWUHZ7FjF/xvmJWW7YLIynGEx77wgmR5CAiA22476vE95ccXFBRYCQ+HxETYsUMU8zXXRFZdBvLzReic2x84cPQ6pz//7HKYGfwXp+weC3Jrs3knV76Wx2J7AcmH4+jUNRAufZzjW/3J1T+VKllYmq5dpVRFcDC0a8fwGy2QFc7CR2ZjefZdlv4MZ54ZCFzOXWG4Stp4RQOLgGhsuqesPDaGEdGKyM+3lgrWcWbDfvRRMTLffVcKJnTq5HbogFse09JkgHPDBomGXbnS7dh57z0xPtetE9V74ID4hVu2lGPfdZeop4sugh8W5kqscG6uxP02lYERlOKfr/+ifQlYBp3KegLRv5YQkH4I2jn0uHPE9Jxzau061aS72iCN0soe+qoqrPoO5YWatbFRCP5TT0kvecKE6u23ciWcXvE8y7KDrFXlu+8kUe7gNlvptXcBGRePgf7DyHBc6uISC08tO4fHP5OEu+3aSb+3oAAOBcic2FdfhcBmj0Hz46BZPN/2/5CLsuXP69NP/8OECZrro7+g+KKRrF0bwSf/hdGjAyT5RIeOTL7BzlVjYdb3reHEXFq1cihcR90pg6FaLFkCCxfyd+oVTDzrJ7p1O4f3Y8bzedYrlITfTVxhFh3ic0mILaJ1aygu1NCqlWhUZ62KKlBQIMZjJRG+1cJud/VnSU2VwdoOHeCIsc4pU8Rb3K6dCO8330jM07nnSqaW6Gj5vGqVezjo4EHJoD1rlvQQ/vhDkqmddZb8iIpqqTpJThaHUwOmUegPQ52SdLgpJzii8vvc1JePbqpk44CA0lnn27SBNm2wPCt5Fjx9PyNG1LBhxllrqCPsdndiME+eeUbeX3pJ3ps0kVdZMjJkpPS008QAnT9f1NWIEe7o+N693dG4bdqIUXryyZKEvn17sSWXfKclMdGrr7oz6DrYnRbhTBZM4D87IQhokeAefq3lfmSQxUZxsfeVmhqkUWpogKSklL88IYEVntl1q0JYGCucMQ8+YsWKFa6B3AssC9h9xSjendMEeIwYj+1U716kvS7lZvbtk854bKym+RcSo3vnnXDRRefSJ176vU89JV6uDh0kR0tRkWLyrktp3lL+cE49VaISs7LgtttiCW8Bc+c6TjZnDs3XirFry8oloAEbpWU7wZU5ZPxlJKRRdNzXrOGHrW15Y01/0q++iyc4h83hfSELticO4ZZ/XmfFwR5Et42hdWu4O/ha+OW2KluXO3fKsz1gwAoWLZLkEL7g5ptXYLGI7RcfLwmA//oLTisuJndnKqk0o8OOH6W80kMPSWKlFStKR1RMmSITdZzpRkeMEIfWySeLsdq7t5S3yc+XZGqtW8vv3r5dTlpUJCGI/fqJ67ekRKI9OnVqcKMzhoaJt3rO1/rx5pNnkJyVxkV+6otp6IFE/qLzDJWTmLiiXKP0aDjl8c8/RbX8+98yQrphg+i1Sy4pvb2jUiCHD7vTKAQEiP0ZFwdnJH7KvIOraDOqHX3LOI9vuMFj+mg9xMWHBNooLPTeKDVzSg11Q0UJSkJCeMAzzXQVeaCSOaXe4Hm8yNyD5ES1qnDbnBzJAzNokPRR4+Pd2nDDBvF+FRe7owVtku/IZZdHxYjYvf++GKaRkdIfHjlSBqguc9QHp0sXBp4exPBee8k75M4abDBUFXvaYc6ddxcvnP4VTd57ialnL+TPXEkm8uNPFqb0/oQX+n5M/95FNG8OUzkkVmYVenhWq9hyNhusWfMARUXiMXY+797y/POwaNEDPPkkPPaYLEtJkdfWwO680+81njrzRynM2KKFaOo1a2SO99atsGyZpBL95x8YOJCcSdNZG3U6ed1OIvvkIdCyJf8960s2nDNeXNbp6WC1kvHJIrj9dikGHhsLQ4bwdf+nsZ86SDJsBwfD8cfX7MfVM40l4QhUnjywseCtnvO1fly86SVyg5v4ZbBOcKCdorySo2/YAGjsz3NDZ8+eB1yJa6uDUx6bNXOr1qAgCfApa5B6kuJRveXAAdl35EhIy5/Id9s68NtpD5ba/s03ZaDEmzb6ipCAEgoLvHfaHnMjpXUt8I0+BNcHLFu2rE72qcrxFr64mQP3p5YqLl6WK64Qb9eyZfD99/J64QWYOVNCM0A8XAMGyODNZ5/Jsuuvh7ffdkdPlA3pv6GcWuotW8LFA1OxpgYTdeTqBos/e4Ybk1z+a+NDAJwYlQyBgZz/ynAe7AH7NmXSvHks/Por/TIyxDMSAMWRm6qUea+oCK66SiJiO3eG/PxlZGSInXjGGa6KSZWSny8jHOHunGDMmweTJoHdXlq+U1KkbtzK06/ixMMvEFawj7v+24lXHryJb5bFcOGaNVKOyWKRDJ4//gjp6eSPup4/mp7P1fmjsL0Sya1h4Yxs/jd3zzuHV7vuouegw5y28Q3mTthI6zN7cMXlr8Lfczg9eCV3Fi9kbseH6PH7NTx9eQ730pOviu5mOK/Tv1p3oX5pTM9zRfjz/0lN8FbP+Vo/7rFuJC+qnGxIfkBIqKIop5CQiGOuO2uoY6Kilrmmb1YHb+XRswqS0ziNioK92WvpGBzMvn3iBHYOlu7fX7oUTX0QEqwpzLPhrXlppNhQCqXUMOC/QADwttZ6cj03qU4Zdk83Pgx5kZJyBm8XLJB3zxpv550niUp79JDsaSAT1bduFeN1yRK3Z+y00yQCsLphRuExQeSlm/qMxyI1kUetoSQsmvXrND2OvwuQkfi9e6FVa495KHFxR+ynFIwdC1OnyqChzSYRsEqJwfn885Jk2plvrEULiXrt3FnS3G/aJIOOlTF1qpzrjDNEqU6aJLmGCgqOzAa6aJE4ejZtsjDs9iv429qeMdcrureB23+B6VELSXnCwjnnSHTt9OmKa66J58w/5zOkiSInEApyQN19K/1fFit43NREMp65heV/whPz+6M1zPlMcXLfEcz5cxSnf/w8KfcrXj1jDcXvLaS3fT29QvKZGTCU3fn5fjvH+1gwQiuito3TY1U/2sMiCevtn+VagkMUhTlFRLWox+EhL2nIslq27fXxW+paHlu0kARF9U1ctI0f9p9A6hTJV5KX5+5X1nf7QkKgMLeYWjVKj3bjlVIhwPvASUA6MEprvcurFhnqDaVUAPAqcC6wF1illJqntf67Ns/72muv1ck+VT1e+54xnFROct/hw8vfd+RIuPBCd4m1Xr1wTTRft670ttXIHeMiIi4Ya7rb/bVo0SLGjx8P0EMp9VBjlcfGOvpRVWoqj0o5R+kVIG7WgAAJFa+I0aNf4/BhmDgRZsyQkfp335WyZlOmSG21Xbskh8m+feKV3boVYmNfo0sXCWePjZWEDKNHi3c3J0eMzOXLoW9fuPpqeOMNceZERUlp37175VyTJ0v97oyM0vLtTIwiSVES6ZoLJ/WT+dpXDNjNJ3tO45oLpbZily6SiCwrC/LzFYMGSRbf00+H228P58orJQnFffeJQbx+vczxadMGvvoK7r8/mMmT4cQ+ipEjoV2vWCbtHsJN2+Cee8Lo2d0msfnt21fnXvqk81RZx+9YlZPyqE4HuarXrT70o7d6ztf68YknXmPAkWWO/YKQMAuF2YWu78eKfjzWOZblceacDxh4ShStW4u+XL1aBjw2bfLpabwiJNRplHrntD2qUVrFG38TkKG17qSUugqYAozyqkU+ojF5oOqQAcB2rfUOAKXUbOASoFaN0s2bNzOgmhrPm32qerzTT680ue8ROGudlocvki80ax3Money6Hk72Gw2xo0bx+LFi+nYseMmYHRDkEeDV9S5PIaEbOaeewYQFyfG2gcfSEjtwIEyVzo4WGyxlSvdGQaPOw7ee28zp5wi8rNqlYTkTpggGa1tNjEAH3xQRkE/+kjmvlxzjXie4+MloVF6usyFCQ2V41Um35GR0L07/Oc/cPll7cjLV8TFSYj8Dz9I5MLLL0P//pJC35m36eKLSx9nyxYJkRo9WpIZhoTATz9JaPGdd3rOzWnGOW1g/HjYv7SAr59N4pK3qmaU+qLzVBWdUFt6oy6cQw1EX9e5PHqr53ytH8PCNtO3r39apW07h/LNS0nc+mELox89KE9uG5mj95iVxy1bt/Dhhydz9dXiHO7VSzL7jhvns1N4TVirJvz21ia6nXyKV/tXZaS0Kjf+EuBJx+e5wHSllNLapChsYLQG9nh83wucXHYjpdRYYCxAu3btanzSV155hTFjxtT6PnV5PF8y6F/tiQ0vAmDlypV06tSJ46SXrQEjj9WggXR+ndS5PH7//SusXu2Wg6lT5b1sWeCyjhhP+enXT5b95z/w4ouSEGn3blGaNlvFWfk85+pUVR6vuw5AEe4wHgMCJGoBxCg9Gs45O2PHupcpVXqeqyc33giFozuxdUG1vE314uwz+Jw6l0dv9dKxpB/Pe/gkti7aCTQ+/ViVrPW+MDQrOkZ5+tKPDNpjWh6derpNG4k6alVxbs46Zdhj/dj5y56jb1gBVcm+W96NLxsA5tpGa10CZAFeTAc21DPl9bSO+KPWWr+lte6nte7XzJm72lBrWAIUvS6X2OB9+/bRtnSabyOPjZcGLfD/HXwAAAgLSURBVI+tWomRGB0tWXoDArxPE+9PhIRZ6HVZ5+rsUhUdavB/GrQ8NlYCggM44WIpoGr04zGFkUcH/mKQAoREBtH1fO8LlldlpLQqN75KD4enxwLIVUptLbNJPJCGn6CmVGkzv2pzeZT5HfFARXFnewHPf/Q2wP7Kjr1mzZo0pdTumrQPiFdKVfsaKt8WJvOqDbXA0Z6nOCB65syZu3HfxwYrj1WRsSrKoZNqt7max68xfi6PXsvBMSqPnlR0HxuMPFaGl3Ji9KORR19yTOjHimStMhms4j7xQJo3x6/ONjXByGPttsPH+EI/lqIqRmlVbrxzm71KqUAgBjhc9kBa67eAtyo6kVJqtda6XxXa5Dc0tDY72ptYwepVQGelVAdgH3AVcHVlx9Na19j15A/X0B/aUJV2KKVOAZ7UWg91fH8YI48uGlqb/U0e/eX6NbJ2VKnzZOSx/jHy2LDbYfRj5TS0Nht5PPbaUZXwXdeNV0oFIzd+Xplt5gHOgOnLgZ/8MT7fUDmOUJY7ge+AzcAcrbUf5PMyeGDk8RjByGOjoSoya/BzjDw2CIx+PEYw8tg4OepIqda6RCnlvPEBwDta601KqYnAaq31PGAm8IFSajvicbqqNhttqD201guABfXdDkP5GHk8tjDy2PCpSGbruVkGLzDy6N8Y/XhsYeSx8VGlOqXl3Xit9RMenwuAK3zQngpDJfyYhtZmf2yvP7TJH9oAVWiHkcdKaWht9rf2+kt7GlU7fNR58pdrUh0aWpv9rb3+0p4G0w6jHyulobXZ39rrL+1ptO1QJmrBYDAYDAaDwWAwGAz1RVXmlBoMBoPBYDAYDAaDwVAr1LlRqpRqopRarJRKcrzHVbDdGMc2SUqpMR7Ln1VK7VFK5dZyO4cppbYqpbYrpR4qZ32IUupTx/o/lFKJHusedizfqpQaWpvt9EWblVJNlVJLlFK5SqnptdzGaUqpLUqp9UqpL5VSsRVst0sptUEptVYptdqH5/f6vvqwDW0d13uzUmqTUmp8OducpZTKcvz+tUqpJ8o7lg/aYuTRz9ps5LHUeiOP5W9n5LEO2luXsug4n5FHI4/etNPIY+2008hjXcuj1rpOX8BU4CHH54eAKeVs0wTY4XiPc3yOc6wbCLQEcmuxjQFAMnAcEAysA04os80dwBuOz1cBnzo+n+DYPgTo4DhOQB1c15q0OQIYBNwGTK/ldp4HBDo+Tynv/jvW7QLi/eUa+bgdLYG+js9RwLZy2nEW8E0dPDdGHv2vzUYej3KNfNwOI491dN/qQx4biiw6zmfk0chjXT7fRh4rb6uRxzqWx/oI370EeM/x+T3g0nK2GQos1lof1lpnAIuBYQBa6xVa65RabuMAYLvWeofWugiY7Wi3J56/Yy4wWCmlHMtna60LtdY7ge2O49U2XrdZa23VWv8KFNR2I7XW32tJ5Q2wAqnZV1fU5L76DK11itb6T8fnHCSdeWtfnqMaGHn0szYbeSyFkccjMfJYR+2tS1kEI49g5NELjDzWEkYe614e68Mobe4UUsd7QjnbtAb2eHzfS93+KVXl/K5tHA9tFtC0ivvWBjVpc31xI7CwgnUa+F4ptUYpNdZH5/O7a+QIt+gD/FHO6lOUUuuUUguVUt1rqQlGHmsHv3vWqoCRRyOPVaGhyaPfPWdVxMijkceqYOSxbjDyWAfyWKWSMNVFKfUD0KKcVY9W9RDlLKvLNMFVOX9F29RX22vSZt82pJL7r7X+2rHNo0AJ8FEFhzlNa71fKZUALFZKbdFa/1LTppWzrF6uEYBSKhL4HLhba51dZvWfQHutda5S6nzgK6Czl+cx8lj5vrWB3zxrRh6rhpHHKtPQ5NHfnjMjj1XAyGOVMfJYA4w8Vo26ksdaMUq11kMqWqeUOqiUaqm1TlFKtQQOlbPZXiRG2UkbYKlPG1k5e4G2Zc6/v4Jt9iqlAoEYpBBzVfatDWrSZp9S2f0HSQoAXAgM1lqXK0Ba6/2O90NKqS+RUIaaCrnfXCOlVBAi4B9prb8ou95T6LXWC5RSryml4rXWadU9l5FHI4+VrTfyaOSxmjQ0efSb5wyMPFYFI4/VwshjDTDyeHTqUh7rI3x3HjDG8XkM8HU523wHnKeUilOS7ew8x7K6YhXQWSnVQSkVjEwgnldmG8/fcTnwk+OBnQdcpSQrVgfEW7DSz9tcZyilhgH/AS7WWudVsE2EUirK+Rm5/xt9cHq/uEaOmP+ZwGat9YsVbNPCOTdAKTUAkdV0X7bDgZFH/2tznWHk0cijFzQ0efSL56wqGHk08ugFRh5rCSOP9SCPupazV5V9IfHOPwJJjvcmjuX9gLc9trsRmXS9HbjBY/lUxDtgd7w/WUvtPB/JMpWMDOMDTEQeToBQ4DNH+1YCx3ns+6hjv63A8Dq8tjVp8y7Ew5LruK4n1FIbtyMx8GsdL2fmsFbAAsfn45BMY+uATc7fUt/XyIdtGISEWKz3uA7nIxnlbnNsc6fjt69DJtifWkv3w8hjLb2MPBp59KItRh79r711IouOcxl5NPJo5NHI4zErj8pxQIPBYDAYDAaDwWAwGOqc+gjfNRgMBoPBYDAYDAaDATBGqcFgMBgMBoPBYDAY6hFjlBoMBoPBYDAYDAaDod4wRqnBYDAYDAaDwWAwGOoNY5QaDAaDwWAwGAwGg6HeMEapwWAwGAwGg8FgMBjqDWOUGgwGg8FgMBgMBoOh3jBGqcFgMBgMBoPBYDAY6o3/AycwAhwdzWlNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x54 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for psth_chunks in  split_list(lh_PTupper_units_psth, 4):\n",
    "    fig, axes = plt.subplots(1, 8, figsize=(16, 0.75))\n",
    "    for idx, isi_psth_dict in enumerate(psth_chunks):\n",
    "        plot_psth_isi(isi_psth_dict, axes[idx*2:idx*2+2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended Data Fig5b - PT<sub>lower</sub> - Left ALM\n",
    "\n",
    "Calling the `plot_psth_isi()` function for only Left ALM PT<sub>lower</sub> neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for psth_chunks in  split_list(lh_PTlower_units_psth, 4):\n",
    "    fig, axes = plt.subplots(1, 8, figsize=(16, 0.5))\n",
    "    for idx, isi_psth_dict in enumerate(psth_chunks):\n",
    "        plot_psth_isi(isi_psth_dict, axes[idx*2:idx*2+2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended Data Fig5b - PT<sub>lower</sub> - Right ALM\n",
    "Calling the `plot_psth_isi()` function for only Right ALM PT<sub>lower</sub> neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x36 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for psth_chunks in  split_list(rh_PTlower_units_psth, 4):\n",
    "    fig, axes = plt.subplots(1, 8, figsize=(16, 0.5))\n",
    "    for idx, isi_psth_dict in enumerate(psth_chunks):\n",
    "        plot_psth_isi(isi_psth_dict, axes[idx*2:idx*2+2])"
   ]
  },
  {
   "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}