notebooks/Inagaki-2018-2019-examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inagaki et al., 2018 & 2019 - A DataJoint example\n",
    "This notebook presents data and results associated with the following papers:\n",
    ">Hidehiko K. Inagaki, Lorenzo Fontolan, Sandro Romani & Karel Svoboda. \"Discrete attractor dynamics underlies persistent activity in the frontal cortex\" (2019) Nature (https://doi.org/10.1038/s41586-019-0919-7)\n",
    "\n",
    "\n",
    ">Hidehiko K. Inagaki, Miho Inagaki, Sandro Romani and Karel Svoboda. \"Low-Dimensional and Monotonic Preparatory Activity in Mouse Anterior Lateral Motor Cortex\" (2018) Jneurosci (https://doi.org/10.1523/JNEUROSCI.3152-17.2018)\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",
    "The dataset comprises of whole-cell recordings, 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). The data also includes detailed information regarding the optogenetic perturbations on a per-trial basis. \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. The code used for performing NWB export can be found [here](../scripts/datajoint_to_nwb.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting root@127.0.0.1:3306\n"
     ]
    }
   ],
   "source": [
    "import datajoint as dj\n",
    "from pipeline import (reference, subject, acquisition, stimulation, analysis,\n",
    "                      intracellular, extracellular, behavior)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The architecture of this data pipeline\n",
    "This data pipeline is comprised of three core components:\n",
    "\n",
    "+ **subject**: the meta information on the animal, e.g. genetic information\n",
    "+ **acquisition**: the organization of the experiment - session acquisition, trial structure, behavior data, photostimulation\n",
    "+ **electrophysiology**: intracellular and extracellular recordings, as well as spike sorted results"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
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       "<g id=\"a_node4\"><a xlink:title=\"subject_id           \r",
       "------------------------------\r",
       "→ subject.Species\r",
       "→ reference.AnimalSource\r",
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       "subject_description=null \r",
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       "<datajoint.diagram.Diagram at 0x20fcab66198>"
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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": 4,
   "metadata": {},
   "outputs": [
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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",
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       "                                <p id=\"nonprimary\">animal_source</p>\n",
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       "                                <p id=\"nonprimary\">subject_description</p>\n",
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       "                <tbody> <tr> <td>anm284530</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-14</td>\n",
       "<td>None</td></tr><tr><td>anm287242</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2014-12-30</td>\n",
       "<td>None</td></tr><tr><td>anm287244</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2014-12-30</td>\n",
       "<td>None</td></tr><tr><td>anm287245</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2014-12-30</td>\n",
       "<td>None</td></tr><tr><td>anm290115</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-01-06</td>\n",
       "<td>None</td></tr><tr><td>anm300476</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-03-28</td>\n",
       "<td>None</td></tr><tr><td>anm301659</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-07</td>\n",
       "<td>None</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 43</p></div>\n",
       "            "
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       "*subject_id    species        animal_source  sex     date_of_birth  subject_descri\n",
       "+------------+ +------------+ +------------+ +-----+ +------------+ +------------+\n",
       "anm284530      Mus musculus   N/A            U       2015-04-14     None          \n",
       "anm287242      Mus musculus   N/A            U       2014-12-30     None          \n",
       "anm287244      Mus musculus   N/A            U       2014-12-30     None          \n",
       "anm287245      Mus musculus   N/A            U       2014-12-30     None          \n",
       "anm290115      Mus musculus   N/A            U       2015-01-06     None          \n",
       "anm300476      Mus musculus   N/A            U       2015-03-28     None          \n",
       "anm301659      Mus musculus   N/A            U       2015-04-07     None          \n",
       "   ...\n",
       " (Total: 43)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject.Subject()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment acquisition"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
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       "<g class=\"node\" id=\"node6\"><title>acquisition.TrialSet.EventTime</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
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       "<g class=\"node\" id=\"node2\"><title>stimulation.TrialPhotoStimParam</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
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       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node3\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "session_id           \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
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       "<!-- 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",
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       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
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       "</g>\n",
       "<!-- acquisition.Session.ExperimentType -->\n",
       "<g class=\"node\" id=\"node9\"><title>acquisition.Session.ExperimentType</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ acquisition.Session\r",
       "→ acquisition.ExperimentType\r",
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       "</g>\n",
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       "<!-- acquisition.Session&#45;&gt;acquisition.Session.ExperimentType -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>acquisition.Session-&gt;acquisition.Session.ExperimentType</title>\n",
       "<path d=\"M304.709,-212.973C267.215,-199.138 215.223,-179.954 185.606,-169.026\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulation -->\n",
       "<g class=\"node\" id=\"node11\"><title>stimulation.PhotoStimulation</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"→ acquisition.Session\r",
       "photostim_datetime   \r",
       "------------------------------\r",
       "→ reference.ActionLocation\r",
       "→ stimulation.PhotoStimProtocol\r",
       "photostim_timeseries=null \r",
       "photostim_start_time=null \r",
       "photostim_sampling_rate=null \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1219.5,-177 1055.5,-177 1055.5,-142 1219.5,-142 1219.5,-177\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1063.5\" y=\"-157.4\">stimulation.PhotoStimulation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;stimulation.PhotoStimulation -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>acquisition.Session-&gt;stimulation.PhotoStimulation</title>\n",
       "<path d=\"M409.405,-226.271C527.635,-219.584 799.243,-202.67 1026.5,-177 1035.89,-175.939 1045.71,-174.688 1055.45,-173.357\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session.Experimenter -->\n",
       "<g class=\"node\" id=\"node13\"><title>acquisition.Session.Experimenter</title>\n",
       "<g id=\"a_node13\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Experimenter\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"430.5,-169 268.5,-169 268.5,-150 430.5,-150 430.5,-169\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"349.5\" y=\"-157\">acquisition.Session.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>acquisition.Session-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M349.5,-212.797C349.5,-199.077 349.5,-180.185 349.5,-169.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Experimenter -->\n",
       "<g class=\"node\" id=\"node4\"><title>reference.Experimenter</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"experimenter         \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"272,-248 155,-248 155,-213 272,-213 272,-248\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"163\" y=\"-229\">reference.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Experimenter&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>reference.Experimenter-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M246.075,-212.973C273.265,-199.178 310.938,-180.064 332.509,-169.121\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node5\"><title>subject.Subject</title>\n",
       "<g id=\"a_node5\"><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=\"397,-319 302,-319 302,-284 397,-284 397,-319\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"310\" y=\"-299.4\">subject.Subject</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;acquisition.Session -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>subject.Subject-&gt;acquisition.Session</title>\n",
       "<path d=\"M349.5,-283.797C349.5,-272.949 349.5,-258.867 349.5,-248.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"node\" id=\"node7\"><title>behavior.TrialSegmentedLickTrace</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_lick_left_on \r",
       "segmented_lick_left_off \r",
       "segmented_lick_right_on \r",
       "segmented_lick_right_off \r",
       "\">\n",
       "<ellipse cx=\"942.5\" cy=\"-17.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"125\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"942.5\" y=\"-14.4\">behavior.TrialSegmentedLickTrace</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node12\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "stop_time=null       \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "delay_duration=null  \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"828.5,-98 708.5,-98 708.5,-79 828.5,-79 828.5,-98\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"716.5\" y=\"-87\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M775.321,-141.797C773.532,-128.077 771.068,-109.185 769.643,-98.2652\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent -->\n",
       "<g class=\"node\" id=\"node10\"><title>reference.ExperimentalEvent</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"event                \r",
       "------------------------------\r",
       "description          \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1017.5,-177 877.5,-177 877.5,-142 1017.5,-142 1017.5,-177\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"885.5\" y=\"-158\">reference.ExperimentalEvent</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;1 -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>reference.ExperimentalEvent-&gt;1</title>\n",
       "<path d=\"M922.604,-141.985C907.974,-132 889.307,-118.769 873.5,-106 866.641,-100.459 859.045,-93.0295 856.438,-90.4383\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node19\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node19\"><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=\"1046.5,-106 882.5,-106 882.5,-71 1046.5,-71 1046.5,-106\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"964.5\" y=\"-86\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;analysis.TrialSegmentationSetting -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>reference.ExperimentalEvent-&gt;analysis.TrialSegmentationSetting</title>\n",
       "<path d=\"M951.615,-141.797C954.288,-130.949 957.757,-116.867 960.423,-106.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"node\" id=\"node14\"><title>stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<g id=\"a_node14\"><a xlink:title=\"→ stimulation.PhotoStimulation\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_photostim  \r",
       "\">\n",
       "<ellipse cx=\"1202.5\" cy=\"-17.5\" 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=\"1202.5\" y=\"-14.4\">stimulation.TrialSegmentedPhotoStimulus</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulation&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>stimulation.PhotoStimulation-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M1145.21,-141.89C1158.66,-112.918 1186.06,-53.9035 1197.41,-29.471\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;stimulation.TrialPhotoStimParam -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>acquisition.TrialSet.Trial-&gt;stimulation.TrialPhotoStimParam</title>\n",
       "<path d=\"M737.347,-78.97C695.603,-67.5401 620.891,-47.0833 570.037,-33.159\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;acquisition.TrialSet.EventTime -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>acquisition.TrialSet.Trial-&gt;acquisition.TrialSet.EventTime</title>\n",
       "<path d=\"M763.054,-78.7611C754.828,-65.5612 739.317,-40.672 731.038,-27.3859\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>acquisition.TrialSet.Trial-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M790.185,-78.9006C818.336,-67.7376 867.985,-48.0489 902.931,-34.1912\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge15\"><title>acquisition.TrialSet.Trial-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M802.196,-78.9223C813.669,-76.1713 826.601,-73.256 838.5,-71 908.602,-57.7094 1133.74,-27.6094 1189.78,-20.181\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimProtocol -->\n",
       "<g class=\"node\" id=\"node15\"><title>stimulation.PhotoStimProtocol</title>\n",
       "<g id=\"a_node15\"><a xlink:title=\"protocol             \r",
       "------------------------------\r",
       "→ stimulation.PhotoStimDevice\r",
       "photo_stim_excitation_lambda \r",
       "photo_stim_method=&quot;laser&quot; \r",
       "photo_stim_duration=null \r",
       "photo_stim_shape=&quot;&quot;  \r",
       "photo_stim_freq=null \r",
       "photo_stim_notes=&quot;&quot;  \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1224,-248 1051,-248 1051,-213 1224,-213 1224,-248\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1059\" y=\"-228.4\">stimulation.PhotoStimProtocol</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimProtocol&#45;&gt;stimulation.PhotoStimulation -->\n",
       "<g class=\"edge\" id=\"edge16\"><title>stimulation.PhotoStimProtocol-&gt;stimulation.PhotoStimulation</title>\n",
       "<path d=\"M1137.5,-212.797C1137.5,-201.949 1137.5,-187.867 1137.5,-177.049\" 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=\"node16\"><title>reference.TrialResponse</title>\n",
       "<g id=\"a_node16\"><a xlink:title=\"trial_response       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"570,-177 449,-177 449,-142 570,-142 570,-177\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"457\" y=\"-158\">reference.TrialResponse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialResponse&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge17\"><title>reference.TrialResponse-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M570.224,-142.323C622.256,-128.461 695.155,-109.04 736.518,-98.0202\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimDevice -->\n",
       "<g class=\"node\" id=\"node17\"><title>stimulation.PhotoStimDevice</title>\n",
       "<g id=\"a_node17\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1208,-319 1067,-319 1067,-284 1208,-284 1208,-319\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1075\" y=\"-300\">stimulation.PhotoStimDevice</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimDevice&#45;&gt;stimulation.PhotoStimProtocol -->\n",
       "<g class=\"edge\" id=\"edge18\"><title>stimulation.PhotoStimDevice-&gt;stimulation.PhotoStimProtocol</title>\n",
       "<path d=\"M1137.5,-283.797C1137.5,-272.949 1137.5,-258.867 1137.5,-248.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.TrialType -->\n",
       "<g class=\"node\" id=\"node18\"><title>reference.TrialType</title>\n",
       "<g id=\"a_node18\"><a xlink:title=\"trial_type           \r",
       "\">\n",
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       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"596.5\" y=\"-158\">reference.TrialType</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialType&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge19\"><title>reference.TrialType-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M668.878,-141.973C695.068,-128.178 731.356,-109.064 752.133,-98.1206\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge20\"><title>analysis.TrialSegmentationSetting-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M959.174,-70.797C955.715,-59.9485 951.226,-45.8669 947.777,-35.0492\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>analysis.TrialSegmentationSetting-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M1021.51,-70.9728C1077.62,-54.7061 1159.23,-31.0437 1190.08,-22.102\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.ExperimentType -->\n",
       "<g class=\"node\" id=\"node20\"><title>acquisition.ExperimentType</title>\n",
       "<g id=\"a_node20\"><a xlink:title=\"experiment_type      \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"137,-248 0,-248 0,-213 137,-213 137,-248\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"8\" y=\"-229\">acquisition.ExperimentType</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.ExperimentType&#45;&gt;acquisition.Session.ExperimentType -->\n",
       "<g class=\"edge\" id=\"edge22\"><title>acquisition.ExperimentType-&gt;acquisition.Session.ExperimentType</title>\n",
       "<path d=\"M91.2548,-212.797C110.055,-198.997 135.982,-179.966 150.817,-169.075\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x20fcaf2b7b8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(acquisition) - 1 + dj.Diagram(stimulation) + dj.Diagram(behavior)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Session` tables contains information about individual experiment session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            <table border=\"1\" class=\"Relation\">\n",
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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=\"primary\">session_time</p>\n",
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       "                                <p id=\"primary\">session_id</p>\n",
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       "                                <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>anm284530</td>\n",
       "<td>2015-07-01 00:00:00</td>\n",
       "<td>Whole_cell_105_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-14</td>\n",
       "<td>None</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-01 00:00:00</td>\n",
       "<td>Whole_cell_106_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-14</td>\n",
       "<td>None</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-02 00:00:00</td>\n",
       "<td>Whole_cell_108_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-14</td>\n",
       "<td>None</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-03 00:00:00</td>\n",
       "<td>Whole_cell_109_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-14</td>\n",
       "<td>None</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-03 00:00:00</td>\n",
       "<td>Whole_cell_110_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2015-04-14</td>\n",
       "<td>None</td></tr><tr><td>anm287242</td>\n",
       "<td>2015-08-05 00:00:00</td>\n",
       "<td>Whole_cell_131_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2014-12-30</td>\n",
       "<td>None</td></tr><tr><td>anm287242</td>\n",
       "<td>2015-08-06 00:00:00</td>\n",
       "<td>Whole_cell_133_regular</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>N/A</td>\n",
       "<td>U</td>\n",
       "<td>2014-12-30</td>\n",
       "<td>None</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 202</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    species        animal_source  sex     date_of_birth  subject_descri\n",
       "+------------+ +------------+ +------------+ +------------+ +------------+ +-----+ +------------+ +------------+\n",
       "anm284530      2015-07-01 00: Whole_cell_105 Mus musculus   N/A            U       2015-04-14     None          \n",
       "anm284530      2015-07-01 00: Whole_cell_106 Mus musculus   N/A            U       2015-04-14     None          \n",
       "anm284530      2015-07-02 00: Whole_cell_108 Mus musculus   N/A            U       2015-04-14     None          \n",
       "anm284530      2015-07-03 00: Whole_cell_109 Mus musculus   N/A            U       2015-04-14     None          \n",
       "anm284530      2015-07-03 00: Whole_cell_110 Mus musculus   N/A            U       2015-04-14     None          \n",
       "anm287242      2015-08-05 00: Whole_cell_131 Mus musculus   N/A            U       2014-12-30     None          \n",
       "anm287242      2015-08-06 00: Whole_cell_133 Mus musculus   N/A            U       2014-12-30     None          \n",
       "   ...\n",
       " (Total: 202)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition.Session.proj() * subject.Subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Trial` table (found as part table of `TrialSet`) contains information about each trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                color: black;\n",
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       "\n",
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       "                visibility: visible;\n",
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       "        </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=\"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=\"nonprimary\">start_time</p>\n",
       "                                <span class=\"djtooltiptext\">start time of this trial, with respect to starting point of this session</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">stop_time</p>\n",
       "                                <span class=\"djtooltiptext\">end time of this trial, with respect to starting point of this session</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">trial_type</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <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",
       "                                <span class=\"djtooltiptext\">is this a stim or no-stim trial</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">trial_is_good</p>\n",
       "                                <span class=\"djtooltiptext\">good/bad status of trial (bad trials are not analyzed)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">delay_duration</p>\n",
       "                                <span class=\"djtooltiptext\">(s) duration of the delay period</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">photo_stim_period</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">photo_stim_power</p>\n",
       "                                <span class=\"djtooltiptext\">(mW) stimulation power</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>1</td>\n",
       "<td>8e-05</td>\n",
       "<td>6.00012</td>\n",
       "<td>lick right</td>\n",
       "<td>no response</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>2</td>\n",
       "<td>6.0002</td>\n",
       "<td>12.0002</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>3</td>\n",
       "<td>12.0003</td>\n",
       "<td>18.0003</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>4</td>\n",
       "<td>18.0004</td>\n",
       "<td>24.0004</td>\n",
       "<td>lick right</td>\n",
       "<td>incorrect</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>5</td>\n",
       "<td>24.0005</td>\n",
       "<td>30.0006</td>\n",
       "<td>lick right</td>\n",
       "<td>incorrect</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>6</td>\n",
       "<td>30.0006</td>\n",
       "<td>36.0006</td>\n",
       "<td>lick left</td>\n",
       "<td>correct</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>7</td>\n",
       "<td>36.0007</td>\n",
       "<td>42.0007</td>\n",
       "<td>lick right</td>\n",
       "<td>incorrect</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>1.20</td>\n",
       "<td>early delay</td>\n",
       "<td>nan</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 45685</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *trial_id    start_time     stop_time     trial_type     trial_response trial_stim_pre trial_is_good  delay_duration photo_stim_per photo_stim_pow\n",
       "+------------+ +------------+ +------------+ +----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
       "anm319313      2016-01-05 00: HI72_010516    1            8e-05          6.00012       lick right     no response    0              0              1.20           early delay    nan           \n",
       "anm319313      2016-01-05 00: HI72_010516    2            6.0002         12.0002       lick left      correct        0              0              1.20           early delay    nan           \n",
       "anm319313      2016-01-05 00: HI72_010516    3            12.0003        18.0003       lick left      correct        0              1              1.20           early delay    nan           \n",
       "anm319313      2016-01-05 00: HI72_010516    4            18.0004        24.0004       lick right     incorrect      0              1              1.20           early delay    nan           \n",
       "anm319313      2016-01-05 00: HI72_010516    5            24.0005        30.0006       lick right     incorrect      0              1              1.20           early delay    nan           \n",
       "anm319313      2016-01-05 00: HI72_010516    6            30.0006        36.0006       lick left      correct        0              1              1.20           early delay    nan           \n",
       "anm319313      2016-01-05 00: HI72_010516    7            36.0007        42.0007       lick right     incorrect      0              1              1.20           early delay    nan           \n",
       "   ...\n",
       " (Total: 45685)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition.TrialSet.Trial * stimulation.TrialPhotoStimParam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electrophysiology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Electrophysiology information are found among two related schemas: `extracellular` and `intracellular`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<polygon fill=\"white\" points=\"-4,4 -4,-252 1378.25,-252 1378.25,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- reference.Probe -->\n",
       "<g class=\"node\" id=\"node1\"><title>reference.Probe</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"probe_name           \r",
       "channel_counts       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"165,-248 80,-248 80,-213 165,-213 165,-248\" stroke=\"none\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node3\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.BrainLocation\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"378,-177 221,-177 221,-142 378,-142 378,-177\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</g>\n",
       "<!-- reference.Probe.Channel -->\n",
       "<g class=\"node\" id=\"node13\"><title>reference.Probe.Channel</title>\n",
       "<g id=\"a_node13\"><a xlink:title=\"→ reference.Probe\r",
       "channel_id           \r",
       "------------------------------\r",
       "shank_id             \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"184,-169 61,-169 61,-150 184,-150 184,-169\" stroke=\"none\"/>\n",
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       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node2\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node2\"><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=\"716,-248 597,-248 597,-213 716,-213 716,-248\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</g>\n",
       "<!-- intracellular.Cell -->\n",
       "<g class=\"node\" id=\"node4\"><title>intracellular.Cell</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ acquisition.Session\r",
       "cell_id              \r",
       "------------------------------\r",
       "cell_type            \r",
       "cell_depth           \r",
       "→ reference.BrainLocation\r",
       "→ reference.WholeCellDevice\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"968.5,-177 870.5,-177 870.5,-142 968.5,-142 968.5,-177\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "<g id=\"a_node8\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
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       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node5\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "spike_times          \r",
       "unit_cell_type=&quot;N/A&quot; \r",
       "unit_spike_width     \r",
       "unit_depth           \r",
       "spike_waveform       \r",
       "\">\n",
       "<ellipse cx=\"102.5\" cy=\"-88.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"102.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"28.5\" y=\"-86.4\">extracellular.UnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M252.314,-141.973C219.758,-130.57 176.828,-115.534 145.271,-104.481\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.Voltage -->\n",
       "<g class=\"node\" id=\"node17\"><title>extracellular.Voltage</title>\n",
       "<g id=\"a_node17\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "------------------------------\r",
       "voltage              \r",
       "voltage_start_time   \r",
       "voltage_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"299.5\" cy=\"-88.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"76.0008\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"299.5\" y=\"-85.4\">extracellular.Voltage</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.Voltage -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>extracellular.ProbeInsertion-&gt;extracellular.Voltage</title>\n",
       "<path d=\"M299.5,-141.797C299.5,-130.949 299.5,-116.867 299.5,-106.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection -->\n",
       "<g class=\"node\" id=\"node6\"><title>intracellular.CurrentInjection</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ intracellular.Cell\r",
       "------------------------------\r",
       "injected_current     \r",
       "current_injection    \r",
       "current_injection_start_time \r",
       "current_injection_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"1275.5\" cy=\"-88.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"98.5006\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"1275.5\" y=\"-85.4\">intracellular.CurrentInjection</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.CurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>intracellular.Cell-&gt;intracellular.CurrentInjection</title>\n",
       "<path d=\"M968.627,-148.978C1031.86,-136.723 1141.85,-115.404 1211.55,-101.895\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential -->\n",
       "<g class=\"node\" id=\"node16\"><title>intracellular.MembranePotential</title>\n",
       "<g id=\"a_node16\"><a xlink:title=\"→ intracellular.Cell\r",
       "------------------------------\r",
       "membrane_potential   \r",
       "membrane_potential_wo_spike \r",
       "membrane_potential_start_time \r",
       "membrane_potential_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"506.5\" cy=\"-88.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"112.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"506.5\" y=\"-85.4\">intracellular.MembranePotential</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.MembranePotential -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>intracellular.Cell-&gt;intracellular.MembranePotential</title>\n",
       "<path d=\"M870.093,-150.246C798.045,-138.209 663.648,-115.755 579.978,-101.776\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.CellSpikeTimes -->\n",
       "<g class=\"node\" id=\"node18\"><title>intracellular.CellSpikeTimes</title>\n",
       "<g id=\"a_node18\"><a xlink:title=\"→ intracellular.Cell\r",
       "------------------------------\r",
       "spike_times          \r",
       "\">\n",
       "<ellipse cx=\"1058.5\" cy=\"-88.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"100.75\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"1058.5\" y=\"-85.4\">intracellular.CellSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.CellSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>intracellular.Cell-&gt;intracellular.CellSpikeTimes</title>\n",
       "<path d=\"M952.794,-141.973C975.283,-130.809 1004.79,-116.163 1026.91,-105.181\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node14\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node14\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"336.5\" cy=\"-17.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"155\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"336.5\" y=\"-14.4\">extracellular.TrialSegmentedUnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M151.554,-73.0353C190.285,-61.6148 244.217,-45.7116 283.85,-34.0249\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"node\" id=\"node7\"><title>intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ intracellular.CurrentInjection\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_current_injection \r",
       "\">\n",
       "<ellipse cx=\"1175.5\" cy=\"-17.5\" 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=\"1175.5\" y=\"-14.4\">intracellular.TrialSegmentedCurrentInjection</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>intracellular.CurrentInjection-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M1252.05,-71.3226C1231.04,-56.8235 1201.21,-36.2421 1185.72,-25.5549\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node11\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "stop_time=null       \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "delay_duration=null  \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"757.5,-98 637.5,-98 637.5,-79 757.5,-79 757.5,-98\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"645.5\" y=\"-87\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M697.5,-141.797C697.5,-128.077 697.5,-109.185 697.5,-98.2652\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"node\" id=\"node9\"><title>intracellular.TrialSegmentedMembranePotential</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ intracellular.MembranePotential\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_mp         \r",
       "segmented_mp_wo_spike \r",
       "\">\n",
       "<ellipse cx=\"641.5\" cy=\"-17.5\" 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=\"641.5\" y=\"-14.4\">intracellular.TrialSegmentedMembranePotential</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.WholeCellDevice -->\n",
       "<g class=\"node\" id=\"node10\"><title>reference.WholeCellDevice</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1026.5,-248 894.5,-248 894.5,-213 1026.5,-213 1026.5,-248\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"902.5\" y=\"-229\">reference.WholeCellDevice</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.WholeCellDevice&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>reference.WholeCellDevice-&gt;intracellular.Cell</title>\n",
       "<path d=\"M950.575,-212.797C944.129,-201.949 935.762,-187.867 929.334,-177.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge15\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M729.991,-78.9736C741.469,-76.1628 754.506,-73.1974 766.5,-71 776.557,-69.1576 1094.51,-28.7777 1162.7,-20.1235\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"edge\" id=\"edge16\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M690.408,-78.7611C679.93,-65.8513 660.378,-41.7604 649.436,-28.2785\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedCellSpikeTimes -->\n",
       "<g class=\"node\" id=\"node12\"><title>intracellular.TrialSegmentedCellSpikeTimes</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"→ intracellular.CellSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"913.5\" cy=\"-17.5\" 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=\"913.5\" y=\"-14.4\">intracellular.TrialSegmentedCellSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;intracellular.TrialSegmentedCellSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge17\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedCellSpikeTimes</title>\n",
       "<path d=\"M724.42,-78.9006C771.466,-63.8721 866.884,-33.3911 901.013,-22.489\" 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=\"edge18\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M663.045,-78.9952C651.747,-76.3092 639.113,-73.4145 627.5,-71 557.762,-56.5007 478.136,-42.2963 419.826,-32.3263\" 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=\"edge19\"><title>reference.Probe.Channel-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M119.967,-149.761C116.793,-138.81 111.286,-119.812 107.303,-106.07\" 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=\"node15\"><title>reference.BrainLocation</title>\n",
       "<g id=\"a_node15\"><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=\"876.5,-248 734.5,-248 734.5,-213 876.5,-213 876.5,-248\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"742.5\" y=\"-228.4\">reference.BrainLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge20\"><title>reference.BrainLocation-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M734.44,-214.698C731.084,-214.102 727.758,-213.532 724.5,-213 604.168,-193.343 463.591,-177.325 378.093,-168.351\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>reference.BrainLocation-&gt;intracellular.Cell</title>\n",
       "<path d=\"M832.806,-212.973C850.723,-202.128 874.07,-187.997 892.022,-177.132\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential&#45;&gt;intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"edge\" id=\"edge22\"><title>intracellular.MembranePotential-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M537.472,-71.67C567.053,-56.5508 610.028,-34.5859 630.22,-24.2651\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.CellSpikeTimes&#45;&gt;intracellular.TrialSegmentedCellSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge23\"><title>intracellular.CellSpikeTimes-&gt;intracellular.TrialSegmentedCellSpikeTimes</title>\n",
       "<path d=\"M1025.6,-71.8428C993.511,-56.5744 946.5,-34.2033 924.999,-23.972\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node19\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node19\"><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=\"939.5,-106 775.5,-106 775.5,-71 939.5,-71 939.5,-106\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"783.5\" y=\"-87\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge24\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M933.669,-70.9728C1011.21,-54.148 1125.21,-29.4111 1162.9,-21.2345\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"edge\" id=\"edge25\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M805.763,-70.9728C755.34,-54.8656 682.218,-31.507 653.612,-22.3692\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;intracellular.TrialSegmentedCellSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge26\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedCellSpikeTimes</title>\n",
       "<path d=\"M871.056,-70.797C881.932,-57.3959 896.813,-39.0613 905.758,-28.0398\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge27\"><title>analysis.TrialSegmentationSetting-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M775.196,-72.3563C772.263,-71.8869 769.357,-71.4331 766.5,-71 657.566,-54.4882 532.49,-39.6816 445.423,-30.0359\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
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      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x20fcb071668>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dj.Diagram(extracellular) - 1) + (dj.Diagram(intracellular) - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, `intracellular.CellSpikeTimes` table contains information about the intracellular recording of each cell, and the associated spike trains (in the form of spike times)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "        </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=\"primary\">cell_id</p>\n",
       "                                <span class=\"djtooltiptext\">a string identifying the cell in which this intracellular recording is concerning</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=\"nonprimary\">spike_times</p>\n",
       "                                <span class=\"djtooltiptext\">(s) time of each spike, with respect to the start of session</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">cell_type</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">cell_depth</p>\n",
       "                                <span class=\"djtooltiptext\">(um)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">device_name</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">brain_location_full_name</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm284530</td>\n",
       "<td>2015-07-01 00:00:00</td>\n",
       "<td>Whole_cell_105_regular</td>\n",
       "<td>cell_105</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>235.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-01 00:00:00</td>\n",
       "<td>Whole_cell_106_regular</td>\n",
       "<td>cell_106</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>280.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-02 00:00:00</td>\n",
       "<td>Whole_cell_108_regular</td>\n",
       "<td>cell_108</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>517.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-03 00:00:00</td>\n",
       "<td>Whole_cell_109_regular</td>\n",
       "<td>cell_109</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>582.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td></tr><tr><td>anm284530</td>\n",
       "<td>2015-07-03 00:00:00</td>\n",
       "<td>Whole_cell_110_regular</td>\n",
       "<td>cell_110</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>611.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td></tr><tr><td>anm287242</td>\n",
       "<td>2015-08-05 00:00:00</td>\n",
       "<td>Whole_cell_131_regular</td>\n",
       "<td>cell_131</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>293.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td></tr><tr><td>anm287242</td>\n",
       "<td>2015-08-06 00:00:00</td>\n",
       "<td>Whole_cell_133_regular</td>\n",
       "<td>cell_133</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Pyr</td>\n",
       "<td>620.0</td>\n",
       "<td>Multiclamp 700B</td>\n",
       "<td>N/A</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 89</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *cell_id     *brain_region  *brain_subregi *cortical_laye *hemisphere    spike_time cell_type     cell_depth     device_name    brain_location\n",
       "+------------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +--------+ +-----------+ +------------+ +------------+ +------------+\n",
       "anm284530      2015-07-01 00: Whole_cell_105 cell_105     ALM            N/A            N/A            left           =BLOB=     Pyr           235.0          Multiclamp 700 N/A           \n",
       "anm284530      2015-07-01 00: Whole_cell_106 cell_106     ALM            N/A            N/A            left           =BLOB=     Pyr           280.0          Multiclamp 700 N/A           \n",
       "anm284530      2015-07-02 00: Whole_cell_108 cell_108     ALM            N/A            N/A            left           =BLOB=     Pyr           517.0          Multiclamp 700 N/A           \n",
       "anm284530      2015-07-03 00: Whole_cell_109 cell_109     ALM            N/A            N/A            left           =BLOB=     Pyr           582.0          Multiclamp 700 N/A           \n",
       "anm284530      2015-07-03 00: Whole_cell_110 cell_110     ALM            N/A            N/A            left           =BLOB=     Pyr           611.0          Multiclamp 700 N/A           \n",
       "anm287242      2015-08-05 00: Whole_cell_131 cell_131     ALM            N/A            N/A            left           =BLOB=     Pyr           293.0          Multiclamp 700 N/A           \n",
       "anm287242      2015-08-06 00: Whole_cell_133 cell_133     ALM            N/A            N/A            left           =BLOB=     Pyr           620.0          Multiclamp 700 N/A           \n",
       "   ...\n",
       " (Total: 89)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intracellular.CellSpikeTimes() * intracellular.Cell * reference.BrainLocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, `extracellular.UnitSpikeTimes` table contains information about each and every sorted units and their associated spike trains (in the form of unit spike times)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            <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\">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\">spike_times</p>\n",
       "                                <span class=\"djtooltiptext\">(s) time of each spike, with respect to the start of session</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_cell_type</p>\n",
       "                                <span class=\"djtooltiptext\">e.g. cell-type of this unit (e.g. wide width, narrow width spiking)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_spike_width</p>\n",
       "                                <span class=\"djtooltiptext\">(ms) spike width of this unit, from bottom peak to next positive peak or time point spike terminates</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_depth</p>\n",
       "                                <span class=\"djtooltiptext\">(mm)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spike_waveform</p>\n",
       "                                <span class=\"djtooltiptext\">waveform(s) of each spike at each spike time (spike_time x waveform_timestamps)</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>0</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.68</td>\n",
       "<td>953.248</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>1</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.64</td>\n",
       "<td>925.127</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>2</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.8</td>\n",
       "<td>878.272</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>3</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.68</td>\n",
       "<td>803.031</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>4</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.64</td>\n",
       "<td>801.097</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>5</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.84</td>\n",
       "<td>621.366</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm319313</td>\n",
       "<td>2016-01-05 00:00:00</td>\n",
       "<td>HI72_010516</td>\n",
       "<td>A2x32-8mm-25-250-165</td>\n",
       "<td>64</td>\n",
       "<td>ALM</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>6</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>N/A</td>\n",
       "<td>0.68</td>\n",
       "<td>623.204</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 3157</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *session_id    *probe_name    *channel_count *brain_region  *brain_subregi *cortical_laye *hemisphere    *unit_id    channel_id     spike_time unit_cell_type unit_spike_wid unit_depth     spike_wave\n",
       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +--------+ +------------+ +------------+ +------------+ +--------+\n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           0           11             =BLOB=     N/A            0.68           953.248        =BLOB=    \n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           1           11             =BLOB=     N/A            0.64           925.127        =BLOB=    \n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           2           11             =BLOB=     N/A            0.8            878.272        =BLOB=    \n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           3           11             =BLOB=     N/A            0.68           803.031        =BLOB=    \n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           4           11             =BLOB=     N/A            0.64           801.097        =BLOB=    \n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           5           11             =BLOB=     N/A            0.84           621.366        =BLOB=    \n",
       "anm319313      2016-01-05 00: HI72_010516    A2x32-8mm-25-2 64             ALM            N/A            N/A            left           6           11             =BLOB=     N/A            0.68           623.204        =BLOB=    \n",
       "   ...\n",
       " (Total: 3157)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracellular.UnitSpikeTimes * extracellular.ProbeInsertion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduced analysis and figures\n",
    "\n",
    "The following sections outline the steps to search and extract relevant data, perform some level of data processing, restructuring, and plot for several figures from the two studies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inagaki 2019 - Reproduce Figure 2 - whole-cell\n",
    "\n",
    "Figure 2c depicts the no-photostim trial-averaged spike PSTH (top) and membrane potential (bottom) for a representative cell. \n",
    "\n",
    "Figure 2b shows the cell's membrane potential of several individual trials, all time-locked to `go cue`\n",
    "\n",
    "To reproduce this figure, several steps are taken:\n",
    "1. Select a representative cell (here we picked cell 96)\n",
    "2. Specify trial selection criteria: ipsi vs. contra trial, no photostim\n",
    "3. Extract ***trial-segmented membrane potential*** and ***trial-segmented spike times***, categorized by ipsi vs. contra trials\n",
    "4. Plot individual trial membrane potential, trial-averaged membrane potential and spike PSTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a particuclar setting defining how to segment out information per trial, \n",
    "# including the definition of even for alignment\n",
    "seg_param_key = (analysis.TrialSegmentationSetting & {'event': 'cue_start',\n",
    "                                                      'pre_stim_duration': 4,\n",
    "                                                      'post_stim_duration': 2}).fetch1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we specify trial-restrictor: ipsi vs. contra trials, no photostim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nostim_contra_trials = {'trial_type': 'lick right', 'trial_response': 'correct', 'trial_stim_present': False}\n",
    "nostim_ipsi_trials = {'trial_type': 'lick left', 'trial_response': 'correct', 'trial_stim_present': False}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we pick a representative cell - cell 96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_96 = (intracellular.Cell & {'cell_id': 'cell_96'}\n",
    "           & (acquisition.Session.ExperimentType & {'experiment_type': 'regular'})).fetch('KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we extract the recorded Membrane Potential of this cell, separate into **contra** and **ipsi** trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# contra trials\n",
    "contra_trial_mp, contra_trial_mp_wo_spike = (intracellular.TrialSegmentedMembranePotential & cell_96 & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & nostim_contra_trials)).fetch('segmented_mp', 'segmented_mp_wo_spike')\n",
    "# ipsi trials\n",
    "ipsi_trial_mp, ipsi_trial_mp_wo_spike = (intracellular.TrialSegmentedMembranePotential & cell_96 & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & nostim_ipsi_trials)).fetch('segmented_mp', 'segmented_mp_wo_spike')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we extract spike times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# contra trials\n",
    "contra_trial_spiketimes = (intracellular.TrialSegmentedCellSpikeTimes & cell_96 & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & nostim_contra_trials)).fetch('segmented_spike_times')\n",
    "# ipsi trials\n",
    "ipsi_trial_spiketimes = (intracellular.TrialSegmentedCellSpikeTimes & cell_96 & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & nostim_ipsi_trials)).fetch('segmented_spike_times')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then perform several processing, data conditioning steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query event times (e.g. sample-start, delay-start, etc.)\n",
    "events, event_times = (analysis.RealignedEvent.RealignedEventTime & seg_param_key & cell_96 &\n",
    "                      (acquisition.TrialSet.Trial & [{'trial_response': 'correct'},\n",
    "                                                     {'trial_response': 'incorrect'}])).fetch('trial_event',\n",
    "                                                                                              'realigned_event_time')\n",
    "# Calculate mean event times across trials\n",
    "mean_event_times = {name: np.nanmedian([t for n, t in zip(events, event_times) if n == name]) for name in set(events)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct time vector\n",
    "fs = intracellular.MembranePotential.fetch('membrane_potential_sampling_rate', limit=1).item()\n",
    "timestamps = np.arange(-1 * float(seg_param_key['pre_stim_duration']) * fs,\n",
    "                       float(seg_param_key['post_stim_duration']) * fs + 1) / fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute spike histogram \n",
    "bin_counts = 100\n",
    "contra_trial_spikecounts, contra_edges = np.histogram(np.hstack(contra_trial_spiketimes).flatten(), \n",
    "                                                      bins=bin_counts, range=(timestamps[0], timestamps[-1]))\n",
    "contra_trial_spikerate = contra_trial_spikecounts / np.diff(contra_edges) / contra_trial_spiketimes.size\n",
    "\n",
    "ipsi_trial_spikecounts, ipsi_edges = np.histogram(np.hstack(ipsi_trial_spiketimes).flatten(), \n",
    "                                                      bins=bin_counts, range=(timestamps[0], timestamps[-1]))\n",
    "ipsi_trial_spikerate = ipsi_trial_spikecounts / np.diff(ipsi_edges) / ipsi_trial_spiketimes.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Figure 2c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reproduce Figure 2c \n",
    "fig2b, axs = plt.subplots(2, 1, figsize=(4, 8))\n",
    "\n",
    "axs[0].plot(contra_edges[1:], contra_trial_spikerate, 'b')\n",
    "axs[0].plot(ipsi_edges[1:], ipsi_trial_spikerate, 'r')\n",
    "axs[0].set_ylabel('Spike per s')\n",
    "\n",
    "axs[1].plot(timestamps, np.vstack(contra_trial_mp_wo_spike).mean(axis=0), 'b')\n",
    "axs[1].plot(timestamps, np.vstack(ipsi_trial_mp_wo_spike).mean(axis=0), 'r')\n",
    "axs[1].set_ylabel('Vm (mV)')\n",
    "\n",
    "for ax in axs:\n",
    "# event markers\n",
    "    for e in ('sampling_start', 'delay_start', 'cue_start'):\n",
    "        ax.axvline(x=mean_event_times[e], linestyle='--', color='k')\n",
    "\n",
    "    # Hide the spines\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.set_xlim((-3, 1));\n",
    "    ax.set_xlabel('Time to go cue (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot several single-trial membrane potential, overlayed by the lick traces of that trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Single trial - correct lick right trials (contra-trial) #2 ~ 7\n",
    "contra_trial_lick_right = (behavior.TrialSegmentedLickTrace & cell_96 & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & nostim_contra_trials)).fetch('segmented_lick_right_on')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Figure 2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reproduce Figure 2b\n",
    "trials = np.arange(1,7)\n",
    "fig2c, axs = plt.subplots(trials.size, 1, figsize=(4, 8))\n",
    "fig2c.subplots_adjust(hspace=0.4)\n",
    "for ax, tr in zip(axs, trials):\n",
    "    ax.plot(timestamps, contra_trial_mp[tr], 'b')\n",
    "    # lick times\n",
    "    for t in contra_trial_lick_right[tr]:\n",
    "        ax.axvline(x=t, linestyle='-', color='g')\n",
    "    # event markers\n",
    "    for e in ('sampling_start', 'delay_start', 'cue_start'):\n",
    "        ax.axvline(x=mean_event_times[e], linestyle='--', color='k')\n",
    "    ax.set_ylabel('Vm (mV)')\n",
    "    ax.set_ylim((-55, -35))\n",
    "    ax.set_xlim((-3, 1))\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "axs[-1].set_xlabel('Time to go cue (s)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Inagaki 2019 - Reproduce Figure 5a - extracellular - fixed-delay\n",
    "\n",
    "Figure 5a shows the spike raster and PSTH of a representative unit across four trial conditions:\n",
    "+ Control (no-stim) trials with correct response\n",
    "+ Control (no-stim) trials with incorrect response\n",
    "+ Perturbed (photostim) trials with correct response\n",
    "+ Perturbed (photostim) trials with incorrect response\n",
    "\n",
    "Each with trials categorized in ipsi vs. contra trials\n",
    "\n",
    "To reproduce this figure, we will build several helper functions: \n",
    "+ `query_unit_segmented_spiketimes()` - for any specified session, unit, and trial criteria, extract the trial-segmented unit spike times, categorized in ipsi and contra trial groups\n",
    "+ `plot_spike_raster_and_histogram()` - given spike times in ipsi and contra trial groups, plot spike raster and PSTH "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query trial-segmented spiketimes for a single unit in the one specifed session\n",
    "def query_unit_segmented_spiketimes(sess_key, unit, trial_key, seg_param_key):\n",
    "    data_keys = (extracellular.TrialSegmentedUnitSpikeTimes & sess_key & {'unit_id': unit} & seg_param_key &\n",
    "                 ((acquisition.TrialSet.Trial & trial_key))).fetch('KEY')\n",
    "    return pd.DataFrame([dict(**dict(zip(*(analysis.RealignedEvent.RealignedEventTime & k).fetch('trial_event', 'realigned_event_time'))), \n",
    "                 segmented_spike_times=(extracellular.TrialSegmentedUnitSpikeTimes & k).fetch1(\n",
    "                     'segmented_spike_times')) for k in data_keys])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spike_raster_and_histogram(contra_spike_times, ipsi_spike_times, axes, ax_title='', bin_counts=200, xlim=(-1.5, 3), shade_bar=False):\n",
    "    # get event timing\n",
    "    events = ['sampling_start', 'delay_start', 'cue_start']\n",
    "    event_times = np.around([np.median(np.hstack([ipsi_spike_times[e], contra_spike_times[e]])) for e in events if e in ipsi_spike_times], 4)\n",
    "    \n",
    "    # restructure data for spike raster  \n",
    "    ipsi_c_trial_idx, ipsi_c_spike_times = zip(*((np.full_like(r, ri), r)\n",
    "                                            for ri, r in enumerate(r for r in ipsi_spike_times.segmented_spike_times if len(r) != 0)))\n",
    "    ipsi_c_trial_idx = np.hstack(ipsi_c_trial_idx)\n",
    "    ipsi_c_spike_times = np.hstack(ipsi_c_spike_times)    \n",
    "    \n",
    "    contra_c_trial_idx, contra_c_spike_times = zip(*((np.full_like(r, ri), r)\n",
    "                                            for ri, r in enumerate(r for r in contra_spike_times.segmented_spike_times if len(r) != 0)))\n",
    "    contra_c_trial_idx = np.hstack(contra_c_trial_idx)\n",
    "    contra_c_spike_times = np.hstack(contra_c_spike_times)  \n",
    "    \n",
    "    # spiketime histogram\n",
    "    time_range = (float(-1*seg_param_key['pre_stim_duration']), float(seg_param_key['post_stim_duration']))\n",
    "    \n",
    "    ipsi_spk_counts, ipsi_edges = np.histogram(np.hstack([r.segmented_spike_times\n",
    "                                                          for _, r in ipsi_spike_times.iterrows()]),\n",
    "                                               bins=bin_counts,\n",
    "                                               range=(time_range[0], time_range[-1]))\n",
    "    ipsi_spk_rates = ipsi_spk_counts / np.diff(ipsi_edges) / ipsi_spike_times.segmented_spike_times.shape[0]\n",
    "    \n",
    "    contra_spk_counts, contra_edges = np.histogram(np.hstack([r.segmented_spike_times\n",
    "                                                          for _, r in contra_spike_times.iterrows()]),\n",
    "                                                   bins=bin_counts,\n",
    "                                                   range=(time_range[0], time_range[-1]))\n",
    "    contra_spk_rates = contra_spk_counts / np.diff(contra_edges) / contra_spike_times.segmented_spike_times.shape[0]\n",
    "   \n",
    "    # plot\n",
    "    # spike raster\n",
    "    ax_top = axes[0]\n",
    "    ax_top.plot(contra_c_spike_times, contra_c_trial_idx , '|b', markersize=2)\n",
    "    ax_top.plot(ipsi_c_spike_times, ipsi_c_trial_idx + contra_c_trial_idx.max(), '|r', markersize=2)\n",
    "    # event markers\n",
    "    for e in event_times:\n",
    "        ax_top.axvline(x=e, linestyle='--', color='k')\n",
    "    ax_top.set_xticklabels([])\n",
    "    ax_top.set_yticklabels([])\n",
    "    ax_top.set_title(ax_title)\n",
    "    ax_top.set_xlim(xlim);\n",
    "    \n",
    "    # spike histogram\n",
    "    ax_bot = axes[1]\n",
    "    ax_bot.plot(contra_edges[1:], contra_spk_rates, 'b')\n",
    "    ax_bot.plot(ipsi_edges[1:], ipsi_spk_rates, 'r')\n",
    "    for e in event_times:\n",
    "        ax_bot.axvline(x=e, linestyle='--', color='k')\n",
    "    ax_bot.set_xlim(xlim);\n",
    "    ax_bot.set_xlabel('Time to go cue (s)')\n",
    "    ax_bot.set_ylabel('Spikes per s')\n",
    "    \n",
    "    # shade-bar for photostim\n",
    "    if shade_bar:\n",
    "        for ax in axes:\n",
    "            ax.axvspan(event_times[1], event_times[1] + 0.6, alpha = 0.3, color = 'royalblue')\n",
    "    \n",
    "    # Hide the spines\n",
    "    ax_top.spines['right'].set_visible(False)\n",
    "    ax_top.spines['top'].set_visible(False)\n",
    "    ax_top.spines['left'].set_visible(False)\n",
    "    ax_top.spines['bottom'].set_visible(False)\n",
    "    ax_bot.spines['right'].set_visible(False)\n",
    "    ax_bot.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define trial-restrictor for 3 criteria:\n",
    "1. trial-response: correct/incorrect \n",
    "2. trial-type: lick-left/lick-right\n",
    "3. photostim: stim/nostim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_contra_trial_ctrl  = {'trial_type': 'lick right', 'trial_response': 'correct', 'trial_stim_present': False, 'trial_is_good': True}\n",
    "correct_contra_trial_stim  = {'trial_type': 'lick right', 'trial_response': 'correct', 'trial_stim_present': True, 'trial_is_good': True}\n",
    "incorrect_contra_trial_ctrl  = {'trial_type': 'lick right', 'trial_response': 'incorrect', 'trial_stim_present': False, 'trial_is_good': True}\n",
    "incorrect_contra_trial_stim  = {'trial_type': 'lick right', 'trial_response': 'incorrect', 'trial_stim_present': True, 'trial_is_good': True}\n",
    "\n",
    "correct_ipsi_trial_ctrl  = {'trial_type': 'lick left', 'trial_response': 'correct', 'trial_stim_present': False, 'trial_is_good': True}\n",
    "correct_ipsi_trial_stim  = {'trial_type': 'lick left', 'trial_response': 'correct', 'trial_stim_present': True, 'trial_is_good': True}\n",
    "incorrect_ipsi_trial_ctrl  = {'trial_type': 'lick left', 'trial_response': 'incorrect', 'trial_stim_present': False, 'trial_is_good': True}\n",
    "incorrect_ipsi_trial_stim  = {'trial_type': 'lick left', 'trial_response': 'incorrect', 'trial_stim_present': True, 'trial_is_good': True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sess_spiketimes(sess_key, unit_num):\n",
    "    return dict(correct_ctrl={k: query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                              unit=unit_num,\n",
    "                                                                              trial_key=trial_k,\n",
    "                                                                              seg_param_key=seg_param_key) \n",
    "                              for k, trial_k in zip(('contra', 'ipsi'), (correct_contra_trial_ctrl, correct_ipsi_trial_ctrl))},\n",
    "                incorrect_ctrl={k: query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                                       unit=unit_num,\n",
    "                                                                                       trial_key=trial_k,\n",
    "                                                                                       seg_param_key=seg_param_key) \n",
    "                                for k, trial_k in zip(('contra', 'ipsi'), (incorrect_contra_trial_ctrl, incorrect_ipsi_trial_ctrl))},\n",
    "                correct_stim={k: query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                              unit=unit_num,\n",
    "                                                                              trial_key=trial_k,\n",
    "                                                                              seg_param_key=seg_param_key) \n",
    "                              for k, trial_k in zip(('contra', 'ipsi'), (correct_contra_trial_stim, correct_ipsi_trial_stim))},\n",
    "                incorrect_stim={k: query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                                unit=unit_num,\n",
    "                                                                                trial_key=trial_k,\n",
    "                                                                                seg_param_key=seg_param_key) \n",
    "                                for k, trial_k in zip(('contra', 'ipsi'), (incorrect_contra_trial_stim, incorrect_ipsi_trial_stim))}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots for example units - spike raster and PSTH - Fig 5a\n",
    "\n",
    "Here we pick ***HI127_031617*** as the representative session, with unit ***24***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess_spiketimes = get_sess_spiketimes(sess_key=extracellular.UnitSpikeTimes & {'session_id': 'HI127_031617'}, \n",
    "                                      unit_num=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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/HqNt0tDlw8MIjaWx9NL5tO9p2w1GW4JvzDTSJk45MGm5x1eiShs7xZ6t0IZSbZGq6WYN1GPKvajU6kctBCFLAd/ch8cDdtOmTXbTpk3unFopaV1S8OXFvHshD3FsIyNS/qZNm+ymM86o0iOk65cr1cWvca8pz0f7m7eB5gn1By+X91HdJk78HtNy/f3yfcLpDVmcm1jtUtOqBzYUQv3UbPOf0AZPLL7yPITqGHU1wAj5K+NL27JGyZO6gUpsXIiVI+WJjTfW2k1nnOH6hG7GRP8vLKTRnkJLPXLzS3kh9OxQq33Igh96BqQ0PQzxcBuMwuORvJs2bbLrL/p0Yw/rjreub+6BbTH+JbcrNi42LLsyptFNnKa4uRZBfn4pL0wVQzKuMLTl4X11E6cC71fSc52bqdKFp7QciS6p4Utk6VIbwNrFxWFFSVp6KykrvBypbq4QS+CTMtoWXyZtp/SfQlIEYzt50rjQEhGJZn89tHSa9iNXxkNCkiujMcHK+4bSwJc4UcMGfy5oG6RnSqIzdj2M/DxTVmC9A/m/2JORtDFjRF0doy5vG+fyuDZljZInpc9DS4NSaAgYmobKD9FWZyBsgibPGkmpgYXYUtLQtdA4HiqH5wsZg1PH49j9jpVRZ1C11l615cHGE1yL8HLE4AQ45ZkNGa05Upd0UnkuyctY2XwuMM5xMh35+aW8sG9iBANJGx6u5d9xG2zajH+SEaqJUYnzvbXD55NH0nOdm6nSQsyiT490osOVKO7ttL3m8xsD2JXzz7crKytCl/bK594J/q5pSFBI3kyex9MUUIRXVlYcbdz7ygUMVzZ5P0n9F+rnOmVammhI5xJ9PK0k9KT+JOWtrKzYlfPPH9AiTbxp39N4euRoL3zz80xZIWygSB2Mra03zkjPQ6iOUQfiEfIH6WtSZpv6mwgwzvc0nn5iTCovNl77//QTKdbalRe/2I1r3a7ztnqBu7jo/q9eXaUhpR3Wxj8jFkZufikvhMbKmLFSMhZLcpin9TL4/POH6wvRICF2v2NKX0hx7mFlZcVe+vEbp++BDV1L2deCy/0YQoajiDxcARz/8rzTm/BS5OeX8sJU0Z+nThot52htebiWf8dtsGljfOdjdYIRuHK/pLRtDcjtkfRc52aqtBCy3HIByC0NdJLElVlqIfTKYC+uA7epSHACF1KOaV2SxYLXx/Nw+gQFtbLZCRdcXDnjirFEm5SHxkvlctRZyrlCLinfEg30Gu176k2nfUInSaEJs6SIt5kAxZGfZ8oK8nPTFIkDd/ZNkmpQ2SSOIo+3ohmkiWwbulme/pjL6wgZpFLrbdenufmlvBAbK1PGTz6u+zgpLZXBKXzS5h6H6ODXBGTbxIlD4gNpnuTPUzdW4nMjGhdAZ2GhKoNr0k8Y+fmlvDBVTE0Gt3zGJsHDs+yBrdwvqjdwI9n0eDrpuc7NVGmBKiWStdHa6sAtDex8Z92IwtgfjPnEidZB/3NGpYIiJGy5kkXpkR6WHi39B63OA8utoPThDSmnkgLIPTEhhopNNCRlOqQUcwEsTWQpjWA7mMbumUQbb0fd/zTk55mywngGvsSBeyYUWOaBtNbmnPClgxqDaFxTsPvT38XUXwuNP7S+lHrb9WlufikxDPdnE8OfpNBExtvO4qLjkZQJXNN7PGL+7AqsNEGNtZH2fZNlxA3u7xD/5kVuXikxTBWzIIMnosDOKAq8X0nPdW6mSgvUIih5CblyJi2fDXn2eDpq/ZUUXV6fNMgDwx4/np4qoDRe8h769NTb6OMkUIuLZHX19VEvM61HKpe3XaqTw5dDP/sj1SvdGz954X3d6bglhoTevvD0bfXXpXsWozekVDe38OfnmbJCPcaovFU88gWikbAYdXLepoxQXjYWBa/ztFJ57NhX6kdVTsaD3PxSYuj1DGHn0LjYNF4oN7hKYRwYsdxxTX75kuL+BLjumW8i03j6uraHDL5SeQRDKyhiCnCs3vHwe25eKTFMFQUqRBWoAltFgfcr6bnOzVTNmI972SQvHR2kQ15Xa8MWfaBqTQx5TOk1jphHkgoIiV6q0PH6LXvQYhMFWjZVFkP18/++HbytqQKQtpe2j0+AQveTe1J92Zw+wAlPv7kWD7ElUPwZkN6XayqIey3Q0FB4jnGyWvHIF4hGwqJpG2IrIdpAUliksTeWVipPGtckns9zD3PzS4nBIeaBDSkhdfHC/6FVCpLxOTQuUwOLZGxJHc8D6Tqdjn3x048decJrgYoS258Ah+YpNE4yztJ5g9S3IeO4/8+N6iEaBJr6cyYqo5vwbur8IrE0DQn8W4cRjKcjKURToLFYBXbcBls+DkrjqGVGf7/vBHWChQzRk0PSc52bqdICHbCljuWDruSB9UfpGhvgxeUwkiIkLQP0kLyHqcoqVzxJGZUHLfQQ0bpCSqH0vgqvk9JDH+5Ym2l76I7Nvjyhv0VFW1JAeBp/vxYW3OSXC0+p/bw8+n98k//8PFNWGEbCpKgtivHABibMjYR7THCEJrehdG36QyonpBRIaUPLjdlY3VdYQu/ecAHcpn3p/ZCbX0oM9YgZVJvEW4FH+FhOZalUbszYksoHgfK9sWUcHlhLyhE9sCGaqXGWzxtCtKfIP6lvQvG8T6iDgc8F6jDKGDWM3LxSYuj1TBorN07L0o+kwE6BxmIV2HEbbKX5vzBGDPFvaB49KToFylNCbqZKC3xQ5DdFGtytlSfJktWSCcTNgN28eXO1rDrvgwQpj6QcenAPoPDQbN682W4+88zha1JbuQIcUpZDZUhe6Fib+cQiZPGV+k/Kw/Nywd473wy4PqE0UqXb54/1Wcgg0E6g5ueZsoLQQzW8MwI2b9484N+cCIwTjeirm3Sm5qtLPypaKCn0er9PpPHWH0MCtc14XJNSQwIPc8QMqk3ircAj3PAZU47qPLCpfBBIt3nzZvvhV58xlkmv6IGlCNEszWXo/xRZxvOFFI46uW/J/ZLmHNNHbl4pMTg0mc80nfuQ9CPJ4CnQuHnzZrvxvVeXp8CO2+jOx0HOnz1U+Fc9sGMOtAO5QKLvTYW8pVQIcM8M29G2n86DluXp8HlCG7HwelOWMcU8J7RcT5+k0FF6JUWQW7Rj9dI+oWkkz5Y0QaACMaQY8z6yduC15fmoxdmnl/qU3mPeRzGLYEyAN0d+nikrDGPyA2B+jMOrUDfpTM03Kh11aKGkiNdD46R6YHOHchAyNjbJP2K6cX9DslaBlWipk2cjtG8ondTnsTGm6dg33rEpN6+UGBQME/kOrGJcSHquczNVWuBeT2lpEbX2cW8tV1wl671XRjsduw2w2849V1ZsQ14AKQ2vg/4PKaBcEaXXu127bds2RxsvV1I6pf7gdYe8oTwPpyV0zuuOpec0xuoOvT9r7aBPpPvD719IsCZYmRsgP8+UFapoqng1xLZt2+y2bdtGLmdSKIK+kgwI3a7d9qIXOR6mG7RZGzZcTha5+aWIAOAcADcAuOGyyy5zPRMzoEgKVQqvC2VuO/dcmUdSlKhAmY3pCqTbtm2bfd9ffnkkb6sUvn3Wu4ZpCcmkkGzm+bgBOpa/rm+5nCfytD+mUdndhE+byt542dl5p8DQti9boQgZF0FbHt5XFdih+9XmmYgZuZrL7jQZlZowa+CDMFdkJM+bdO7BPbqsnM7i4mBDEZqXutRDSlrMQ0D/hxRgScm2tu+ZrGyWIAkY/k4w7w/aVlofX74ce+D4BFNSiLlyLk1KqbJN6fBeWG6o4Oe9PJX7xe8bP3KFmdZb926x1H4JwAabm2cyB3HyO+ifWN/F+zYBI71/MwUUQV+epX0y0NuILWb8k3h3ghRpGAq9nhHuTeicx4UUKKHMDsiXAGJ1hJ6FEJ0h+Srl5TLey5uWmzhZyO/NesU2Oh+QaAz1LW+nZMCWFEwuE30aKq+F+ZLvkw7dkyQ2v6mbI6UgPgbk5pUSQ9u+bIUiZFwExb4DmwningNNITmBJAdVYmkpITdTtWM+aYlobEMDGu//BxRE21MQOzFvnQe93nSNeMhCSdvDlcAeXf2XrXk9VNHmcXzCwOuvs8bWtYPXHbP2SoKSg3tcQwp3tzuY/Hrl16/hpwqyvy4ZLvhR6ouYQs7h8ufmmZLCcH/So3RtBFQ2YWuKVA9PKF9C+o7fsKhJPU3pqMvTpJ9T+0Squ+5e98bOzsKC7SwsWLtqVXXsW7XKHRcWhnk39IpCHf31yM0vJYbhviT/u10re2BjMriX/7rOcJl9Hq6TSzEFT0ojyU0OIpcqtFFlLaCMtvHAesW24oEV6Ox2bZWfqBwKTQypsZ0rplK7pXkCNYxzuohSP7QJTK/ubteV1emwMhkdMXEwdM3L8t4zV7lP+XmlxBDGGGQuhyqws4Wh+9XmmZCcPynzABlJz3VupmrGfHzwlgZwbunzoJMdaknsdxf61yoTzBTllQ7YtDwpPc0jXQsJ/J5wqHhgpTS0LVK9/hijgV+LgSqKlJ5Y+wNCsAJJkfTxTMh2Fhbkz+hIVueQRSgm3CVrcqx/1AMr8+9wP4X7cAT0jTxtELIgpuZLSN83kDWppykdTfI0KTNWrlR3rF8Ir/b7hAaeP1TmuPqO5NCQyMO+w0IpQgYV8l+2X3ZkD2wqAvm6aFBet1uljShr4/iMDldsKxNgQb672RqqaeoUcul6nfFVmicAVSMSQ0WB7XYHDgG4N8C66PaPQ4ZlRqpE/tC10DjRO9PQjH/HDVVgZwsF3q+k53p/zAo2bAA2bhwcu10X7+P8+YYNg3hrB+dbtgziu91B/IYNg/y+bABYs6ZaPgenZePGQR0SfLm0Dl4WUKXNtwkAlpbc8Y47qnlpu339nh7fNo66uI0bgU5nEE8Dx/Kyo43TTGnn5fN7J5Xf6QzaTOHbRuvZuBE49FCX5447BveO1sVp53RK/c/TSm2RYG1NAgWA4X4dFxYX2+flNKXSGHruQ+A01uVrSkeTPE3KjJUr1R3rFxq3vOz6ZM2aKt8vLLgjHQ98Pj9GjavvFK0R7GI6Vgb+R29P3VjbNF+3QXkbNqDLzj0eOTo+xnz4gwfgLeftTapm53n/r1g3gErnuL/d4TT8P4Uk/8j/IbHO50Yey8vA8rJLL1TTF7udDrBhA/xvQPEGdJYBLG1wU4Ze2cvLwFKvDM/OnPylJeEZ6XaB5WVXL4CPLHZxltQehUKx7yJV080aYstlQlYDmpYvfaHeWW7R7HaHP8pd5830dMRc8JwG3gbuoZS8HkB1CTH3AgfeEa3ExSAtT4p5OULLl2JrgUJxTTxS7F6K78DWlTEOhC3Z+XmmrNC8L5t6Xkj6Wmti6vPZho5QPkofXeIcq7eu7lH7LFZnrGxpWZCUNxTHr1nSJ3Qs5Bs3NV+G1Ba5+aXEMIQRWLQ2f6fTsYuLnUas2jRNKnhZKd4bwE7Eg5PaLmnxGb9OxXto435pCiFdBzp2YaEztGK5261uHULLiE3reJ0hBBZj5OaVEsMA45AbNYwpvibTtq6GdafkG8UDK70KsOOt6+UVFHUrH2IyUsqXOicIyNkhmdpjys7i4vCqUyltHa3jQ9JznZup0oK0hLPfzMDoxhVYrtxJy1h7cVsBu3XrVltRiOhoL00u65bq8JGfL7fjZfN29ZTWrWef7WjjSibPw6WDL4MjtOy55p2XSvoU5TMmhWKSMzZY9ejbunWr3Xr22YM21y2rCpWXijqJm5tfygv1aGLAqMm/detWxyOpdcXqa2sEibSnP77U1dt0aeyoBpuY8YePV/wo5Q3F8WtWGHOl/NMyTOXnlxLDcCcJsbGhtMnjunXrVgtsrWXVUH0paeoQGua3bt1qL750c3SSe/ZbH5uIAptiK+bpJHFIpzVUbNLpEv+inZ/DSmWffPLgfgGVFcTi8CBNq0I2xxRbJDvm5pUiAkIbKY5DbtQw5tatW+3WpvPAMdWdki+Fh0PBYvj9dwtU+VeSVSG6QzJSypc6JwjI2ZBM3QoM7ldM/tbROj6kPeOpCbMG6V2Y2EYekmmRKnOS1cOP2v6/P9LvxC4sDOJDih2Nj0lR7q2l+SUFltLP2xmyinhpI5lFabmUJm+F4Q+slJ7Ww+niqFNEJYWb18fB03e7gw2cmqApE0ozApZCQyXzifvPAAAgAElEQVSErYqUf5tYGjlC6Zs+i6nxdeBtCbUtVm+sbul6043kYmVSoxJ1z9B6/DUaiFW3siqFlkXz8jpoHv6N79RNm3ibQnmUf5vxcEL3xYbS0BAQS19320L1paSpQ2yYn9p3YCPtirVNslvzeSy38VMxTM+lNLx+SVlNUWB5GWNCbl4pMQzQlBElpIynzcbc8dadkK8tD++LHliRDvXAjimEFBuq9PDRWZrMSUqnoHBuhfN0hqwV4uhL66H08XhJ0eX0cElBHrS+d4lOMGm5ob7h/30eusyDXuPLlDmtvF0SQmuXaH203dS8mzIA+D7xXmnad3zSHRv0miov9Uybn2fKCsN8kmqhbIGKB3ZyFsJ0MBpqPcQtyhTjRm17ykwzZabK0wp5gxbgUN4mbeBl8WvCFQ0CDycgZT6TsjdTKo+0mLcmI5RvFO9NzEvbZhOY1Pls3X8uKr0tW7Jn8fI7HWvPPnurPfvsrX07k8/r932icbwsbpeSrjVEbl4pMUwVY5FxE8S4eHhU/i0FBd6vpOc6N1OlhZDpjyss/FpolKbKEpWmPWWuAww+o8O9rn7E9pMfaZ2/T+9B03MFTZrw8eW75H9ncdF9boIrorQ+2kZJgaX9wCeJ1ANLLTSUdh4X2rG5btLIvdCcNnqN9iFtV2/30k5oIi0ZOCRaJAndZsLs8ufnmbJC2PAx6tbtAirvwE7OQpgORsNYdvxrawFvWge30vJy+ctzfCYcM4CR9OI7OHXGs9Q28LL4tWHk5pcSQyPEblWKLWJcu2KOwgKhvKO8P0cDYCuKbNMJcMy268FFXCh96BqfRvE0AzHs7lfIhsXjY2JWspk1RG5eKTHUY4zWoHHxb2MaEtswLh6uVWC5rGzatiYDWEzeSvMsLn/pKiiq+9Ddx9vK4eZIeq5zM1WYMLJ+f2j0sz3SY4qYFEfz0SMbLfubAoXy8TzSDaXx/D8FVa54e4T8FeU6RgOnNVSm1Ec+f2pcSNpI0qdOckntkWgnYUiBpQYA6VmQJrVSujZLMl3+7PxTWBgPJi08U2aEddcTFKXgBhcpdbdRyEJCsW0bpTKoe6WuDiFdZ2FhWICmuOomg9z8UmLoI+XRiykefHGMVFZbHm5CRx1C4mtck1+vuHpFtokCS6cwsXamTENoWdSuT6+FbM3+2uJiVYGVphl0UVdo2uHPJVt5A+TmlRJDQq8lJEtkqIkqsDEaEtswNQU29qCn0N9kAAvpQSE6+BxamjdL5YwyqKYj6bnOzVRpQZJ4KZZ+6Tq3SHAPbM86059geisEL6vuhtI0lJElpYjTxiUI+d9X1nhb+DvBtG2UZqn9IdqsHXhX6bWFhSrNfPKa6v2S7pE0s/F94Mul65I6HdtZvdr1yeKitca4dnov9eJidTcKa6v9y2mRGL1ulsDLyc0v5YXxYNLCs4mwic0Ya+I6vVUDjcum1xLqCcbHymhLhyT0YnSy60EBOh1hOUShhjAP81vCh00fVzf8xx7XOh6WbE1t6AjZdqwNv4XU6XTsUU//hbFNfNt4YLkiGDIqUIVSmpKE7NzcjssVXBpcXKenxFanFgMFt5on1s906XJL5OaVEkM9Ujp90kbkFDRh6EAa9cCS6131wE420BGPdl7d6O2PKRMxMpJXFNhQXsmsGUoj1Sedc1p8HJFWogLL0gyVIfVFXbs4TZJlJ1ReSKGXEJrwSjOUgNGg3ydcOnIpS8sLPTuxelPusyqwUqjvN4+Y100aA4Q0M+OBbVIGvRYSIqnlSUKtDR302MSIRWe5XoAuLLg+8Zuw0ZfmuLFx8kI0N7+UGPqQRESKqOOIPa51PCyJmzZ0pIhjeu5pAzpjnfy28cCmtI0rrpIopUosncPScrjiOqz4dnqhWjYP0nRsAjaq3LxSYpgqJqrAjgFTU2BnBAXer6TnOjdTpYWQouQ7PGYSpqN4yLtH0/VuZF9JbDrJi1lBpPIo7XRSyC0ePSnSfweWt40ralyR4/TETLYS7bQuujNz6DptD6eHK5NUafH/pZkE3yG5l6ezuGg7xgykrLcY+Ti+GwWlISY9QwqTNIOr0p2fZ8oKMqS+5+b/1HzkWoGDcQV9+trO3MY145vAzDGpbH6P/T2TZr+0LD4bnhxy80uJwVpbL+7qroXOucj0PBIqO+RQkOqhdKTSw/PRa51Ox/7EQa8MelKlc+k/T/+msx6VGxuhi/cDbaf0P3SNinUpja/Li1N63ulYu7Aw+A4sdeD4o7RITJru8La2RG5eKTFMFbMgg1WBHaDA+5X0XOdmqnTmk0ZsZlbsdm1VmaNp+MQnoshu27bNbuulu67THUoi5qM0Sd5KRnM/W+okvtcuSpuoqJMy+qdcgoW8nAFah5RkKb80uSRl3b7YEeOpItztknKY8ty/RtvaUxz7fcLCdZ2u+Dz4R+iKxWq/DN3f0CQ5ZDAZ3IPc/FJakCExVMwDG8tHrm3bts1u27YtXkZG9OlrO1MbwwxvrOU0LVuYUW/bts1uO/dceamSemCzh5AtNNXWJOWn51zseB5JHYJpHan1xEBFHse5526zwLahCSxgg+fSfyl9ZFpSaUuo30LxUpv4uWQ/kmzQ9Dj4v60XbCVI0wZpyiLRy/ugAbLzS4Fhqsgug2NWNevoe99ffrnPe/zTOKFP5cQU2G+f9S65PslaRK/HFEfpVUIaT9samk9JdbO0/fvlLVSLi8OvCkpl8mshq19zJD3XuZmqOfNRBbCn9FQGQTJyVpQefgNJOZU+9vmEkVUSMEMKaa9ASXnsdntKEyUL1QK73V6cdON9OSSPV7B9uR5XLHargoC21+fvKYb9fiK08z7odGylD6V+e//C4HqnMyjf/+8rotSyDvSVW8C6MpjC+oBZ6N9bT3+3O0jb6Vh7Oxb7dTxgyC7NRIr6Mvv3kbbf2mp/kf4eQr3ilZtfSgv1qFN42sLfYLppUl150stvqfXw+kKDvCSYuIDhCltEAIlCI1SuR8jtEasnVBf9HxNooTJ9PG0zXT0hlSPlGQ9y80txwY+N3H6c8ij0ht+heOko1SFByseVa4kenoaX4c+96JBYBLD2TWc92tgDyz2vkgc2pHjzdkoswRVC6R7QNnHFkt4nZiOuXKdl+XdbaRwP9B1YawdvCfC8Hnx+zNuSgOz8UmCYLyRYvuh3YC1Q4UV+Horj18X6uFUoZHWK0Svlk9LU5Y+l5enqLGKhazFrVDqSnuvcTJUUuKJE+4UPwFT5A2xVmaTF9JQgquP0ew6b7ebNm621wx7YkJLDhV1I8PB4LlClNP22da3dvHmzfcELNg/VIwk+Ss91nW5FAFnr+qDffqIUD9HQ7VaEJk0jTSD6mz6Q8r23kwtIn+a6ziCt/z90j0h5Pty+2LHAZgtstl107e1YHOTtBdrWLrp9wemVfN9X/hialEnnAWTnmcJCQo9FkjUcBDdvHvBvUGg0oSWl/rqBn5SxGbCbpQEsJOzqBAo/l+qOCSLpf4pQrKuXlyXl6WHz5s2uT+qCREvsejvk5pfiAldErR0eC2OPRSwtBzDg4brxNsbWUh2SvJLyeZql8s88c7Pd+N6rGy8xBGz0+lVbHkzqp9ibU/S/dA+4klo3b+FsxZVKf33z5p4M7lav0fSS4kyDRJ8Ul4Ds/FJgmCoqMjgHYkZS6+ijPDzvHtj+/VIP7PgDHfz4faDvZPhz3q8hQeYHSL5Jrd8SnpZBy5fiQ1ZEOoh7yyOlPSRkeN6BsOlYoDPUD3xPGKlsrqCFFfjhvqObOPi8fUW1KwtGwPGA3xBYajsw6H8axzeNWL26mrZ6zfVJSDDSPNxIwF/n5W2UBHoCsvNMYaHaiRR8cO/97yflA2KkLG9sqbzP4R866hGtG1T5J24C6SvRlCG559DT0UPlPXb6chgRUtd1ukMe2H6c1A6hH+9fWBymzfctF4ghIdQ7im3l/+nLb7T/pTbR8XVx0e0kvrBg719YdCsyen340CryAl2vnv6rAb0Cbl/sDPfHaMjNLyUGURGlCM1bEtm3ci31naxYPXWPQ2jeF5rnDR7zdu/PcY9ryjt0qf1dd71mqAiWGZpfU5Z3we1C7ON4WXQ+EpLx3A7FZbFEXwC5eaXEMFUU+E5lBfoObBUF3q+k5zo3UyUFSTnqtzJwnmIEkJRMFz/YQCJk1Q05A/h/rhzR+jitEt28HC8oYv0gKVtUyeTxdZZcrvDyOqV2SNdC90lKJ8XxPhuk6UQV2JhgpEcqUKW6rJUNJAKy80xhodrZlZ7C8H9gEB2yGAjxbjRjmziFGDiGVCtFKJk0SJD/KfQFuypQphgXKzuxjcGqAvetltlY3/TvGdhO4iHm5eU0b0oKcvNLiWGM9oF6FDih6mMSk9+2E+Bp3pNQfdTgwGWj5HigMpTa+Zhtq2/wp/lDNDDk5pUSQ3NrSNOHi6Tv8++oD2gTK02DNJ3FxekpsCEaU5SUlPJS7mvo2LNAVYzqnim9J5anb0pvCobLS3quczNVMvPFPOEhRa0OVCmh8MzHFUpOB62THyUlcWCtrJ5zJZWC18Npo3VyhVyiUbpW11ch+mhfS0qv5wF/nXtg6abC9LpfuUBXMfj/9HzVKl9OxwKdvqcXGHhsfaB0UI87V1YX2OuzVInlBoBIv+Xml9KCQ52QJA9zPzpl8KdRXeaBbTLYN0kTSybNtCThLhXSOw92VZ3QI3H09YehshsKoKHkoQHQM5gw86x4kCUP7OKiva7THXhYu93BRmu8XeR8ApP43PxSYqigbZ+nKCFUzqWWVxefMgyk5pmUAhvbhThEc2i+EDvWXZNWCPJ5lU/n5y8LCwPDOqUrJGel50DKEzM6R5CbV0oM8Y6rM4amQDLSjmpdTJ2cNkzTAaanwKYY4Zv0UxOjfIwh/RHs85whQ7R0HBeGy0t6rnMzVTrz9SA5LviKP3+dK2rWps3/qDWRp0nhc3rf+UDtr9F2xFbycfjlzbQ8ToekCHN6QkJEAhdCoeu0LCoMqZJLj/xTuzFni8RPA2W1Y+s8sFR4hsrhS5qo0kvbz/tS6jINMv/yfqt7BpvGWxuf/NbxV13ZsTSp5XFvBU2Tqm/XKQChdE3obktPAx27fx7y4ND/dfdljIpsbn4pMVQ7qBfT9FlLmf+4sXiwJDX2DIUUOEne0TSSLAvl4edega1bEtw0NJmrh/hEkrP8KMVx2efr4/F8HjMoo9OXwdSOxeulr+zUtYkr26njgM3PKyWGZgN9XfqaMtQDm0BjymQkpbyU+xo6qgd2esKTDrbUKij1BR00K70COT297pmPe0dDA6pUB6XB2uFXwmg76oRwtU2DyTl9fc6fS/3C66BCiYKXRdtDaeB9ICnOdcojF6A+D+8bSqv3jtLyqfCMvVMjKamULu/ZpWmsHTiTKD2cl/myYsAu2/w8U1KogBsoeFwobUq8tXEFVqq7SdmxNKnl0VUUPE3KeFVHY6yNTehuS09dH0tl8j5p8ozU0dYCufmlxFCBJKOayNq6OZd7FjrBckJGxDol1MelyN3QuVdggfimTJP0wIb6RFJWudzl12iekAzn8wUqT9358KtNdWXzNo0RuXmlxDBVpK6gyIVJrKLQd2DHiqTnOjdTNWI+rlzQAdiDK1QhAcfjqLBaWVmxL37xChugq+Xyc0m48IGb1hFSjiX6aX3nn79iV1ZWhqypklDi7aP1h5Ra2j5OF+2HkMXWp+XX/TldvusVUm/4CQlcrmD6o1cugZVekMuQQkzZ5fdQipfSsP+5eaakUIFkTU8xUqbEW+v4d2VlpTZfm7JjdKeW5+mTjGFchoxCIy+bXoulidWTkq6uT6QyaZ/48cNfSzX6jnEynJtfSgzW2vjzID1rKfdESrOysmLPP38leD1Eh2Rg5elCaaWypTatrKzYSz9+49g9sHUT4BgfS23hRlc+zlDe8jK42x3swcYDz0+dM+ef7+ZMPt6Hbte9zuPh80irzlLHmgTk5pUSw1QRk8ElwPOwKrAOBd6vpOc6N1MlBaoQcQXWKzPUm8kVOA9fDh28uReUL+3l1kx/TVIS+X9aNi2Ll0fLpEozr4sO9FKbfTqqJNK2cbokBdmno++C8jKo8sfvh6QYSnG8zbwd0m7DTQPdAXn1avkZoYowFfZU0FJPt+Sl5tehHlgehgAxtnxwusfRDmnsGCekMifRjnEhYBCaKgkaZB6W7kVIDobS87yxNLTskIOAliHJu1BdPK2kQNFyaZn0G5KTnADH+tbH8VcN6RyGGn15HC1Tig/957RI8pzLfd5/ofvF5yQx40VEwc3NKyUGuSNHsfq1yZtw81rVXWflEq6Pm4cbLyGeRLuaWgwlSxi1TtFr1JLMLWF19TRH0nOdm6nSiIQdGhitp95Wr0nKIR08peCv+bybNm2yJ520qX8veVpquaR1SIM0HYTpoMyFo39mqHJK8/j/J520yQKbhvLygZ++XyotVQ4JHUnB430u5aH9FFI++dLl0HWqPNO4sFLr+iRF6aX10v7l7Q0JzxCYYM7OM4WFIYwqv2LYtGmT3bRp00TKHoeXgNNXI2dHRp0MnFS9TUD7hBvrMtCWm1+KC7F5pyRrpWsSQuOsfx4kmRUrQ1KWQnVJsjlWrk+3adMmu/6iT09FgY31LaVbUnS5AT5krJYM/9woS2W8pBSfccZgXsKD31CRG/N9Xkn2cpkt9Unk2crOLwUGuSPrGDSGSN6gDE64ea3qjj0owvVJ8HDjTZwm0K6kuoT8mwB3vyTG4/csNJmO1dMcSc91bqZKIxJVT5jvF7qEhQ6u/BOMXMmiXjZ+H1wdg/exaN1cSHAllgoOLjQ8uCeW1s0FDy13MMB37MJCZygvPZeWJ3MhR/vO55F2DAbk77T6ZUb8OlUiqSJKlXP6CUyav2kYlN+xqZ/R4c8G7zPeZo9UQ1jvmJ1nCgvRfhw3Op3BBjAlgr5vkltxHAfG0YbC3sHJzS9FBADnALjBBdcxoXEwZoSPPR+ha1QGD+Lqy4gZg3j+uuX6fJnrQLmb3md06pwbIcXbH/ncR1JEubyjcyOubErxLq5TkcHSnIHX6cvknlpOZ6ztAWTnnQKD3JGjDN6RvMHxPOHmtao7dYLWwyR4eJY9sJ3FRXe/qDeNp1cPbLsgWUFDAzcfHHk8VwwlzyIVnnzA9UgVHDReopG2h9PiUVUcO9ZvbhFqMy1Loj+mIHPBKAke2vZQGh5P+zuWNib8wqGTrMDyNlKjCH2+pL7giEzqsvNM7kAnv5dddtlQn00Sjnc7k6+oJahwn0Z/TBrjaIMqsGWHkFez32GRZ6DN8+Gfh3EaeELyumn6aSqwdbRROSVdi8lAf6RGbJpPUjaluZSXwXQTJ1oel6d0zivJ2Nj8PLV7NAyFqaKw8XwIU1VgZwAF3q+k53o/zAA2bHDHbndwvnEjsLhYTdftAp2OCzRvpzPI64/+/5Yt1bzdLnDHHdW03W61bgBYXnb1Ly1VywNcfKczSOvppO3odt25D75Mfh1wNC4uuriTT5bb7eteWHB9s7Q06Cden28zpWfjxmq/bdzojr4/P/KRQb3Lyy7Ot31hYVAvLYPC1+X7w5fvQfPdeeeg3RJWr5bjU7Bqlavb982WLYN6Oh1gzRrXVkqz70MOH8fbogCstZdba0+x1p5yzjnn9OM5H00KnEdKhfRcTQJ0nJH6nsc1uT91bQiVNelnQDE+cBnMEXsGqCxrW+84wGmUaKb1cVk2SjsoPvzBA2rT1PGqp53OMzw8nV7u+3kNPb/4YjdG+rxUnnc6Tp57Oe/lYqfj8iwsVOdUPr1Ps3q1O/p5lKcJcHUsLw+3j7ZJ6gfFmJAy+IfipGuh/6G4pje0Lv2o18eNJv0W65sNG6rKRYoQpenpNa5oSOXdcceAYdesGZTnQ4jGGG3TQKqmmzkMgXvFpCW13GsoWfO4J9IdB9bfOi9rjAYpD1+6Sq/RMnic/8+/A8uP3HMp1ce9qNyjyr2jPG+dVTfdcyrnDwW6BLxaR8eGPLD+/RuguqTZh9DyZe7FjkE9sM35d5KexwKtiRXkoE8aa/j12PmoddfFF3bPcvNLiaGPFK9Y6PWaVI9ak+ehxfgcTCPJRAonM0bz3gDy53eoByeVVyUPLKed931oHtBU9vsynSzu9L7HXi2HH2PjSp2XvwFy80qJodczNQ9ULE66Fvjf599RBUtd+pbXJ+aBbdJvsb4JKRF1ZUrXONMKaTuA7fBBpy5fbJAaHUnP9Ux5YOk5V/6919FbGalXzJ9LRg9qVfX/vfeGe964dZZ7enmdHKE4Wq4vQzJ6bNgw8E7y9vjzTsd5FL2FlJYdspJzD6Jvt/ckA4M+oV7mhQW5PO8xplZa70Gm8GVKXltfNvW2ek8p9ZrW4cc/Hvx/4IHh674/KQ3eMr1lS5pxSa3EzTEtz6PCga7SSBmbxnl/2njtFOWBy5oYpFU+Pi+Nj5VDPXgp9ITK4nXyMpaWBmkonV4m0LK73YHMoAh5VT/8wQP61/zx7LfuraSn//lcJMYjXt6H5LfU9z7Oe0npHIfPm6iHlntdgUG/+Xv94x9X5078KPWnNPfhcy6Vr2NEk4lpShmh/6G4poN+XfpRr48bTfot1jd+qURduTReWjJC64rdq8XFAfPSiTlXdKQycgryVE03ZwgZFiQvIn+nQvJQDqn6rIwdO3bYHTt2iAYN7tGk5Urvp1NPXoj2EJ3UA+iNWevW7bDr1u3oG0ak91Ykw5f0ri9NSzdtCllMJQttXUh5j4aH5h7cHb3QNJ8cfJ9T72xDZOeZwkIfTd9pqtvDQMKOHY5HQvli+Se1twSN9+NLal1t9khouvdDynUpPuW8Lq7TGdwzPybxNON8FzIBufmlxCDKrhCovKHgcjhUjpO/wzxMwenh8tlfq5P9Uj5JZvk069btsH/x199K8qr6vDyN9P+qLQ+K8jbWHp+G7+sgtZenp4H3I5XBsa8H+Dzr1u2w++03kMGxlU38v3R/QqvZEpGbV0oMU4WXcaVix45hHp7nd2ALvF9Jz3Vupkojkg1s/JxuDhAaEKX0NM6n5zsPUsR2LQxNCLlSRBVFvkSVCyEuMKhg6nQGS2J9Hio0fFqax9fLd1/mwoJuouS/pUp3GF61angzptgOxJ42icaYIjmOb8FKwS8rXlwMf7Sd0hjaaC0yucjOM4WFPppORkKTzlHyxfK3mCxF88fqTK0rlK5pO/jkuOl1KT7lvC5OmhiH0kwJufmlxNDKoFBnCBnFmCTNA2LPo1ReSEHiGw3xsvk3JM9+62PipPbstz7Wv0bT0P/AQIHlfRxrjzTX4fMNnldSDHleadmvlJfXE5K3MSM2d0LwdkrztQTk5pUSg4Jhat+BVbRB0nOdm6mSAh/Y6GDq47gV0f8XeybAzl5xufLKK+2VV16ZnC8EqqRR2mMCgB65ldTT9oY3XFlRsGh9krCgdcesniGhExI4/ki/OVsnwKgSS72/XqGU8lNlXQ5XWuDKmjSjB99n9JNAkkXcFsAzhYU+UiasfBJX5+Xj1zyPpHhDQzRIdddNpGLeRhrvxxfJWCbVkfL5EJ6+zuBS94WDWNk8rXSkZUqGP254vPLKK+3zn39l3xDGDUepRqQxITe/lBhagT8Xqa85ex5pquA2GSv89abK+JVXXmn/qHvF2Ca+XpnlCiynPzYexXhX6ntJQae8So+eJ7mBnY9pRx454F/Ow7wOHug1TouEmrE8N6+UGJoNoE2tVSxNaA6dkjdKZ+/YT9JSAIybh+sU2CiZgYuhPNd15AvXdbpD/TSUljFkt+virnzDGwbj7erV7tjt2m0LLv11na69f2Gxmq95k5og6bnOzVRJgQ9q/JtlfMmsNFBzRUMa6L2ytLgof0cyJEAkQeDPfZmhAdzXS8vhSmVVmAw2caLfcrW26uWk9XDaaBz9JisXVl4QrV5dLYuWT+n1tNL0XEkNBVp2SFmmcQsLNG3Huo233PmqVfG6uLLsy6LPkq/HH7lRQDJEqAIbEZ4J4IYRHp9ahueRJvlCZXFerEufEi/RF2p7Sr1t0sfSpFxrco9S+sX1RyeFt5LrHQG5+aW40FZZpPePjud15fnNCkP3tyk9Kf/rMFAEO/aop/9Ca0U1NgGu46Om+kaoz3lZIX7mMpjH+XM6pkmy3vXbcHydfB6UPyiD0hLor+z8UmCQOytlcG4hUPzzkIQUYcJueD9JSwHQmdQmTolNTLkYzBO7Z5wxpL4lAXBxHWAw3pL4UGg7F2qApOc6N1OlESn0oVfG+CDJB1dpIKX5pMHeK0QekpfT/+dHadLL65ImZxRS/oEy7JRrSSGXypXbVy07Jli4AOP97s+9wJGUTl4P9cDSunn/0L6g5fK63O6HnWThyJVUqR98nbTd3hCxalW1H7gBwxbAM4WFJISs6k0nmdTIMwpCBqtY+pR4ib5Q21Pq5UiZN8TKTLnW5B6l9Asf1yRj4Dj6JhG5+aW4QGUSl390LORpJGMuz9vvdNCyB8+DhNgcl59LcwIpvu6ZHsiBjgU6/YnryS96PKig0uXDgPyebMwDy+uX+o23nf/ncoyXZe3wygr+6hG9xucVdF4SkvWDdNV4KvslYzenlbc/cM+y80uBodkA2tTKw9I0UmBThAm74f0kLQXAtBXYKJmBi6E8k/DAdhYXB3MS9cCON9CBzStL1g4GNv8eI43r9wIGg6PPzwdKOlnyHlhgMJniQo6W6eNoXfQZ8nXSgZ0rgJJA4ILfD/qeNmngp5OEbnew7JbT59uTorj6/3TZLBcyXBEc9KNcDr1nvF5Ps/fi+ndwudJKg+8T+skcKfAypCXL0v2gzwF/vgKMmp1nCgvW2vTlgxQxxS4Unyo8U5WucSjCFE2Ee1NB0VTJa9K2UfqhLi9V6kdtUxuw8nLzS3GByyffZ3x852lo/9J7G1KQBnKi05fBEiT+5M9OSOGKKWIUUj4vb6gH1iumkoJKrztep8sAACAASURBVIU8sD5N3Tt0VHZzRY4aB6R+5nOZUNulo9QH/v/AGN2xISMyXdklXferwCRZ7evhho8Qetez80uBYapopMBmwLQV2NJR4P1Keq5zM1Uy84UGb65gcmEaUnqosBwecIetiXywl65RIULTWVtVXKkw50qRNCGo0jmwTHM6eH6qaPprtP8kRZSmp3RJfUXzLixYa8xwXu5lnUzo2FWrOklp+fJiyagR6jMvWL3AjSA3v5QW+n3YFJznQ9cpUgfjGD18TBkFPH8TYZE4YYumT23nqLSMktf3CR1zYnlHvSc15eXmlxKD2FdUnlHwc87HXN7RfFwG16GOV0NlhGjktAzHVSe/qR7YuqXFKRNgbtAL9WOsv0NtTS2Xy0R/v+hrPNK8istdacUTdwCE2h9C73puXikxTBz0/hWoEFVQogI7LkNzqNtjfEXvl+S8yYCk5zo3UzViPskK57101g57CrnHkHreqMLi80jCkyun3a6rjyo9nh6u+FDhztNQbzJ9SLhyaO3A4+ziBu/nhoQAL0NaHsQVaF4WV2zpsl++BJgLq5Ci6/supFTy91J9Pq8YxxTYuiXE/l1e3jYq4LmFmD5rvLwa5OaXogL3BoQQm7BIHpRQnpjwrMvbNF0KeH5OX2zyX3etTnFoUn7i5LAV6ujyY67UJn4rY5PctjSyfNl5psAg9lVqf8eUE+keUo98rB7+vKSUH6MxJW5xcfyT39AEuAlPUj7h/dB0nJDK4uX5eYGbT3XswoLLtHq1S+PnNn4VlU9Pjf4+vy+TbxZVh8B9zs0rJYaJgxs0VIFthkkbmvmcloLeL26wGrexOBFJz3VupmrEfNINkJQma6tKk+RVlPL4fLt27bLArkp8TEmjgyithy+T4kojVzxpPq5EDa472mhbpPZxhXi4nEG9VKGnyirvn1j7ATu0hJdvyOTroJ/jqVM6Uz6l85SnDO5XaqBKeEjhpv1EFfwEwZqbX4oKqQPguNLt2rXL7tq1a6Q6JglOH6cpVZC1bUudgJsWaH1+zK1Lx8+b9F0T0jQMBRFNlMOUe+PLe/vb4zxSVyaX3aF6mp53u+5Z/fdn3DUVBZbPD2LtiSnyXn7xtHxe4tNK5dMjn9/s2rXLvv3tu4ZkpzSXkc75NeoAiCEwFuTmlRLDxEGfpZgMLgG7du2yH7v69qIU2FSDeh3aeGDp/aozkk8JSc91bqZKZj5poPVKER3s6PuxXIkLpePLfnk9/Jr0vVVJCHCBQPPQbeZDCii1ckrXqSJGH07edirEqNLGFWrpnVW+JMifhz5Zwz+RQ9/DrVMqJY8rV2JTliNzj63PQz29IXoo/fS+eoS8COw52WDz80wxwT9HtP9i/SqhjUelrpyU82mgCQ2xfkj1XDTxykwSUv0pykbIO8TPR2hPdp4pMMgdFbwynC7l+eSGQw/pPscUnJCRQzLgcvpC5/w4DQVWWnXGaeOGZto3oXmDpDTyPLx8eqT/+fyG10O/V0+v+/aF5gd1Ro+Q0m7z80qJQcGg34EtGknPdW6mSmY+OgCGBnV/5EoGVWK4AOOCzKW7wl5xxRW1k6M6zyf/Lw3SPK1EZzXtFfb0068QhRHPI/VRSHjxJcOScsf7MsU7GgoxgRWL53S7cEUvpNclBa4cxyY7vG8pevG5eaakELx3rM+CqLtOccUVjn9TwMttUk9bNKGvCeqey1D6EuD7JIWmKbQvN7+UGESkGgkkZVCCv3b66XEeoeNzrJxQ3dLYzvNIZQDuWX3Jyy/P6oHlYyed81CZR+OkV2e4MkrnV7x8erS2Wt4VV7h5ScjALM1NaPk0Ha0j9R7zSxrS+Lct6vh+nDIuVNcoBtcrrrjC/t67Phjlx7rPXo1DgU1pQ6pBu2kZNG5Sc5JQ3QlIeq5zM1Uy8/H3Nn3HeE9q9T1R0gsYVjZD1kMf779BJ3W8r8fnCXmWaF7ujeUeUO4B5sufaVr+fTzu4QwJLk+j9N4qfYeYeod9OXSX55CAWrVKVmz9smLumaXlrF5d9eh6T+nq1fKmSz6vL9tt4BTfhZh6d/37sCEvMhfy/FjnSYB6YEX+9X0m8Uiq1zAFTd6/yeGFnNT7QSkeLil9CeDvPMYwhfbl5pcSQytIPM55n1+zdvBOdEq5Ta7XGaXrynAyUn5/LraJU8pEWfqMDt+/wv/nRnqpHfRdU55WykPnGzyelzGcb8C/vgz+fXWJ9k5nML+g78nG6uTtFJCbV0oMY0WdcXCcMq7GUdAKKe/AAs1WWbRRYEc12I5izKVxk3xnueV9SnquczNVMvNRhUJSZqnyxi2nVKnjih5XaqkCG7LA0jjJoiuloXVzBZPTScsZVkY7PfrCy3u5Qiwp7lyJ5PTTeP6f5uMKbcoSX8lzy+uOKZeUJhc6tsl3YGMKrv9Pha40iYhBFdhmwlOaJNWlj8V7/p0mxqFgp066x4m2ZdcpIJKSEsvPjYZ80ivlmSBy80uJYQhc2eGg8sza4f80L5eZTv52woWTtLHzWJ62Q0Ro8itNeFMmwT7NVVseHJrs8fmN/y8Z6nn7uOznfSPdA3nO4dtdVXSpoknnTLH5htQuKUjpOFSBHY1/R0Edn41TIZqEXExRYOfJAztJBbblfUp6rnMzVTLzUWHJFR1/nSqKtPO44kMHYUnZ9Dv9SgOu32GPT6pouXRXPU9jSPnjii19P5NvguSsmh3rv48HDOjhihgtl3pcudLq6aX9w9879d9klcqn322l94WfGxPfTZh+HzamaPIynIe201iB9fVwKzU3InCkMGMvX26eKSnU9lesz6X0sXj3OYdOfUFjRArdHiFhUdeuSaBt2bGJtj/Gyh7OXzUaSs/EJPuBk6dB5mFJ4ZGu+etUHtO0MQXMyYNhD6ykrIaeQyk9pyuWls8h6P9pemC5MmjtsKGaz3kov9AVZrQsWg7NR+cfPF9M2fRzJj4P4YHORTwNfE8KqhhTmkN9IyA3r5QYpopJKkTjQIoCO45XAGYFBd6vpOc6N1MlBUnYhDyoMSEqDcZSev8RdUnAScKPl0ePoSAJCZpHEgAu7YA2vjQ5VCdXXnlfUBrqhFUsX74w6JO6wDfPopMAvtyJ8rNkqAgB6oHlIYpxeGBpGdQDOw7rZUqe2DPB47mwqGt7GyvrKJbZlGtc1kne0pSy6T3zn9Hx8fz1kBR6RrHKE+TmlxKD6xjCzVx+cpnIlZBQXjrmDmT1MI9IMpc/RzF6eFmSgVo65/8nMfkNKbAxxZSeS7wfSsNlvM8jGW9j85Tq5pIdSw3r0hyEr7wKzdV4/ZS2kKxgZeXmlRLDVFGgQlSBKrBVFHi/kp7r3EyVRiTCAzQf3Lgg4steQlbLwUTK3czVqzviYMkHYC4sqHDx1kX6rVdPBz3Ssvg3Vv21wXsl1Q+8c+8uVcRoebweKtzoOe8r2gZqKfXx/N1Xn9d7OOk7rMZUrwHOU9vtVr/RurDgzlevHryzSsvi79Ma04kqsIN08nV+X/mzJP0PPZM9ZOeZwsLEQfmeDsahSWzdtTZ5QteGx6SqsGhDR1094yizSVvb1ufzdYQlo5LiU1fOmPoyN7+UGKy1cSNJnQEmpFzycdja4dcAQgqP9L+OHula7Jz/n6QCG3t+JeXOn0v5JAWf56ceWmsH535lG7/GDUX0fnmPOZ2/cCWaytbYGMLpjeXjeW1+XikxNDYOj2IMpDJuVGPqmIySFYybh09+0eNDCuw4jMh110PG/KbodAb8GyszwXg0LiQ917mZKo1IYaCTBjj/31pZ4aTpuHeN5t+zZ48F9gzF0wGUKqyUDmlZsAdfzsPT8HjJ+rl+/Z4efdV4rqjyvuFlSn0WSxdT9qTrTfKH0qaHPb3QLr+/N1wx5u2hz4rUzwRT4YsZChMHHQ/27HE8wuNjedrUk3qNx1P62tJRV884yhxFAWhax549e+z69XvEaymGYfXA5uNhaVJTB/6s8jJCPFKXL0ZHE/6ITeL27Nlj3/Bb97We7IaWFUse2JR2UAO3lC5FOad9GauPw9dL7xddwcTvkV9N4V9LouXTpcM+Py0jpa095OaVEkPUONLvOMj/m4I+D6nlhNKNQkcIe/bssZ/+wvfGpsACdkiBHbXdKdcTjDlJ8DpPXZn82La+BCQ917mZKinQwcv2qOZKBR+sqWLC3/v016X/dPClSoykZHElhiukXinykBQ4Wgb9L72ryq/7o4+nnlAPSTHzZYXed+GBWlMpHb5c/q4upZ3vJNztpm3QlPqJHmnH41Cg9dJ3YOkzQPuSerclowSdWLNJdnaeKSxkhTTZiU26J2UJ5uNYLN08o4D25+aXEkOQV6RJzaTA62gygWsy6aorF2j/HdhQ3lQF1kOS3alt4df4XMbHSXXy/KF7knqNyn0+L5Po4/MSOt/p5c3NKyWGVsaRcSC1nEnJ3RDG+R3YWffA0nyxMpsYDEdE0nOdm6mSmY8PZlzxC3UwVwI9+BJaeu3SSy+1r3vdpUODuh80qSXRK2wSXXwA5+9b8ne7aLk+PaXTWmuPP/5Se+mllw4p3z5fqJ/okbaHxnmauPLMBYxPS8sIKb6ScuqvSUoqV4BDiizt58MPv9QClyYpulzhpfdCUlR5O+reK/Jdq6HKv9PEpZc6HvGQJlGxSXebiWAKBs9Ulb5x1zOLoPesgPbn5pfigqTgWGsrY2DqZCplwuOfh5jxSSpLMio3+e8hef19uksvvdT+3Ev/tPWmTSe/6HExvV9CnDoh5HObkGFO2svB/5eM+LR83n7et36Ocvzxbs5EFUrplSYq46nTgCqk1EDMV63R/zQwmrPzS4FhquAyuDRceuml9px17x37KwCzigLvV9JznZupkplPGvwkrxhV3iQPGs0f2sTBb+Lky6FppHqkOH9ep1yHyufK3iBN9R1YSdmUlirzNkseZt7HvBwqdOi5v+6/+yYpryHhw9+VCeX19fDvwjpPqrtfIc9unWc2RGdIAaeby/B+UQvwaMJzVOuhtcPvmMYmwdJENtUSHDsP1ekmdPENE5paXJtcz4XQJNpfo/eMGvFGrZMrPBx0Ek3S5eaX4kJIsap73iRlss5A4eRPp/dedFqZ9BqXqXV1cbkt5ePjy0knv8ICaZ/N4UotTUP/X7XlwcoqqRjN/sgN8VRO8fbxdnEDLB+/pPkMzzMIHUv3oZDmNTGlU5L3fIWTNI/jBn5VYINhquAyODeGZc5sbeKUOicJxdeN06F3lulY07TMEZEml1ITTjsAOAfADQBuuOyyy1yLhAFQGrBpWimfNIhWB1z5I+r0JlLlLvT+Fd8gQTrywZcrjTyNV6654Am1KVY3vy4pbZwWSisXklzRk4QRFUSh/uB0UCaS7iHdNTolSAqy9N4rb4vUp4EJQnb+yR0k/k1B3YQzJV9b4dm0bp4+NFHk56MI9zoa2/bfpFHXN7RPxtUGadwK0cXSZeefEgLl4de//oZ4ZwcgKYUpSq9/HuqUZn5vm06yqHwJzQf4NT/5TfXAAjZ4HvLApjyzEv1UieXXebvqFF3eL5Is9IFuLikpldzZIM1TJPnOy5D6RWhfdt4pIbSVweNAaQosf25mTYENjQep8XUyNSR/A/IxqcwRkfSM749CYa29HMDl/hQAul13srwMLC25/90usGHDIJ//3+0Cl1wy+E9B8/N8ALC4CKxZM0wTTbO8PFw/vc7TxI4etCx+zcPTRdPxeim9noZOZ/hI8/n6NmwYLk+ipdsdlO3T83NKh8fy8nAdvvzFReCss1waf4+Wlqr96+M/8hGX9pJLXJ/s2AG88pXu2saNrqw773RHYNBvvrw1a1z4yleApz7VlUvvOe0PXxelARj0oT+n+eYdEv+moG3/jaPfm5Yh8W/KtVFQV06pz58fd4BhGuk16XyUOvk4z+FpSUk7b2jLwxQhGRODJFvqypSupdYlydwUGfiW8/YmxZ391r3Bc54+Js8lWjj9fC4Smh/F/kvlSmmk+dCaNQMZ77G4OJz2jjuGywnNnzg9HLF2zDPGwb/7CkqVi6kI0Z8a36T90jwmpAPkhrF2Jp7rqRK51JvJLNMRtBCUTFsuFNgnJjcBhUH5l6B0+nKgsD5R/h2G8nAPS0tLuPeBJ3D5R78w1nJ37X4Cp7/ykLGWOS0Udr+Uf4eh/EswCR5W/h0rknh4v0lToVAoFAqFQqFQKBQKxTgwEx5YY8w5veUQSkcPpdCidAyjJFpKQEn9UQotSscwSqGlFDpKQil9onQMoxRalI5yUUqflEIHUA4tSscwUmmZFQ/sObkJ6KEUOoByaFE6hlESLSWgpP4ohRalYxil0FIKHSWhlD5ROoZRCi1KR7kopU9KoQMohxalYxhJtMyKAqtQKBQKhUKhUCgUijmHKrAKhUKhUCgUCoVCoZgJzIoCW8S6bJRDB1AOLUrHMEqipQSU1B+l0KJ0DKMUWkqhoySU0idKxzBKoUXpKBel9EkpdADl0KJ0DCOJlpnYxEmhUCgUCoVCoVAoFIpZ8cAqFAqFQqFQKBQKhWLOoQqsQqFQKBQKhUKhUChmAqrAKhQKhUKhUCgUCoViJqAKrEKhUCgUCoVCoVAoZgKqwCoUCoVCoVAoFAqFYiagCqxCoVAoFAqFQqFQKGYCqsAqFAqFQqFQKBQKhWImoAqsQqFQKBQKhUKhUChmAqrAKhQKhUKhUCgUCoViJrB/bgJS8JrXvMb+4z/+49TqW1paAgAsLy9Prc5UlExbLhTYJyY3ASVB+beK0unLgcL6RPmXQXl4gKWlJTzw8BPY8L6/H2u5hx2yH175sweNtcxpobD7pfzLoPxbxSR4WPl3rEji4YkrsMaYVQBuAPBda+3rjTHPAnAlgMMBfB3Amdbax2Jl3HvvvZMmU6FQTAjKvwrFbEN5uIq9TwBHHbpqrGXu2v3EWMtTKDyUf4cxbh5W/p0+puGB/X0A3wbwtN75nwB4v7X2SmPMhwC8BcAHp0BHMs4777zcJARRMm25QPvkQx8CjjwSeOMbMxKkyIrSeaR0+nKg3yff+hbwsY8B73kPYNSRMq8omUfOO+883PDtH+Umoyj4+/Xkk8Db3w689a3ACSdkJkqRDSXzL6A8zCHer89+Frj7buD3fm/6BCXCWGsnV7gxxwD4KIB3A/hDAKcB2AXgGdbax40xLwOwwVr7K7FyTjnlFHvDDTdMjE7FvoPjjwcefhjYsQN4ylOykaEzbwLlX0Uy/tt/A7pd4J57gGc8IxcVyr8MysNVXP2lhybigT39lYeMtcxp4847gTVrHAtv2JCNDOVfBuXfYYybh/cF/u3jvvuAZz8bOPhg4K67clCQxMOT3sTpEgDvAPBk7/wIALuttY/3zu8C8FNSRmPMOcaYG4wxN+zatWvCZFaxc+dO7Ny5c6p1pqJk2nKB9sl99wHf/z7wuc9lJkqRDaXzSOn05UC/T+6/30XcfXdeghRZUTKP7Ny5E7t+kGVSVyz8/dq9251/+9t56VHkRcn8CygPcwzdr4svBh54wE2mn3wynDEzJraE2BjzegA/sNbeaIxZ8tFCUtEFbK29HMDlgLMeTYTIAM4880wARb3Q3EfJtOWC75Nrr13uC9APfAB405syEqXIhtJ5pHT6cqDfJ2vWuIi77wZ+7ufyEaTIipJ55Mwzz8S9DzyByz/6hdykFAN/vzZuXAbg3gRQzC9K5l9AeZijcr927gT+1/8CDjkEeOgh4N57gZ/8ybwEBjBJD+zLAfyqMeYOuE2bXgXnkT3UGOMV52MAqKldMRY88ABgrVvC9KUvATfdlJsihULRCOqBVShmFt6A/G//Bjz+eDytQqEoEBdf7CbS3a47v+eevPREMDEF1lr7x9baY6y1awCcAeCL1trfBHAdAL/FzpsBXD0pGhTzhfvuc8d164DVq4EPFrU1mEKhqIVnYlVgFYqZg7c/PfYYcNtteWlRKBQt8K1vAT//88BLX+rOZ1mBNcb8D2PM04wxBxhjrjXG3GuM+a0R6nwngD80xnwH7p3YD49QlkLRh5/7nnAC8LrXAVP87FnRmAAPKxSTgZ8Be6G5ezfwP//nXLtzlH8VswLvgQX0PVgP5V/FTOHhh93y4aOPduezrMACeLW19kEAr4fbdOk5AN7epBJr7bK19vW9/7dZa19irT3eWvvr1tofN6ZaoRDgFdjDDwde8Qrg9tuL5r1pYmQeViimAr6E+K//GnjHO4AvfjEfTfmh/KuYCVAFVt+D7UP5VzE7ePhht/vwDCiwKZs4HdA7vg7Ap6y195l9/Pt8b3vb23KTEETJtOWC7xOqwL785e7/9dcDb3hDJsLKwVzxcOk8Ujp9OdDvE7/zmldgb7nFHa+7Dnj1q6dPWBmYK/4FyuaRt73tbfjKv+o3JCn8/br2WuBpT3NBPbB9KP8WBuXhKir3yyuwBx4ILCzMvAL7eWPMCoAfAfhdY8xRAB6dLFl5cdppp+UmIYiSacsF3yd/9mfu/PDD3UZOT30qsHWrKrCYMx4unUdKpy8HTjvtNODRR4Ef9SYVXIEtdDfLKWGu+Bcom0dOO+00PLnwUG4yioK/X5/9LHDoocDznqceWIKR+NcYswrADQC+a619vTHmWXAbox4O4OsAzrTWPjYBulujZP4FlIc5KvfLK7CA88J+73t5iEpA7RJia+27ALwMwCnW2r0AHgFw+qQJy4mbb74ZN998c24yRJRMWy74PvEe2EMPBZ7yFODFL3Ye2HnHvPFw6TxSOn05cPPNN+Pmr33NnRx1lPv+3OOPA9/5jov72tfclv5ziHnjX6BsHrn55pvx3R235CajKPj7df/9Tv4+//nAykrRn5CcGsbAv78PgPqz/wTA+621JwC4H8BbxkXruFAy/wLKwxz9+2VtVYF9xjNm3gMLa+395P8eAHsmRlEBOPfccwGU+Q2rkmnLBd8nL3zhMhYWgP17T/XatcD73uecOgcemJHAAjBPPFw6j5ROXw6ce+65wCOPYBkATjwR2LLFWX6/8x3gBS8A/uVf3HKK17wmM6V5ME/8C5TNI+eee65+Q5LB3y9rl3HYYY6F9+xxn5RcXMxMXAFoy7/GmGMA/D8A3g23+amB+yTlf+ol+SiADQCK+uZCyfwLKA9z9O/XF77gDMfUA/vVr2akLI5JfgdWoZgq7rvPLR/2ePnLgb17gRtuyEeTQqFIxN697njSSe74ta+5ZcVvfjNwwAHuPViFQlEsdu8eeGABfQ92DLgEwDsAeF/2EQB2W2v9tux3AfipHIQp9kE8/LA7UgX2nnucZ7ZARBVY43DstIhRKEYBV2Bf9jJ33Lo1Dz0lQHlYMTPwn8rxCuyWLe74whe679IVas2fJJR/FbMEr8CeeKI7f+c7gb/4C+DHc/qtiVH41xjzegA/sNbeSKOFpKJ2YYw5xxhzgzHmhl27drUhQTFv2NNbGHDQQe549NFuCeODD+ajKYKoAmuttQCumhItCsVI4ArskUcCz33ufCuwysOKmYFXYH/mZ9zRK7AnnAAsLQE33lisIJ0UlH8VswSvwB5xBPCxjwFPPAGccw5w0UW5KcuDEfn35QB+1RhzB9ymTa+C88geaozxr/8dA+DuQN2XW2tPsdaectRRR7UkQTFXkDywQLHvwaYsIf6KMebFE6dEoRgRXIEFgBe9CLjppjz0FATlYUX58EuIn/c8YL/9gG98w20lfswx7oX2J55wcfMH5V9F8bDW2ZcOO8ydn3mmk72nnAJ86Ut5acuMVvxrrf1ja+0x1to1AM4A8EVr7W8CuA7AG3vJ3gzg6rFRqphvzJgCm7KJ0y8CeGvPCrQHbgmDtda+YJKE5cQFF1yQm4QgSqYtF3yf/MZvDCuwz3kO8KlPzf1GTnPFw6XzSOn05cAFF1zgXDa33urcNz/5k24Tp+OOc8qsF6T33puX0DyYK/4FyuaRCy64ANf/yyO5ySgKF1xwAR580Cmqhx46iDfGKbCf/KTbkXi/+dx1Zdz8+04AVxpjLgSwDcCHx0LlGFEy/wLKwxz9+7UPKrCvnTgVheHUU0/NTUIQJdOWC6eeeiqefNJ5YI84onrtOc9xluFbbx2sTJxDzBUPl84jpdOXA6eeeirw+c8DT3sasGoV8MxnOgX2hBNcgiOPdMf5VGDnin+Bsnnk1FNPxZ6nzOcnnUI49dRTcdtt7j9VYAGnwH7oQ04Ge3aeM4zMv9baZcBt0m6tvQ3AS0Ytc5IomX8B5WGO/v3atMkdZ0SBTfkO7J0AjgXwqt7/R1LyzTK2b9+O7du35yZDRMm05cL27duxdet2PPmk7IEFgFvm+JNf88bDpfNI6fTlwPbt27H9O98ZrD985jPd0c94vWVqDhXYeeNfoGwe2b59O2675V9yk1EUtm/fjq9+1d0vrsC+6EXueOONmEso/5YH5eEq+veLe2AXFtxrPIUqsLUeWGNMF8ApAJ4L4AoABwD4BNwL5vsk1q1bB6DMb1iVTFsurFu3Do8+CgDLQwqsn//+279Nm6pyMG88XDqPlE5fDqxbtw646SYs+49GcgX2wAPdzohzqMDOG/8CZfPIunXrcO8DT+Dn9RuSfaxbtw67dwPA8pACe9JJwOrV7nN2Z5yRg7q8UP4tD8rDVfTv13/qfV7YK7DGDD6lUyBSrED/AcCvovfhZWvt3QAOmSRRCkVT+P1fuAL7tKcBT3/6fCuwUB5WzAIefzzsgQXcMuIf/nD6dOWH8q+ieHgZ7FnY44ADgBe8YH49sFD+VcwKuAcWAJ7xDPc6T4FIUWAf620FbgHAGHPQZElSKJrDf4GDK7CAW0Y8z0uIoTysmAVQBfZ5z3NuG/9BScAtI55DDyyUfxUzAC+DuQcWcMuIv/51t5HTHEL5VzEb8ArsQeQRnXEP7GeMMZfBfXvqdwBcA+AvJkuWQtEMIQ8s4BTYOffAKg8rysfevQMF9td/Hbj9drcbsceRR86rAqv8qygeMQX2lFPc2AOVdQAAIABJREFUJ3ZuvXW6NBUC5V/FbODhh93rOqtWDeKOO84x7iPl7dqcsonT/wfgbwB8FsBzAPxXa+2fTZowhaIJ6jyw3/8+8MAD06WpFCgPK2YCjz8+YGD66RyPOVVglX8Vs4DHH3dsS1cfeszzRk7Kv4qZwZ49Ve8rALzqVcBjjwFbt+ahKYKUz+gAwE0ADoRbAnHT5MgpAxdddFFuEoIombZcuOiii/CRj7hlwvz9G2DwGt0ttzhL8Jxibni4dB4pnb4cuOi//lfgl35JZmCPOVVge5gb/gXK5pGLLroI/7S9PG9ETlx00UV473udA0f61qvfyOnv/m4+N3KC8m9RUB6uon+/Lrts2AL1C7/gXmTfvBn45V+ePnER1HpgjTG/DeCfAfwagDcC+Iox5uxJE5YTa9euxdq1a3OTIaJk2nJh7dq1OPjgtTj4YOApTxm+Pu+f0pk3Hi6dR0qnLwfWPve5WAvUK7APPDB4X2BOMG/8C5TNI2vXrsXzfubnc5NRFNauXYsDD1yLoxYeA371V92WwwQHHAD85/8MfOITwJ//eSYiM0H5tzwoD1fRv18PPzyswB50ELB2LXDNNXmIiyDFA/t2AD9rrf0hABhjjgBwPYC/miRhOXH99dcDQJEMWDJtuXD99ddjZQU4/HC5T447zu0GPsfvwc4VD5fOI6XTlwPXX3cdAGBtnQILuJ2In/GMKVBVDOaKf4GyeeT666/Hyr8+gqNeUR5tuXD99dfjttuAE37iaODznwdOPnloudOf/IkzIv/+7zuZ/NrXZiJ2+lD+LQzKw1X075ekwALO83rBBcCuXcBRR02ZujBSFNi7ADxEzh8CsHMy5JSB9evXAyjzG1Yl05YL69evx7/+K3Dsscvi9ac+FVhcnGsFdq54uHQeKZ2+HFj/3vcCAJZTFNh77503BXau+Bcom0fWr1+Pex94Ar/wCv2GpMf69evxzW8Cb3ruJS5CWO60ahXwyU8CL3wh8L73zZUCq/xbGJSHq+jfr717ZQX21FOdAvvFLwJvetOUqQsjRYH9LoCvGmOuhlu/fzqAfzbG/CEAWGvfN0H6FIok7N0rb+DkMec7ETfmYWPMsQA+BuAZAJ4EcLm19k+NMYcD+DSANQDuAPAfrbX3T6MRin0YsW3EPY44wh3n71uwKoMVxePxx4GjD9ztTgLC9qCD3J4wf/M3gLVuZdQcQPlXMRt4+OHqzv8ep5wCLCy492ALUmBTPqNzK4Cr0PuGFYCrAdwD9yFm/RizIgs+8QngzjsH53QDUwnPfS6wsuKE5hyiDQ8/DuBt1trnA3gpgPONMScCeBeAa621JwC4tneuUIwGv414qgd2vtBKBhtjjjXGXGeM+bYx5pvGmN/vxR9ujNlsjLmld4x0ukKRhscfB446oKfA3nJLUNi+6EXA/fcDd9wxPdoyQ+fQitlAaAnxqlXO8lTYe7C1Hlhr7cZpEKJQpOKrXwXOPNN9KvIzn3HOmx/9CHjWs8J5TjzR8ebOncBP//T0aC0BbXjYWnsPnJCFtfYhY8y3AfwUnPV4qZfsowCWAbxzLIQq5hc9BfYHew/DdZ8OGHnnVIEdQQZ7I9TXjTGHALjRGLMZwFlwRqiLjTHvgjNCKQ8rRsLjjwNHHtD7Vt1DDwE/+AHw9KcPpaOf1InJ7H0FOodWzAxCCiwAvOQlwOc+53j7kDLsLikeWIWiKLznPe74uc8Bd98N3HOPM/aeHdnX76ST3PFb35o8ffsajDFrAPwsgK8CeHpPufVKrrDeRKFoiN4S4r/67ALOOMPNfYfglxDPmQLbFtbae6y1X+/9fwgANUJ9tJfsowD+fR4KFbOOH/wAeOMbgfvuA558Ejhsv92Di4Ft///dv3O7Es/jN2EViqIRU2Cf/Wx3vP326dFTg9TvwDbGLL9Dd8kll+QmIYiSaZsGvvlN4OqrnQf24x8HPvQh4JFHLsFLXgI873nhfCeeOMj/mtdMh9Z9AcaYg+E+wL7OWvugSXxpyRhzDoBzAOCnp+zyLp1HSqcvBy5ZXAT23x8f/6Gzqd56q/AqzurVzvKrCmxjxIxQxhjRCKU8LOOSSy7BdTfuyU1GEfjzPwc++1kAcPfrMFw9uHjLLcArXjGUZ/Vq4Gd+ZuhLO4p9CCXzL6A8zHHJJZcATzzh3nUNKbDHHeeOt90GvOAF0yMugokpsJjh5Usnn3xybhKCKJm2aeDii91GEO9/v9vR++KLgb17T8YHPhDPd8QRbjWTemDTYYw5AE55/d/W2r/tRX/fGHN0b+J7NADJVwZr7eUALgeAU045ZapvHpfOI6XTN3VYi5NvuQV49atxSW9/pttuA172MiHtkUeqAtsQbY1QysMyTj75ZNz54EP1CfdxPPqoMyC/+tXAkUeejE9+Ejh8/48BBx7o1hNHPrx+yilzt5HTXKFk/gWUhzlOPvlktzQYcBNsCd4De+ut0yEqAbVLiI0x/8MY8zRjzAHGmGuNMfcaY36rLt8sL1+65pprcE1hLyt7lEzbpPHP/wx86lPAOec4hfR3f9etPDziiGtw0EH1fXLiic4DO29ow8PGzXI/DODbbJfETQDe3Pv/ZoCa3MtA6TxSOn1Tx91345rvfQ/XHHII7rvPRQVl5BwqsG1lcC9v0AjVux40QuVEyTxyzTXX4Bs3XJebjOy48kpnRH7HO4CzzroGl19+DY47fLcTzs96VnTb/3nayGkU/p1VlMy/gPIwxzXXXINr/uEf3EnIA3vYYcChhzrrciFI8cC+2lr7DmPMf4D7ntWvA7gOwCdSK5m15UsXXnghAODUU0+dar0pKJm2SWL3brexyzHHAP/lv7i4170OeOUrgXvuuRDveQ/wK78S75MTTwQ+9rG5tPq24eGXAzgTwE3GmO29uPUALgbwGWPMWwDs6JVVFErnkdLpmzpuvBEXAsD112PvgS4qKCOPOGLuFFi0lMEJRqiLUagRqmQeufDCC3HvA08USdu0YC3wp3/qlgK/6lXAL/6iu1+/c/jh7nMbz3pW1APrN3L68pfdN2H33x9473uB/fbNXVlGnkPPGkrmX0B5mOPCCy8EfvQjnAqEFVjAeWFnTIE9oHd8HYBPWWvvS12CBMzm8iVFWXj0UeC3f9vtIPxP/zT40saqVcCWLcDSUlo5J53kVkncdRdw7LETI7dENOZha+2XAYQS/dIYaVPMO/zLcAcfjPt2ub9RD+zKylTIKghtZfDMGqEUebF3r9sckfsOrAWuugq4/HJg+3Z3rDyKDzzgvDQnnAB88YtBa7HfyOktbxl8Avqgg4Ce3rOvYaQ5tEIxFTzxhDvGFNjjjgO+8Y3p0JOAFHvX540xKwBOAXCtMeYoAI+mFD6ry5cU+WGtm6du3AgsLrqNIt7znsB7cYnwGznN4XuwrXlYoZg4brgB+ImfAPbbDz8k78CKOPJI9BPND1rxr7X2y9ZaY619gbX25F74e2vtD621v2StPaF3vG/iLVDMFM46yzlRL77Y7S7s8ZGPAL/2a8BNNwHdrktXwe7dAwX2kUfcZwIErF7tvspxyCHA3/+9M1C/+93OC/vovieZWvGvfsdZMVWkKLDPfrbbhdinzYxaBdZa+y4ALwNwirV2L4BH4N5jjWKW36FT5MXmzW5H4ec/H9iwwW348MUvAm9/+2jl+k/pzNt7sG15WKGYOKx139PofVfuvvvccsK773bfdh7CkUe6ZRQ//vF06cwI5V/FNPH5zwOf/CRw/PHAH/8x8IY3uD2ZrHXLfV/4QuDOO51sPuAAlnn3breE+IQT3HlkGfFVVwHf+Q7w2tcCH/iAO/7RHwHPfKbbpHFfwQj86zdCfT6AlwI43xhzItzGp9daa08AcG3vXKGI43vfiyueqQrs3r3Ad787XtpaImUTp5/4/9m7zvAoqjZ6bjoQQgudgNQgRYSEXgKIgiKIKFJFRUH9VOwigg1UFMWCIKICIipSFQGliCCI9Cag9CY9BAikZ3fO9+Pd2ZbdZANJdgJznmeeZPq7M3PuvW+9AJ4AMMm2qRLEkpQT9PCljkqp7bblDkj40q1Kqf0AbrWtmzCBjAyx6nbuLOHBEydKR7l4MdChw9VfPzISKFv2+vPAXgWHTZjIXxw/LpNJFi8Oi0X6UL1Cv8fp5iIj5e915IU1+Wsir0G6elZJKap0/Djw+OOS27pzJ/D++6JofvQRsHIlsGsX8PTT0j97hB5CXKeOrGdTyCky0pEOFBwMLFokxuv69YFhw7wYsAohrpS/hbkQqgmDISFBLFLDspnwxVcFFjBMHqwvObDTAGwB0Mq2fhzAHACLsjupMOfQTZ482d8ieIWRZbtSpKQAn34KjB8vnpcHHhDl1Vs1b3fk5plcp5WIr4jDhRVG54jR5StQbNkCAJj81lv4r8zNuPVWoGlTYOtW6SP1sH87dAX23Dlx1VwfuK74CxibI5MnT8aKTYV7Dsn+/YHkZJlTHQCeeAKYZFOvAgKAH38EQkKA558H1q4FXntNPK9lywJ9+2a93uTJk0ULbtBAFNgqVeRC//3ns0wBAUCnThJC3K2bzDoQF5cHP9b/uGr+XkkhVH/CyPwFrg0O5wpz5gjhP/0UeOopyctzwuTJk4H584FXXsk5BxaQIhW+Fp/JR/iSA1uT5FgAmQBAMhXeFdNrAtHR0YiOjva3GB5hZNmuBP/+K7kwL78sg9WlSyXPxlflFXB7JgsWSGlDL4iNBTZtAn7++erkLmS4rjhsdI4YXb6rBgkMHCidZU7HLVwIBAYiunt3lCwpz6RpU9l98KAYjseOdYp80hXY+Pj8kd2YuK74CxibI9HR0ahctba/xbhikOLp/Pln8bKePQtMmQJ06QJ8/DGwYoWDg0qJMTk0FNiwAXj0USAsLOs1o6OjER0VJUQtUUK00cjIK+Jp69byd82aq/iRxsJV8de9EGouzhuilNqslNocX8DtpZH5CxR+Duca330nSqtSEubohujoaETb0niyVWCjoiT8wiAeWF8U2AylVBEABAClVE0A13QC0sKFC7Fw4UJ/i+ERRpbNHbt2AatXe9+/bp10lGfOAEuWSKd62225v4/LM3n6aWDkSK/HvvYa0KQJcN990lFfJ7iuOGx0jhhdvqvG0qXAjBlizfU25Q0p82FNnQo89hgW/vYbFi+WZxIdLX3ooUPAqFES9aQXKkatWvL3+soDuK74CxibIwsXLsTGtb/6W4wrxqlTDlqOHy+VhDMyJEz46aezOlYqVRLvbO3aEl7sCQsXLsTCOXNkpWRJ+Vu27BUpsKVKiSM3Gzt0YcMV8/dqCqGS/IJkLMnYsmXLXu1vyBWMzF+g8HM4VzhyRMg0ZIh4X7/5RgbnOvbuxcKvv8bCTZtkPTsFNihIFGGDKLC+hBC/DmAJgCil1HeQ3NYH81Mof2PcuHEAgG7duvlZkqwwsmzuGDJEeHL8OBARkXX/u+8KV7ZsASpXvvL72J/J7bfLzRITvZbvj4gAfv1VOulu3YC5c2U+2Wsc1xWHjc4Ro8uXa5AyH0b79sD994uFt3x5ce2MGyflw3V8+qmMmDMzgb17gcGDgfHjMa5jR5w9CwDdUKaMRCpt2AD8/becdvAg0Lw5ZDRdvrw99Pg6wXXFX8DYHBk3bhzOJVrRteud/hblirDdNqFSw4bAt9+Kw7RzZymc6A19+wJ9+nifP33cuHFAcjK6Aa4K7Nkrm2SibVuRzWrNJt+28OCK+FuY53E2Mn+Bws/hXOH77+Vvv34yAJ48GfjgAwl1tFiA9u0xLi0NiIhAt4AACbfIDgaaC9aXKsTLAfSEEG4mpJLaqvwVy0Rhx9mzwPr1UjB0ypSs+0+elOJMDz10dcqrC06ckB7v4sVsc2/KlBHva716QPfuwPTpXg+9JmBy2ES+YvduYNo0SV5/8EFJXnvrLaB3b1FYnb0wU6dKtZj69WXejM8/l3BDOOaDLFNG+sgNGxxTatj7S6UkD8Dukr32YfLXRF5Cn8Zx8mTh15kz4nnNCTlOXarH+ZcoIX+v0AMLAG3ayNhBN2AVZlwFf81CqCauDqRYgtq0AW64AShdGrj3Xsl3TU2VgfDp0zJmTkkRj1JORK9ZM5uJ2gsWvlQhHmWbN24xyUUAztusSCZMuOD++yVHBpC53UgJmR8/Xgw9zpg+Xfq7QYPyUIAjRxz/5zDZcrlyUlWxfXuR4fjxPJTDYDA5bCJPkZYmLhm9GtqqVfK3TRsJT6peXZTZ11+XTlKfE8Nqlcmde/eWiZ1fecWuvAKONqJUKUetiB49xOnq0l/GxkryfPL1UYTD5K+JvMT27ULRli1l6pr69cUDe9XQCax7YMuVu2IFtm1b+Xst5MFeKX/NeZxNXDUOHZK+sndvx7YBA8Q6tHChKLdFi8r2hITsw4d11K4txxqgDoUvObBVlVLDAUApFQrgJwDeJ/cycV0iKQmYNQv48kuJDFy4UDyrH38seuWcOcBvv0nIblqaeGXbt3dMF5cnOHrU8X8OCiwgU09OmiTTCcyalYdyGA8mh03kHTZuBH74AfjkE1lfuVLyYn77TSZy/OormRejbl2Z/2rJEjnu0CEhvz4hsxsyMyXCKTjYUX345Zc9RCzFxAhp9VjIax8mf01cFcaMEaMyIF1jo0by/9y5UmU4wJeRYE5wV2DLlpWJnd2t1z4gKgqoWvWayYM1+WvCP9iwQf62aePYFhcnVuEvv5Ry4/36yWCY9E2BjbXNALVxY97Lm0v40mw9BKChjYALAawk+Ua+SmWi0GHlShmAklJ4ZdkyyTG96y4ZgPbrB9x6K9Crl8Oj8sgjeSyE7oGtUsXn2KPatYWPM2fmsSzGgslhE3kHPf90/nyp/vLHH6KohobKxJEdOzqObdZMEuHT0hwe22wU2DJl5P/+/YXCzZqJNzaLAgtcT2HEJn9NXDH0YIchQ6Q8xL59wM03y76iRR0Rv1cNXVF1DiEGvBdyywFt20oRSOf5agspTP6a8A82bBCSN2jg2BYYKBFUv/0mUUwDBkhdCcA3BTYmRixeunLsR3gt4qSUauK0+gmAyQDWAvhDKdVEn2D5WsSMGTP8LYJXGEm2b74RT0lsrBQeLVpUBp5ffin7u3UTrkycKCX7O3eWMe6HH0rIbs+eeSOH/Zm88QZQsaKUNvbBA6ujb1+Z727fPsf869cCrlcOG4kjnmB0+XKErjgmJEh+QEKC9znhYmJEM92506HAZpncVZ7JwIES2QTIHJQNG8r/NWpIW5OWZpvCo1IlWa7xQk7XK38BY3NkxowZWLohyd9i+ITz52WO18hIKRExapQYmXUPbF5hxowZwIQJMueVswcWkFDDChVyfc3OnWX2j40bgRYt8lDYAoLJX+OiMHH4qrBxo/TBQW6qXv/+UmAxKgpo2xYzZs+WgbwvCmx4uCjERlZgAYxzW78AoJ5tOwF0zHLGNYKoqCh/i+AVRpFtzx6p11K3roxNly6VMeyIEVLPJSTE4Yjp0kUWHc7/5wXsz+TIEUlUb9QI+OknsS75MKFs794S+ThzpscpsgozrksOG4IjzpX/3GAI+a4GmzcLideulRExIB5YT9DDjTZvFgW2alUJV3JDVFQUUlIcHlhn1Kghg+4jR5wqpV4fhZyuS/4CxuZIVFQUyh6+7G8xfMLzz4t9acMGmTpOj/rXPbC5gpfK/oDT+woNdUwUW66c/L3CXLlu3SSdYN68wqnAwuSvYVGYOHzFyMgAtm2TqXPccfPNUmCiQwcgIABRjRsD//sfEBmJ8eOlQLEzYmNl8gA7mjeXvEBNy6P8gyuDVwWWpJcRybWPWbaEyN7Oic8GgVFkGztW/v77L/DOO8CBA8KTatVkGlar1fOE5/kB+zM5elS8r40aSWe7a5dt7o3sUbmypAVMny4O3JIlJdQ5x6qLBsf1ymFDcOT118Xq6UGBNYR8Vwo9BnHgQHHrfPutaJhVq3o+vmpV0Uq3bBEF1jmUCWI0WrUK6NhxFo4eBWrUyPpM9IJOBw86KbAxMZJof/myR4X4WsD1yl/A2ByZNWsWNv+birt73udvUbLFtm0yEB02TOY+f+op4JlnJMK3WrVcXEjTgOHDxSi3davDs+qEWbNmAVu2oLdzPLJ+3BVOpVOyJHDLLaLAjh1b+Ppjk7/G5C9QeDh8VdixA0hPlzwcdygl+a82zJo1C2jTBr1798YnNSUbQI/SiI+XqMqHHpLCbwBkXP3llzLw92PYYnYhxANIfquUes7Tfrd5qa4pTJo0CYAxyWcE2Y4dA2bMkNCkNWscXku9kmFBezHtz+TYMdE8debt2OGTAgtIJeKBAx1VlENDJX+3MON65bDfOXLxojTsgYHSgbjNq+Z3+a4G27bJ35gY4KabRIH1Fj4MOKa9Wb8e2L/fpdxpWhrw3HNSxX/XrklISADKlMn6TGrUkL8uebCxsWKkWrcOuO22q/9dBsT1yl/A2ByZNGkSziVa/T/4JXFi1X6cL1MbwSEK0dGuSt7IkVLRe/hwWX/oIeDVV4W2PiuDqakyvcC8ebL+yy9SYdwNkyZNAv75B71LlXJsdA4hvkLce6/Uyti+HWjc+Iov4xeY/DUmfwEDcTg/oYf4+jAG1t9Xhw69ceiQGIxefFH2JSVJpPGHH4rTFYBDKd6wwa8KbHa+Xz32sriXxcR1Ctsc1XjxReDNN2UcWbWqn/NH09Ml165aNQkjjojI1SRy998v8+H9959MMfDOO/K7CjlMDvsDW22pTVareCuvJehhuzExojh26ya5BNkhNla8rxkZLgWcZswQ5RUQnd9ikWnq3FGunGQCuCiwrVtLXt3AgcA//1zVTzIwTP76CenpjtmhjIjMVAvWNn4SlTtG461Gs3Hjja5hf3/9JbrmSy85aipFREjV4XHuga3Z4cUXpVjbuHESnrR4sfdjLRZH/isgZFbqqhTYu+4SO6CuPxcymPw14T9s2CB9ZC7Cudetk792Tysk5fXRR6UZOHzYtrFePdmxYYPk2T79tGPS9gJEdiHEk21/3yw4cUwYDadPA599Jgpe7driRPniCylcVrWqcOPOO8Ug49cQH508N9wggjRpItVRs8nbcYeesvPSS8Djj0tl5Y6FOEvF5LCf4JybuXu3oxpRXoKU+VW7dcvjuahywObNQnzdu/Lzzzmfo1cNBuwKrNUqVt7GjUX/TEiQ3Z5yYJUSL6zLXLAlSoiG0aGDeID1inHPPw9ER1/JLzMcTP76D9Ony6Bt27YrzBfNRyQlpGNn7Z5ofeEXWAKC8UGLH7H5dG/MnCleVkBqUZQvnzX97bbglUBQSQBe3JlHj4oBrkcPScGZNEly4557Tog6d64YioODs55rsbiWNA4MlDSDq1BgIyMlvWfuXGD06MIVRnw98vfCBTGUmMgbZGZKKRdnu5A7zp0TnmRZ37hRvK820pw/L/QMDPR+rXXrJPPJucsGgCefFBvW+PG2ad0DA8HYWGjzf0Lg11+LkN27S8x/ASLH7FulVA2l1EKlVLxS6qxSaoFSqkZBCGei4HDpkqSUnTol6/HxEjIQHS0dx+23y7aHHpLc1rffluOUkvNefdV/sgMQkzngSO657z7pgPWQx1zgwQfFcDVmTN6J508YicPnz7ukXlyb2LJFquQGBjoq7+Y1duwQZe2rr7Ls2rBBDE35gi1bHIWZfIXz8TfeCEA8KgcOSJhjs2YOBdaTBxbwMJUOII3TqlXC+WXLZHLpyZNzJ1shgJH4e71A7zayczj6C3+99BNaXvgF6/uPR9ADAxD1z1L0udeC338XHm3cKLQYNsythuGxY0DXrjIa9YbXXpPpAYYMEa9KyZKOQm133CE58Lqbxh3uHlhADF1XmAOro08fmVv+r7+u6jJ+w/XC35QUiV7TZ6EwcfUYPVoiG5O8FEz+7TcZq+7dK+t7/ziNjWW74lJ0rER/2cKHk5PFCPzpp9nfb906MSoXKeK6vUoVyc6bOtUxrdXu8OYIPHUCliq2MbcfqhL7Uj7qewCzAVQEUAnAHADX9qyZ1xEuXpQooagoMaBUqiSEqVBBxsctWkgp+//+k9TStWulkmGlSv6W3A26B1ZXYPv0kdzDadNyfamwMODZZ6VxMHIYWS5gGA6/9JKMj7xOUJ+YKCa+5OS8u+n27Y6qwAWBzZuBVq2AWrW8K7CpqVenbM2eLX+dNdVZs5C5cRvuuMNz4UE7tmyR3FVvOHtWynIPHSqWqcRE2X7unGiduVVgq1SR8Ibq1e0j6lmzxJHbo4fMsa5PIenJAwtI53vokIew/jp1gE2bpIFq3txzZeIffzREyf+rgGH4a3TEx+cuku2jjxwzPTlj1y75++uveSdbXiHz519wMbA0mn/9P1EqL17E/bXXw2qVgIjx46Wu2cMPu5340kvS7mze7P0hbdkiVqSvvpIQpNGjHValTp3E8+pNq7daPSuwV+GBBaQOXsmSjgrKhRDG4W8+5kUdOCBdxbVfHL7gsGWL0MfbMHbzZqHdypWynvnK6+iE5ThprQDcfbdMsWE7LjERWL7c+71I6Uqdw4ed0a6dOLpOnpT1HyOH4BMMxbKRa6Qf9kcfSzLbBcAGD9vW53ReXi4xMTEsSMTHxzM+Pj7f73P2LPnll2R6uu/nuMuWmEimpjr2p6SQly55P//4cfKTT8i//yY3biRvuIEMCCD79CF/+YV86y2ya1fy9dfJzZtJTZPzvv6aBGSfvs0oiI+PZ/zAgWTZsq47evcmS5cm09Jyfc2kJLJGDbJmTfk/lygwbviy+JvDOn+PHyeDg+U7uuOOrA9t78aLZPPmcsBnn+XuiTshC3+7dSMDA8kC4DQTEkT+MWPInj3JOnU8y9evnxy3apXLPk0j9+xxPX7vXjfOaZp8mADZoIFss1rJIkV4vlF7AmR4OJmZ6VlES5t21JQid+/2fMC778q1S5WSxqFZM/LYMbJVK3mOW7ZkOeXwYc80O359mRzQAAAgAElEQVScXLGCPHLfi0waOty+vV49skcP+f+XX0ggnkA8163zLNLEiSLSyZOe95Mkn3pKfrjF4tiWmUkWL07GxWVzYhb4nbPOi7/5SxaOPjgjg6xQgRw0yLfjNU3oCZCzZ7tuL1mSDAqSzz8hIats3yw4zLU7kvN0+emPbDpuG/7dbeUZlOU/jfvJhosXyaAgasNeZrVqQtXgYPLpp91O/OMP+aF6+7pmTdaLJyfLD371VfKbb8jBg7M2Ih07Otoct2cSHxZGvvCC645evci6dXP8XTnhxRel6Tl2zKfD/c5Z58Uw/O3Vi+zb16cHeCWYM0c+rbZtC24Mzfh4uaF7p5njaXnP4Rz5a7WS994r3HLHm2+SQ4Zk6USfK/8tf8B9rFFdk25txQqyUyf7oP+hh+SZDxxIcs8eWlQgx+NJtm/vevkxY+S40qVFDE/P47ff4gmQM2d6Fn/FCrnGihWyfvvtsj5sGMn77yfLl89L5cCn79oXD+xKpdTLSqkblFLVlFIvAVislCqtlPIS8FW4ERkZiWLFItG5c/5ZYNeskdyawYOBDz5w3Xf0qFg75s/3LFukLeD95ElJKYuJEefI2bOS+hkd7ZYv5oRHH5XIoJtuktA9TROv6syZEiY8YgSwaBHwxhtyXT3n5IEHROaZM42XhxIZGYnI06cl/9UZDz0kMasLF+b6msWKSbjEwYPyTAo5DMHhDz+U7+2RR4A/f0nE8de+kMI+AFbMu4gLzW6DtmWrJNHoJsUrgDNHYLUCq1fL34KIXd6yRf7Gxgo5DxzI4u2IjIxEpB6G99prYvo8cQKYMgVffm5F3brAro0pwKRJ2LrqEqKjgQULnC6wbZt8mBUqyPU1TaorpKaixI4/UB6nkZQEHPrqd8kDd8KF3Seh/lwDRSLz1VGef8PKlVKk4fx5aYS2bZMY3o0bxXXapInL4cnJMjvOKLfLkZISc8stwA2zx+Ku3e8AEG/r/v2OKXFatgSUigQQ6dUDqxeIyzaMMCZGYq2cC2dt3SpT7axbJ96nwglD8DdH3H038PHHnvelpuL4LQ9gb51u2LdHy/FSkZGRCJr5K85E1kPKxl0+3X7lSqnZ8O23QGqvgdL+Z/POd+92fCrO3r2TJyUyqW9fodayZVlliyjp5UPNZ6x4bzPKIR4VH75DNpQoAbRpA/XrL+jZUyhqsXiIwHjpJQmzsk1vgrVrs1585075wY0bS9GLL76QhDhndO0q7uk9e1w2R0ZEIDItzbcQ4gEDpPpjLvDEE9KeTJyYq9OMAmPwt1gxYOlS6QvzAXoY6759bn1wfuK332RgmssIK79w+PvvJZn7889dt1+6JPlqX3whBWVs8cIZ6cSgM++gN2aj9OHNUm7igw/kNy9aBMDRfv31F4CRI5GGMLyFkfZ2QIfeb54/77muZGRkJP79V96XNw+s3gfr5+t/162DRD/pVVALEjlpuAAOZ7Mc8lVTvprFm/VX08ht28h161yN7leLadOmsW/faQTEiZdX2LpVjGANGoihs1YtMWgWKSIeDJK8fJm86SaxbAQHk4sXu15j6tRpnDZtGlNSyKZNyWLFyLAwskkT8uab5VqlS5PVqolzZ8wY8oMPxJD6++9y3eHDycmTyZEjs1qXCyOmTZnCaWXKiHXLGRYLWbmyPKiLF6/o2k88QSpF/vxzrk7zu9XXefE3h2NiYpiQIN/qgAHk+fPku8Ej5WPs1o08fZqHImOZjmA+U2MBtQEDxJueS2uepol1cdo04QhJctMmuY9SYrm04cIF+fZTUnJ1C4/WS1I+NU2jw9R5/jz5ww8kQG3bdpdjp73/PqcBtNRrSALM+GIaWb06CXBo2e8JkOs6v04CPF61BSNw0dVwPmyYuIfeflvudewYuWCB/A/w3agJLI1zTA8NF7fFrFkkJTLj/arjSYBz0VO8sLt22eUnKW6sYsXkw9fx888i3/z5Hn+/bpmtVcv1le3cKdtfeUWoWby4PL+9e2X71187jq1SZRqBaTx3zvPzzcwUEZo3z+az2LVLLuxs4da9yYA0gL7B75x1XvzNX9KtD05IcCGCppHaocPyjD311WfOMLFeC/t7eBBT2bNnNn22pvGLO+7iNNvxp+t3yPrSPbTnQ4ZIXxiGFFpVoNyvRQvyzBmPt3njDWkWXn5ZDt28Wbb/+qvjcylTRpwLzpg2bRqfenlSgXtgLRZyXPHXaYWiC1HGjiUBbpz/n71JdcF//8kPGjtW1uvUIe+8M+sNJk2S444c8S7EqVMS5eB2/rTx4+V9TZjgevzrr8s1dU9ueroMbCIich3adM89EhRy+nSOh/qds86LYfj73XfyLjZtkqf03nvkLbd45UduMXCgneKcONGpD74KnDghwVju9E9NJd9/n8x86lm5YatWubpuXnJ41eZkPvFcOmctdeNvairZurWMCdLSJNwRkD45MZGktDV7R3wt24cOFcXg1ltJTeOh+dvsD3RK8afZtekZORewhy+VLStDgZ6YSwJ8A6+xWTM5RA+U0jQyMpL27VOnen4eLVpMY8WK3vtXq5UsWpR89ln5OQEBcu8iRcjMdbZxlnMoy9XBN275eqA/F3cFNimJHDGCrFLFQZjISCHQnDmedZULF8hp08gvvpDxzX//eX9ybdvGMTg4joCEJLm80JQUxncZwDNPjvJ6/uzZ8i06twvLlkm7X7asdDAjR8o3fOyYfBTdukmUT9eu8mHMni1KaViY9AF//ikdbnBwHIOC4li9unS+P/0kIXjBwWRICLlkiXTEERGOZwOQ3bvL9apWdQ05LpRISRECf/UVSTKuRg3GAfaBugu+/15YVrcuuX9/rm91+TIZG0uGhuZm/Ot/zhhpiYmJ4Ztvyne4cydJTeOZUnV4EhVIgFrRokxDCPsWX0iA3PXcFDnYplz5ijvvlM8iLi6OcXrI6Pvvy7UGDRJinT3Ljz5y8KJ4cQ+RxcnJ5PTpWYiSmCgD2kmTXA+3WMTo9OijlFFWjRqyw6bBvVz1O55btZP87TeSZFx0NOMAxmIj/0NlEmBm8ZK8VK4G/0Fdlg+MZ3JQBNmwITNVEDchhqNDRzHz9VHkqFFilLn9drmeHtPzzjskwAOowVPR7TghYrgMdBs3JgMDmT59Jtu3J9egDROrNuDd7c7xkipOS89efOIJaUsTEkj+9Zdcc86cLM/3wAFpZz/5xHW7/m7dX9no0dJGnTwp7S5AHjzo0LXXr3ccW6lSHJWKy9YQ+dlnct7KlV4OsFikMXWOn+zSRax5emikb/A7Z4y22Pvg3bvJ0FBqd9zBTb9f4pAh0tfMv9UW4x0YKI2mjn/+YXqV6kxGET5VeR6Tb27JS8XKszgS+dFHHp58WhqtffszDmA0KvD5oI/lunPnOo7ZulVkeG8sd+0Snchikb61d2/y6VYbSYCWBwbJ6KpnT48vuWFDsk0b4XV4uENR1ZuMhASyf38xCHfuLP3r7beT9evHsX6jNgWuwC5bRm5ELOPrtHTd8c8/JEDrW29z+HAPmQGTJ8sP0ncMGuQ5lnDwYNEQczIcvveeXO/XX+2b4ho1kj7YPf5wwgQ5Vtc6t251NBbOFiwfsGOH0LtpU2mi09O9dhF+54vRlpiYGHkHenpLRoY05oAYNI4ezcWb8IwWLRz6VZMmTn3wVWD4cLnev/+6brfZhhlfp5Wj3bEphb4gLi6O9W/OGw6/82EaAfLpV9ys4Xr/DMhHCzisZQsWUNPISpXI9RG3inVW06RzBchFi7i/xwtMRzATYzowKbwcn4Zt4GJrjC4cOk+A/OjmabQggPvLtmAxXOasWXKYbkvav1/WP/9cUiMGD876PNq0iWNgYBwHDMj+uTVqJDqKrclht27yd/Nf6TJIfv55OfDqQ4l9+q697wCaAqjgtD4QwAIA4wGU9vUGebHExMTwz97j+cPDyzh6lMaGlc5xMgZzffnuXD1oGudNvcj+/aXtBcggZHBM+FvcVLYL13z2tz3X01mhA8Rj+eqr5I6fDlGbN1+s9aNH8+ZSN7Ac6vLVrlsYiEzu3Wt7pCkpPF7vVvsF5rSfwFOnXN/V7NkyVgLkYxk6VLwPwcHSYZ44kfVNOTsJAHL8eJKXL/PsWTGQKUUWRRIBjaVLx7FixTjecovkz+pYs4Zcu9axfvyZ93kwthdPbTjKTz+VawDkjBk+fj5GxhtvOB7WSy8xTinGRUZ6J83KldJh16jhPTEwG5w7J17zokXJli3JDh0kfzgb+L3DooE4HBMTw3/+IT/+2PZ0tm8nAT4eOJnftpvM9FLleAcW8ZtvpEHv3/KgvNtPP/Xp/ZCST67zLibGqfO84w4xXuzYQQJMH/85y5cXa6TuHJgyxelCSUlk+/ay4623XO4xc6ZsrlTJNVVF7zDKBZ6jtVg4+cADJMmUi+nMQBB/RWcmBYRTCwkhjx1ju3Ll2QpBbNPKyl8HzODxoKqMC9/MJ8pLEtHB0jEkQOu2HexX7CdeVuGuDURAgAzojx6lvWfq148XIqryNfUmNaWYGliUP4b1pnbpMq1t29GCAD6PD+T40aO5fDk5BsNohWJ5nNI3O7y6Z89mecb6Lvdn1qkTGRWV9ZHFxAhfSOGLrofo419nQ2OrVnFs1Cgu23ecmippNrfdls1BrVvLQjq8yf/7nwwg2rbN9vpO8Dt3aSD+0sZhahq1WzoxPaQYMxHIbWjEGmEn2LAhuRBdqekEtBlquGoVrREleDawPDuX2sBDh0hu2kRNKf5R8T4+FTyJZ0dP4qFhk7j5kUnUPpsk7whgVdzA2rXj2OPOTP4b0lCMEElJIkObNiTAJFWMlXCc3bs7xoqzZ5O7h35OAhze5xB3NHuE6eGlmJluZWamGHvnziX//eUgm2KDvU166inpo0+dEvrWrJBELlrE2bM0AmLgefhhsR0BcSxTtl2+K7Dnjibxz7dWUrNKv/Z4T1FAMl4fnfWLvesu0cL1JPHlyx1e2rvukuen949TbAZCd003NlYGHDkhLU3CLaKj5X+LhXHh4YwLCcnqVZ09mw7LJcXoDMhgzXc+2rFggYxlmjQhK5ZJZ3f8xA/HZen3/c5dGpG/pFhaO3YkFy2iXaGKiJB36S28yEdElbrME2HVeT+m88Yb80aB1btid6/h00/LOD89MEw6GptS6CvyUoHt92AGAbLLXW7FbF5+WZwngweLfB06CF+KFiWfeopHj5LlcYoWBDBz2Ag5JyND6ls0bMhLEZX4E7rz8vR5JMALKMHDJRuJhwrgoeFfcCjEwLdcdWKp4MsMCZFbVKwoxjdSHHaA1L3p3NljCjtr1owjEMcNG7J/br16kbVri+NM78/tOkuLFsLpGTOko3ZWSEgxMFSrJk6lnOEbx7zuALbqJAPQDsBJAPcAGA1grq83yIslpkkTHg6qRQL8Cy14LqgcrYFBjlFTmTLk998zMyWDO977lacqNrZ3cOkI5mQM5uIi9/BCozhefPkd7lmwh++NsfK25hf5BWwfl9MSZ1sI8Cwi+W+bR8jHHmN8+Xq0QnFc/SncfkN3WhDAfviWJUtobN5cCiEFB5OtW2ncsjaVnTrJ91u7tlh3L1ywvZr4eGnIbRsyM+WdL1lCHjuqyUg/MNBeyCZ+5ymmhZfmxTv7u3qXvGHqVPktSomL6Z13uObNFXzr4UO0HjriJEgu8PffYm7xFBby8stitr4aq4t75SlnK74zDhwQS88990jHDDAuKIhxLVt6Pl7HPGkEOG/eFYl36pS831tvJcuVkzYmOdnr4X7vPGkgDsfExJCHDkksu6ZJTGlgIJ8dcJahoWS/vhpDQqR9++ADEtD4X2BV/hp+D3+cr0mDN24c+fHHPL/rBDt3luOcMWUK2Qzr2TRoC8uWFY6MGJbJlODi5GOPyX2joxlf+SY+i3Hc9cYcalaN1ao5Qu52bkjm+iJxtCCAB0LqMqN4KRctq3dvRxEqm/OfmiZOzmrVyDHK5vW0uQUWLiR340YS4D7UZoYKZsYjj7N1UCjrIdLugTx8SGPlyqSClQlRkj8wG/faO4lJE60sXtTCx4dYxNVksfDECfK2TlZaQsLE6tmoEVeH386BTcU0qinFG7Gbe/eSD957mSsR52jj9uyhppG96knI7cy2E3jHHWKQt3ToxMSqDdiypVhZndG0qQwcb7tNdOjff5e2S484btHCEUGq69bvvSfrKSnSpI0cKYUnKlZ0vbZP7RodEdp6eNSFC3LPqCgxYu/q9DRZtCgTz1v4XKt1cvCcOeRLL8nL80DaqVPFyOjU5PiduzQQf2nj8IWv5YN8Cp/wqTpLmBFajBmdu/LSmRSmqCKcX+IBCUt/803SYqG1ajUeCavD6NDDrgOjoUPp3ufqizU0jP0DZ9o5/PHHZDuskus2by4jJYBvYzjTVCjX1x5AQLyvYWHyDrUhjzIxsCQBjQPxNQmwXZldrFRJbtMJy3gREUxFKE+sPUyS3LdPusvXX5fvaWGVx4RHf6zmzp2OcGeLRaIFgDi+MCI93xTYpCRyYWmJydz0+kJeukS+E/Qq7SNRdxw4IC7iAQNEG9c9NWlpDiOODj2G/4svHNsyMuR89yJM3rB4sVwjLo4cO1bGTDfemPW4lSvlOL3yyxNP2MckBOjwDviOCRNkCDCn7ggS4N2Yz/nzXQ7xO3dpQP6SJJ97Tt5zjx5i1E9Pl8EnICGAOjIycvVOzp0j++FbEuB6NGO1al7a88xMn50ImZlk0SIaQ5Dm6jXMzGSzZmQTiCLHb76xK4XZIi1NBnKnTvmmwG5P4l+bL+bI25saWwiQ1WtZXH9bbKyEeGia9EHHj8v2Ll3IunX5ww+0e1W3fOvU2eqWcoAPF58lltuSJUmAL6mxPHpEI+vWZVp4aRLgpU53s0Vj8QLrw+CePR2BYI8/LpSzWBxpE87G48xMMjQ0jhERHt6XG0aMkH5cN2afPy9Gvb59KVYF3QUPyIDAWSeYNk226wbm7OEbx7zuAHY4/T8RwBtO69t9vUFeLDExMUy5kMb0TybRWr0GtebNxYujaZIAqwd3Fysmf8uXJ+fNY/rxszzUvA8zA4JpqVlHXK76wy1WjCxdmlpAALd1ep73VN3I4khkSEAmS5Voy+YNm1L7fibnhfVlSlA4LREluQatOa75LKkanJTE5AZy38PlmvK5xr+zcmXy1g6ZzOjcVdw0hw9nNWrt3OlwB5coIeX1PvrIsfTv75CvTBkZoT36qF3uuKpVGRcTQ/brJwrc8OHk6tVybU0T00hQkLhF9u0Ti6r7QCEkRDqQzEyxul244PjQDhwQ1+7ixVLZbcUKUTiCg+W6gATB60R1Dgl65RXHddLTJcRo7lzvyigpjcqgQcKq3r3JH3+UOC2lHG6ezExppD76SBqE8HBpDNLTyWHDJHwpp8FvZqZoGXlgFdT75Gee8XqI3ztPGojDMTEx8m3o30itWuStt3L/fofX9Pbb5cElJUk7uKbWAzwfWIaTAh53+XZPhFTjDThEwDWU9a3YH5mOYGYEhrIUGrJ8+Tg2xQYS4J5RElpueXesKw9eeIFPD9UYGiqf6Hf13yIBToqbye5VpHM8/qikCqSlSSfwyCOixNWuLR3CkiVyqW8/jmdKUDhnBfaxOy8ffpj8Ivh/tDZoyIVfnuIkPEaLCmQcwFpFarl8MPv2iXKWuWAxLUXDWQ+77AVDDxwQe03FikLXc+ekii9AHg6vT955J60hoRyLF8Sj1KYNE3o+TECczwA5blSSeKOdPCw7dpDx5etRa9eOa9aQwUhnWmARTgh4ioA0YYcOybF6Gt2YMaIDRkWJsVVPMZ450xFJcuyYXc9wGZ/WF1HZooUYo53hqwJ78aI4DHr1knV9HDxggOg390PMzfc32cWXITs/GXlWL3Xs8A7a4FyJtmNHe9S437lLA/GXJOrf2ISHA2twt6rHqZNtg1s91tamMN2GJTxaqhGtt9zKy/OWkgB74wd35UJw5gx/nHSKPVud4vT3TnFwt1Msj1OsV/Uyy5QRj3xcXJw9j/q3J+ZLODDAf8IaM7qWhdaXpU35rOdyAo6q1mzalFr7Drx8mby0VeLnPmv0Obt3Jzc8P4vWwCAeK1GfaUFFhVg2dO0qxslmodtpVbaGyRZN4Yx27eIYHtGOAQEaJ05LzbWi+uf2ZK7emnX7j6su8dw5CX54vs16EmAmAnk0tBbnvL2XKQhjfKc+Hh6mDXp4IuCoNqwbCxYtchynaaLx160rxBk/3h4V46N3RPDddzKWABgXEeGZv3pe+g8/yHrLltJwnDwpg90nn/T9fk6w7j8oWizANSW7MixMhj42+J27NBh/Y2JiqGlkyvxfHd/IY4/J00pKkvGmriV+9JF0dkuX+vw+/vqL/AVd7NeuUrqZ5++hfXvP+dcesHUr+RCm8CIi2KmOLcR5wQJqpUqxReBGPhUoaQtpew7blUKv+Ptv2i1YAOPKl89RgT0y9A2mlavEvzYmeD1m1eZkhoRqDA7W2FhtpVaqlIx5z52TMewbb2SVRaz0/PjeNTyCqtyMJnz/faf9VivZuDGTAouzQwtbWPLgwdSUYtWA/yRK9y0Zq0zDg0xPzuSTT8pPe+45OVxvms+cEbVHL/+xVJplLlvmuJ1Uj45j/foe3pcbpk+X89u1c0z6ce+9otLYQ9HuusuROvDjj46TnXURfWDhHb5xzOsOYBeAINv/ewC0c97n6w3yYsmxhH9mppDu4YcljMA9ydNZizxyRHJChg4VhcnmBrFaJczt/HnXwVSvXmRUZSv79tFYrJhbvnt6uih7+lw048ZJowBIZ1uvnsPbmZIintXwcBmJzpwpZhJ35TIgQAb5tjAB3nuvNPRPPUU+/LDDO1yiBHnjjQ6lMi5OvipAYpV1E4umSdzysmXiapg6VX4UIKYTXekvVUpG5V4s4+zdW6xXeoeoN3Z9+khj98ADsj02Vjoom8WIgJjG779fHp6myfPv2lWu1cqWw9CjhzwbQBT3xo3lt82dK5Zk5+fjNsWKr4NfvdgFt24Vgg0ZkmtLow69uJNzQ+CEAuNGdotROBwTEyMEG+wU7WCLf+/dW1adnQEkHdY6gOOCXuJ9XRLZ54Z1TEApJkdW5aO3HiQgh6V8O48ZCOLhCs2Z0eBmtoViFKpzfY2+JMABtwlpv/qKDMclLvruIvUW/1iv5whoHPfaRSagFP+pLe7Y48fJZUW684IqyX/WnrfrP4sXS1RcHFbyrlZnWa2a0MjywjCb1/MfvvCCIyevbx/N3v7M+uAY0xDCOIA314n1+OGQpNWi2dMh9KIK34pxm506if4fGioK10+qBzUbbx7ANKm/omku1xg+3Oni7lESukn2xAk+3nANCfCZavP5xx/SJNSoIc2H3h/puUh6qs6998rf//5zOHZiY+WZ1Kvneqv+/WV7yZJiFXaGzxymjNOVkjF3uXIydiGlidU90F9gMBNuaMxjJRoQIGd8dkna0REjXK6l5/LoUwJ0707SANylgfhLEo1vrM8zRatx/6TljoeXnu7Q/osW5evDUjkB/2NSQDh/KXYPz6E0v5ro2xRmqamOruC77xzfg6bJO+7fn+TGjTxTtx1jsVH0rMuXyehoaqGh/Ln/DxIRm5GRNRerfHnpfywWKQIRGyvhHqNHyw1txQ2WLSMBjSsRx9TwMuR994lnxy23Li4ujjc2bMPyFays19DCP7fLYHbWwhT+sdmDZ3VZCucvTeHaHcn8fkEKa9a2sk5dK5f+Kft/+SOZjw5NZ8XKVgISibEezZgUUZErn5S0gjOqHFNUEWpHs5lH5tIlIcOkSfIcbpToD4aFZY08ePVVIbcexaa/x1xOR8LVq8l27RgXG+uZv2fOyHU//dSRoz50qOx7+GHZ9+yzua/CeffdMnZ54AFqAQHs0vA4AQm0oAG4S4PxNyYmhp07k/3uSrIbHVymUhowgGlFS7Jb7ElqERFkQACtQcEcUeVrnl+9UxwvtuXo4p3sVn0n/5m9UxIsNY0/fHKamQhkYo+BtEKxdnA1+R4uXaKWkcmePclPh+6jvf93MyTqmPSZxi5tLtNikanT1kEMMVPxIC/GZ9jHqCvQgX/WHMjTKMc/12gy9tY7Ih1nz4rMixdTK1WK8aGVuOyuT8muXRmnFBs0bOFdgd12mQklq5EAd4393utxMz6X767rXZn8FbZxavXq9oJZ/auvZUSEDNf1qC09nSlTBTExqBTvqrTRnqY/fLj4pnjkCLuU2ciHHrKdc+4cuWoV+/QRA27i2TS+3W4Ja9WQsYVen0svXfHnn/ZmmYCj/MPFi9J3hoXJdSIixDcVFhbHdu3iPL4TZ6yzBTUFBjocqfqjP3bESi5Zwj07M1ivTiZTq9clb7yRZ09mskP0CYmi0fUEt/QsD/CNY153ACMArLXF7G8DoGzbawFY6+sN8mIp6DnokpOTmWxr8PWBGyADJ49IShJLrvOBK1fKl1G7tow6K0jBGnbs6Agl0M+9cMGxOOeQDBgg50RESNhxRgaThwxh8siRDsU4OVlGkxUrioVp/HjfqjTNmSOWkqFDRbF77DGxjI0bJ6PUP/+UIgvLl4vi6oxhw0SuUaNEoXzxRRmkv/aaWNjatycffFCsvn/8IdpeSIgoprb8JVavLh1QsWIO6+y5c2KxuXxZmNaggRwbHCwazoULHr25zu8rW5w/L4wuXtzxrvSBjjN8CHG5fFk8SqGhjqjkCxfs/bDfO08aiMN2/lqtEspWrpw9P2vPHvnsslTDPnGCLF2al58Zwa53aGzcWMady97bKpETVaJ4f6sD7KXm0KICuRYtuXpRIpmQwENVGjPZ9n6PV25GQGxHISEySLZaKQNbmzFmUtgzfE1JJaKE5Y45To/9vI2ZCOS6kLbsedtlhofbUr527KIViitK9GCTJuT8z8/Id9yvHwcNks/qkUfkr3tdsb9ve56Hi1dn0uXsK3DqxZE56wYAABuFSURBVBF0T2NiojQjjRuLc+WXX2T88R5etH/L/eu4JmaPGydj9Gwj+3fvlvNfe43JtW5iWmARnvlXXsb69WJTql9fvKbR0Y7TkpKEzoDN+mrDQw+JjI0bi7XWGbpVGLDlzDjBZw5TapGEhjoM6s5T6V66YGFCEYel3fLiy2zTRnQYS1wH+UFOAzc9FOr4ccdcszQAd2kg/lLnsKcJy/VQUlsc/oZnvrc/+5O9hub0Kl1w8aJ815rm+j306SPd24ED0p02bOhkkz53ztGnfP21eFoAsfjo6NlT+hvd/aD3Nykp8vE2aEBmZlLTyGeqSM7m4eGfkxskgoOTJ7vImZyczB9/2Mk9NW9jC/zF9z5J43ufpDEgQGOPXhkuntaXXk1jSIhGpTROq/AiRwW/wYgSGgODNH5UdSz3Nb2HNcokECDrN8rkB+9r/OtO20c5fTotFvL38DtJgKtu8V440iP036uHt3iCptm9OSxW7IrzIL3y12KREfOLLzqsRXrxJotFwm0AIXONGmIRyynFSU94fvtte4WajDfftvsOaADu0oD8feUVGa4lt7pFnrXTu7YsEs/sVtws0Qd//skd4a3sXM52GTyYizpJKGzGtl3cW6Uj/0YNJi1ZSkZE8HSXB0RnCXpdlJgKFaQzd+uY0tLIqUX+x9MoxwXTEvh8d1F4UyMr04IAHu0h/fX+etI5WsOKcgG6ifdSD9W4/34Zv/30k91DT4DJlWryBhyS4te/rWMywD+f/8SrYrrtq+X2c3fX7+ExvPj4QJHnFbzFpa+JhXtvXVvHXa4c08OKMwgZHDxYbElRUbahpaZRq1SZJ1CR4x7ayf79pX07dcphW9Cj/MaMcf309Yinjz6SPlandmqqOHb12hwWi/T9zzwjWQHOev2XX8p252X5ct/6X32ae4B25frQIfmudEO5Pgb6sK2k7W2KfYwvwOY82rtXHFx16+aUcugbx7LdCbQAcDeAYk7b6gBo4usNvFy3C4C9AA4AeDmn4wtagXWGzovixel1igeS0hi8+658LXrDMHeudK6tWomy6DzS8gVHjsgo0b3spydoWl5OIpw9MjMd3t7gYFeF3Bt275aRd/Hioozqc554GhTpOHpURvDOuRlXi+eeE3PYtGmiWOsDlDFjJK6xbFkxL3XqJM99+nTRQjwYBc6dk4G9UpJOAkiUMguwY8ppMQKHs/DX10Ja3o7bvp0sU4Za2XLMRCD/RCtWK5XoONxqlRC1kyeZcCrdHmTQsKGboqxp9gGUBQHcXv2uLLc69v4PzEQgV6MNB95ty1HTIxgA8ea/8IK04Hv2MD3d4c0LCcma2k2r1Sevv17oKDvqWyzks+Ff2GV555VsQvWzg24oCgvLElLw+++OcYC7AU+vPpxT5UIdyx1jAm+RCz7jcVtkecuWHpq9lBR5/7YKe/r0YdPHnBBPU7FiYpn85hv2qf83mzVznPrOOyQNwFt9MQJ/6YnDzvjgA0dVOz35GRBPQx7gC8cnzsBACdt3QVqaDIpKlyY//FAOdE7g1re1ayfHOFdg02sjTJhAJiczKbIqd4c0Yspl27xYDRpI8vfp0y6JY0fuuJ8E+G9wA1atks6wMAkjDAzSOHtxCtdsS2b3ezIJkM1aWji2+1L7j/j9k9X88MkdzIBET+0NupHzxm7ir/P3O/qkXr3s44jfph/ne+Gj+N++XM75RcpId926nI/7+ef8q/DYubOEXXxsqyjtnsP79dfSiPTuLVFX9erJwOv06az9bmamWNSqV3fsi4uTohRWqz6Dlt95qy9G4u+JEzJcGznoRJYQzh++zeRplCMBLij9INeulemo7gtbwIdLzmHG93PIOXN4ccoc9g2ew/5hc9hLzeHFQc+QNm/iP6GNSJIr+kuRMC0oyN55tAzbyn2oxcM1Ojqiqz74QMbEtsJj80bvpgUSuv9dpRf4cak3aIXi5bU7eBG2aTVat2av7mk8GiTTzn1QcjTvvtv2I/QOqWVLaSiaNZNwqblzeW/HBHuA30cfamTNmjwb055rdyRz/Zrj3DllCXdOWcKNyw9w7Y5kbmw2mEkoymmBg5iuQrhu9XGu3ZLIv6ev4M4pS3j6LuH/kWIS5ZBZvCSPB0axR5dUe+WpZUW723NSf/6ZLrazjbMOMRJn+eOPDifZwIG2gq1FHV2yp5ItbdvKOLNYMdeC+wUF3XD97ruObfoUVwcPyrgnPJwMCtR4/hExsGcgiBsRK7UQPpcie/zmG3n/+uI6WPKNB74emFcLgEAABwHUABACYAeAetmdU9AK7MSJEzlx4kSS0ofcfDNdY9QLEm4KnrNsfsWJE2I1zSlx3hliWs9zUXL1TJwViLQ0RwU7QJIbBw2SkCb3cOroaMkHnjlTTE/jxpFHjzI5WXTiEX0PclOroZxf4XHSAB1nfi655XC+8HfHDrJsWWa2bMvb21ziyJGOXe7fw5gx8prdAwlIkprGY72fZ6oK44nF2zzeav87s5mJQF6o30oq6wESglyypAyeihRx0eJSUiQU9dFHPYvuy/f6zz8S7ZjTzE9j71hJAjyEG65cX/j4Y+kNvYR1/fyzhC27Fy1NSBB90DnNJTvExzvodMwtEjK37drhw1IZdvnyHA+lpontrHp1MvPYSUdoJcA0hHDWA4vdT/E7x/Jzyfc+uFo18bBcBZy/h7NnJX37rbeymf5uxw4xIhUpIiNA55BU3ZMKZB3xaZoYLkuVcqTHOBtMdaULkOu/+y4nvvQSJwC8dJPUwPgfJrBCJSunzUphaJjGLndmcsAgqUw6YFAGV29MZFLt+kytVJXpkeV56aZmPN/6VqaERPCd2JnMCHdKtwFcjeCFBNnyd/duUSiKFBGFJjsD3ooVYmDWn0WZMq6G/08/le3OidV6EaI33tCtWX7nWH4uV8PfgQOlqXd2cmua0HVKyeeYGRzGKBxlnTrSvc2fL49Wr905apSsL10qtoZnn6U9tGZyPSnp/fuPF/kBQjmuXBSP/LafCSjF+NIynnq2xBSmp9jmndPfcUQEtdVr+FvxHrwcUJwHG3RnKkJ5DFV4pKYUS/iwwru0QlFbvYYVKpATWkukx6hOf7hOdanztWNHu0Kkp2GPHi3+pBtuICd0uZ0TAO6avJBpZSvaZbEUDeeuzxbwQmBp/lSsDz9+SKaVW933A55v7ZiBhACPDXmZFSpkcn7U/0iA7970FcuUIbV166kpxSH43B7Sa7XKkLJpU5FVz2Y7fdq1jEz37vJM9XW9eLczfvzRsT+vVIHc9L8tW2aloD4sqlPHkVoXEEDWqa3Za1E8FzaRvXtTBg5hYa5tHiAPyDEHtW9c8PXAvFoAtASw1Gl9OIDh2Z1T0ApsbvKxChqGki01teC8vtngqp7JiRNiEXKfB03TZJR98KCUk61a1aXBtf+vV7MJDJTOuUQJ0gCdXH4uueVwvvE3OdnjQO+KvoecDCtz50qPHRQk7z8hwWHxDQjIVTXNvOTw8q+PkwBXFO165VTUNNG6CwBVqniOVMzvdk3v9F99lZz/QzqXTDzAt/vv5mY0oTU4RAbG8+bpJm+/cyw/l3zvg7dvl3bzKnBF38P/ZCBpd3voyMiwF4DyWMH3778d1eTuu891X2qqFBP87DN7mlBcYCDbBgVz/dpTvNA0jklhpfjnS9/y33Hf88MOs3g35vFuzOOYlrP4z7jvefxB8VL9O+477htl8z4APPT8GK7dkczNi3fzwIhPuP3ZDz24lwsHcnxfunGgadOcL3bwoOTxTpwooYbBweKtmz1bDA233OI67sjMdBS/bNGCNADH8nO5Gv5u2yaP6fHHHc2drvNN/jiFqXuOsGxZWR82TB5zw4biFJ87V1Ix9LDVfv0koG7OHDI65BCfe1beyZEjZAhasHLFtrzvPnJYgGhrluBQRuAiX3mFXPDtJf7xxu9cM3IJL1Wqw8wg8dRuuXsUk3YfYRoklnbfMEkcHTJY443F/7NPsDFxIsk9e+zeyylTHL9nyaf7OH9Whn29Rw+hf3y8QyGvVamZfaaRy8UrcPZDc/j9kIU8W95h3Jza50f+vj6J+wIkP1xTisu7vctvH1/CL5/bwJFvSeXfp55P45aFf/OJF1PlOU4m7264nzWrZboEkenzmH/yiQwZ9SrBeiV/QGw1hw9Lc6SU527ZYpGAA8A3A64vyE17q6exOs/BrBuJdSWcdASqNWpEagcP8YXnNQYGir1p6ad7+ccbv9uX9c/NYnqxkkwuU0XSUHzlgq8H5tUC4F4AXzmt3w9ggofjhgDYDGBz1apVfXqweQVDKYluMLJs/kKBPJNLl6RTXbNGRt/794vlccAACVV5+mlRhq8PC3COHL4m+Tt/vgymRtvmYbx4UULOBw3ym3ypKRpPBVTksrgciyIYAv36SYSVO/Kbw1arDMTcjb6t6p0X94PzRgNwLD+Xa7YPPndO+Pjii1n3de0q7hdvePZZMUw5PABZYbWSI0YwDmBM1dqSKzd3Iy2hHrwJbsv5Vp24dnsS1267zMSbWzCpVj3+tflCtvPAFibk+L7Onxft59lnc3fh8+fFQ64/y7Awz24pTXNM7G0AjuXncrX8vdXVkUhAylLoNty33xaFT88M0+cR1RdbzTNu3SpKlr7dFr5Nq5UMCoojEEeAfHSg5Jpr/fq5OF71pSzOcAsa82RAZaadkzSYNS1fYCKKM+WkuIqdZpYh4IgG0r2rOS1PPCHHWywSTAfEsRFK8BBuYE3stx9XGue4AU35X0AUV66VKXRmtRzDdASzF2Z5vPa0H6RA22ffXXZ5Hu7T2Ccl0W4cAKSGmY6uXV1nnRkwwPN8rTomT3bMW50XyE17O2GCeOfdo/vnz5fvQS8xsXGjKOJ6YfNjx1zSkrMsN2G7PYydPnJBTyovMCilegHoTPIR2/r9AJqRfMrbObGxsdy8eXNBiYj27dsDAFatWlVg9/QVRpbNXzDgM1H+FiA/kVsOX1P8vXABKFkSULZXfP48EB4OhIT4Tb4LRxMRXrYogosG58n18hOpqYCmAcWKuW4vCA4nJgJHj7puq1oVKFksE9izR/pRALjpJpO/big0HL54EShaNCsfU1Lk/bp/eDpI4NIloESJnGVr3RrnUgLwxfSlAIDghDMITjjruFUyEFYECAhwun2NG4GgIACAysyAslqhhRVxuW78RSvualfchx9pPPj0vry9m5xgtQo/rVagfHlZvOHMGaB8eZO/bnDmb3IycPCg6/6KFYGyZeV/TQMSEhzrJLB3L5CRIfSpWdNx3pEjQpvgYKBuXUe32Lp1e2RkAFOnrkKdOkBoWiIQGorE9LAsbTAAwGJB5ZLJKFND+KdlWnHx8AWUrhOZRYbixYHq1bPK4P15AdHRjs/uwgXgttva42JiJl5/8xdowa7fo7JaUS48CcWjIgAAVgtxcvclpBcpmeXaRYoClatIvxF/0YqbqxVHYqJQvW5d1zYAAOLjgVOn5P/oaCA0VP5PSZHnHh4u6+npskREeP5NJHDunOMdXS1y095arfK8S5XKuk/o5339xAn5trwhIOkSQk4eQZ17feuDg3w5KI9xHECU03oVyATPJkyYKBy4fjns3mqXLu0fOZxQqlrOg26joEiRnI/JL5QoAdx0k6c9wUDDhgUtjj9x7fK3ZNZBJgBRnLKDUj4prwBktA6rfTWzTHlklnFVqtKyOZ3BIaDxbU15D2/vJicEBgL16/t2bHbK7bWDq+JvsWLe2kFBQICrYqSUKGOecMMNnrcHB8tib1ZDhVslwrzdOwiAg38BwYF25fVKZfCGUqXkGaRaAlCjfjAAdydeAACH5hgYpBDVqISH47KiWrXs95ct61npdG+eQkMdyq0nKJV3ymtuERjoWXkFstLPfb1yZVm8IwJANh+nGwJyPiTPsQlAbaVUdaVUCIA+AH72gxwmTJi4MpgcNmGi8MLkrwkThRcmf02YAAo+hBgAlFJ3APgYUk1tKsm3czj+MqRkuL8RCeCcv4WwwSiymHJkRRjJBv4WIj+RGw4biL+Acb4TU46sMIosJn+zHm8UDhvlGzGKHIBxZDGKHCZ/sx5v8jcrjCKLKUdW+MRhvyiwuYVSajPJWFMOB4wiiylHVhhJFiPASM/DKLKYcmSFUWQxihxGglGeiSlHVhhFFlMO48Ioz8QocgDGkcWUIyt8lcUfIcQmTJgwYcKECRMmTJgwYcJErmEqsCZMmDBhwoQJEyZMmDBholCgsCiwX/hbABuMIgdgHFlMObLCSLIYAUZ6HkaRxZQjK4wii1HkMBKM8kxMObLCKLKYchgXRnkmRpEDMI4sphxZ4ZMshSIH1oQJEyZMmDBhwoQJEyZMmCgsHlgTJkyYMGHChAkTJkyYMHGdo9ApsEqpF5RSVEpF5nx0vtx/tFLqb6XUdqXUMqVUJT/J8b5Sao9Nlh+VUlc4S3ieyNJLKbVbKaUppQq8iplSqotSaq9S6oBS6uWCvr9NhqlKqbNKqV3+uH9hgclfF1kMwWGTv3Y5TA7nAH/z1yaDIThs8td+f5O/hQj+5rDJX4+ymBxG7jlcqBRYpVQUgFsBHPOjGO+TvInkzQAWAXjNT3IsB9CA5E0A9gEY7ic5AGAXgJ4AVhf0jZVSgQAmArgdQD0AfZVS9QpaDgBfA+jih/sWGpj8zQKjcNjkr+BrmBz2CoPwFzAOh03+mvwtVDAIh03+ZoXJYcHXyAWHC5UCC+AjAC8B8FviLslLTqvF/CULyWUkLbbV9QCq+EMOmyz/kvTXJNnNABwgeYhkBoAfANxV0EKQXA3gfEHft5DB5K+rLIbgsMlfgcnhHOF3/gLG4bDJXwAmfwsb/M5hk78eZTE5jNxzOCgfZclTKKW6AzhBcodSyt+yvA1gIIBEAB38KoxgEIBZ/hbCT6gM4D+n9eMAmvtJFhNeYPI3R1yvHDb5WwhgJP7a5DEah03+Ckz+GhRG4rDJX0Oh0HLYUAqsUuo3ABU87BoB4BUAt/lbDpILSI4AMEIpNRzAkwBe94cctmNGALAA+C4/ZMiNLH6Cp5bYLK3tB5j8zb0stmPyncMmf03kBKPwNydZrsc+2OSvCV9gFA6b/L0yWfyEQsthQymwJDt52q6UagigOgDdclQFwFalVDOSpwtKDg/4HsBi5BP5cpJDKfUAgDsB3MJ8ng8pF8+koHEcQJTTehUAJ/0ky3UNk7+5l6WgOGzy10ROMAp/s5PFA66LPtjkrwlfYBQOm/zNvSx+RKHlsKEUWG8guRNAOX1dKXUEQCzJcwUti1KqNsn9ttXuAPYUtAw2OboAGAYgjmSKP2QwCDYBqK2Uqg7gBIA+APr5VyQTzjD561UWk8Mmfw0PI/HXdn9DcNjkLwCTv4UCRuKwyV/DodByuLAVcTIC3lVK7VJK/Q0Jx3jaT3JMAFAcwHJbOfLP/SQHlFJ3K6WOA2gJYLFSamlB3duWhP8kgKUA/gUwm+Tugrq/DqXUTADrAEQrpY4rpR4uaBlM+ASj8BcwCIdN/gpMDhcaGIXDJn9N/prIPUz+usHksCC3HFb57DU3YcKECRMmTJgwYcKECRMm8gSmB9aECRMmTJgwYcKECRMmTBQKmAqsCRMmTJgwYcKECRMmTJgoFDAVWBMmTJgwYcKECRMmTJgwUShgKrAmTJgwYcKECRMmTJgwYaJQwFRgTZgwYcKECRMmTJgwYcJEoUChmAfW6FBKlQGwwrZaAYAVQLxtPYVkqzy+380AKpH8JS+v6w8opT4GMJ/kai/7nwSQTHJawUpm4nqCyeErh8lhE/6Gyd8rh8lfE/6Gyd8rx/XMX3ManTyGUuoNAEkkP8jHezwImYT6yfy6R0FAKVUawC8kW2RzTFEAa0k2LjjJTFzPMDnsO0wOmzAaTP76DpO/JowGk7++43rnrxlCnM9QSiXZ/rZXSv2hlJqtlNqnlHpXKdVfKbVRKbVTKVXTdlxZpdQ8pdQm29La7XohAEYB6G2bfLm3Uqq0UuonpdTfSqn1SqmbPMhR1Hbvv5VSs5RSG5RSsbZ9fW0y7FJKvefldzRVSv2llNphk7m4UupBpdQEp2MWKaXa2/6/TSm1Tim1VSk1RykV7uGy9wJY4nT+u0qpf2wyfgAAJFMAHFFKNcvNczdhIq9gctjksInCC5O/Jn9NFF6Y/DX56w1mCHHBohGAGwGcB3AIwFckmymlngbwFIBnAHwC4COSfyqlqgJYajsHAEAyQyn1GpysR0qpTwFsI9lDKdURwDcAbna79/8AXCB5k1KqAYDttnMrAXgPQAyACwCWKaV6kPxJP9FG+FkAepPcpJSKAJDq7UcqpSIBjATQiWSyUmoYgOcgjYYzWgOYazunNIC7AdQlSaVUSafjNgNoC2Cjt3uaMFFAMDnsCpPDJgoTTP66wuSvicIEk7+uuK75ayqwBYtNJE8BgFLqIIBltu07AXSw/d8JQD2llH5OhFKqOMnL2Vy3DYB7AIDk70qpMkqpEiQT3Y75xHbMLqXU37btTQGsIhlvk+s7AO0A/OR0bjSAUyQ32c6/ZDvWmzwtANQDsNZ2TAiAdR6OqwhHnsMlAGkAvlJKLQawyOm4swDqZvP7TZgoKJgcdoXJYROFCSZ/XWHy10RhgslfV1zX/DUV2IJFutP/mtO6Bse7CADQkqRX64wHeGKBe3KzN6Z4ZZDbMZ6SpS1wDUMPczp+Ocm+OVw3VT+HpMUW4nALgD4AngTQ0em6uXkeJkzkF0wOu8LksInCBJO/rjD5a6IwweSvK65r/po5sMbDMsiHB8BeLc0dlwEUd1pfDaC/7fj2AM7pFh4n/AngPtsx9QA0tG3fACBOKRWplAoE0BfAH27n7gFQSSnV1HZ+caVUEIAjAG5WSgUopaIA6DH26wG0VkrV+n9798vSUBTGcfx7wspQLFqN64LN4guwL4lGLeKfIAbBaLAJvgWzoEllCAqm4UQ0+gIs2sRyDPcMJlzrdg73+0mDPWc7C7/w3D3n3lTfDiF0an7HGzCsmQJm0l3htvk7vtEBXmrWSzkyw2ZY5TK/5lflMr8Nya8NbH62gMVQHcJ+BTZqanpUIxJPIYQucDRcAxwDazVrzoC5VLMPPANfaRzjIH3mAOjHGC9GF8YYf4AucBpCGADXVFd0HoB3qvGNE6Cf6j+AdeA8fd8j9eMLV8Byej0NXKb6O2BnpG4JuKlZL+XIDJthlcv8ml+Vy/w2JL8+Rqch0pWhVozxO1R3a7sFOilYk9zXPbASY/z85/0FYDfGuDrenUl5McNSucyvVC7zmx/PwDZHG+iFEFpU8/Wbkw5esgfMA7XhA2aBw/FtR8qWGZbKZX6lcpnfzPgPrCRJkiSpCJ6BlSRJkiQVwQZWkiRJklQEG1hJkiRJUhFsYCVJkiRJRbCBlSRJkiQVwQZWkiRJklSEXz2w8JVXzQ5DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_counts = 100\n",
    "unit_count = len(sess_spiketimes)\n",
    "fig, axs = plt.subplots(2, unit_count, figsize=(16, 3))\n",
    "fig.subplots_adjust(wspace=0.4)\n",
    "\n",
    "for ax, k, is_stim in zip(axs.T, sess_spiketimes.keys(), (False, False, True, True)):\n",
    "    plot_spike_raster_and_histogram(sess_spiketimes[k]['contra'], sess_spiketimes[k]['ipsi'], \n",
    "                                    axes=ax, ax_title=k, bin_counts=bin_counts,\n",
    "                                    xlim=(-4, 1), shade_bar=is_stim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Inagaki 2018 - Reproduce Figure 4 a,b,c \n",
    "### Fixed Delay Tactile Task (1.2s) and Auditory Task (1.2s or 2s)\n",
    "\n",
    "This figure features the neural activity in response to 3 different task categories:\n",
    "+ Tactile task with 1.2s delay duration\n",
    "+ Auditory task with 1.2s delay duration\n",
    "+ Auditory task with 2s delay duration\n",
    "\n",
    "All without optogenetic perturbations and groups into **ipsi** vs. **contra** trials\n",
    "\n",
    "Leveraging the 2 helper functions above, now we only need to specify the queries for those 3 task conditions appropriately. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify segmentation setting to align to \"cue-onset\" event\n",
    "seg_param_key = (analysis.TrialSegmentationSetting & {'event': 'cue_start',\n",
    "                                                      'pre_stim_duration': 4,\n",
    "                                                      'post_stim_duration': 2}).fetch1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trial-restrictor to all good trials, regarless of correct/incorrect response or photostim status\n",
    "contra_trial  = {'trial_type': 'lick right', 'trial_is_good': True}\n",
    "ipsi_trial  = {'trial_type': 'lick left', 'trial_is_good': True}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Restrict to extracellular sessions of type \"Tactile Task\" and \"Auditory Task\"\n",
    "\n",
    "With this, we will group all tactile-task sessions in `tactile_ec_sess` and auditory-task sessions in `auditory_ec_sess`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tactile_ec_sess = (acquisition.Session \n",
    "                & (acquisition.Session.ExperimentType & 'experiment_type=\"Tactile Task\"').fetch('KEY')\n",
    "                & (acquisition.Session.ExperimentType & 'experiment_type=\"extracellular\"').fetch('KEY'))\n",
    "auditory_ec_sess = (acquisition.Session \n",
    "                & (acquisition.Session.ExperimentType & 'experiment_type=\"Auditory Task\"').fetch('KEY')\n",
    "                & (acquisition.Session.ExperimentType & 'experiment_type=\"extracellular\"').fetch('KEY'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a convenient function to evoke `query_unit_segmented_spiketimes()` for different trial condition (e.g. ipsi, contra) and task type (e.g. tactile, auditory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sess_spiketimes_alltrials(sess_key, unit_num, contra, ipsi):\n",
    "    return {f'{s_id[\"session_id\"]}_{u_id}': {k: query_unit_segmented_spiketimes(sess_key=s_id,\n",
    "                                                                  unit=u_id,\n",
    "                                                                  trial_key=trial_k,\n",
    "                                                                  seg_param_key=seg_param_key) \n",
    "                               for k, trial_k in zip(('contra', 'ipsi'), (contra, ipsi))} \n",
    "            for s_id, u_id in zip(sess_key, unit_num)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tactile Task - Delay 1.2s - plot 6 sample units (randomly selected)\n",
    "\n",
    "At this point, for sessions of **tactile task**, we need to specify one more trial condition, `delay_duration`, to limit to the trials with delay duration of interest (here **1.2 sec**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = (tactile_ec_sess & (acquisition.TrialSet.Trial & 'delay_duration = 1.2')).fetch('KEY')[0:6]\n",
    "tactile_short_spiketimes = get_sess_spiketimes_alltrials(sess_key=sess, unit_num=np.random.randint(len(sess), size=len(sess)),\n",
    "                                                         contra=contra_trial, ipsi=ipsi_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_counts = 100\n",
    "unit_count = len(tactile_short_spiketimes)\n",
    "fig, axs = plt.subplots(2, unit_count, figsize=(16, 3))\n",
    "fig.subplots_adjust(wspace=0.4)\n",
    "\n",
    "for ax, k in zip(axs.T, tactile_short_spiketimes.keys()):\n",
    "    plot_spike_raster_and_histogram(tactile_short_spiketimes[k]['contra'], tactile_short_spiketimes[k]['ipsi'], \n",
    "                                    axes=ax, ax_title=k, bin_counts=bin_counts, xlim=(-3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auditory Task - Delay 1.2s - plot 6 sample units\n",
    "\n",
    "Similar to above, with **auditory task**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = (auditory_ec_sess & (acquisition.TrialSet.Trial & 'delay_duration = 1.2')).fetch('KEY')[4:11]\n",
    "auditory_short_spiketimes = get_sess_spiketimes_alltrials(sess_key=sess, unit_num=np.random.randint(len(sess), size=len(sess)),\n",
    "                                                          contra=dict(contra_trial, delay_duration=1.2),\n",
    "                                                         ipsi=dict(ipsi_trial, delay_duration=1.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_counts = 100\n",
    "unit_count = len(auditory_short_spiketimes)\n",
    "fig, axs = plt.subplots(2, unit_count, figsize=(16, 3))\n",
    "fig.subplots_adjust(wspace=0.4)\n",
    "\n",
    "for ax, k in zip(axs.T, auditory_short_spiketimes.keys()):\n",
    "    plot_spike_raster_and_histogram(auditory_short_spiketimes[k]['contra'], auditory_short_spiketimes[k]['ipsi'], \n",
    "                                    axes=ax, ax_title=k, bin_counts=bin_counts, xlim=(-3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auditory Task - Delay 2s - plot 6 sample units\n",
    "Similar to above, with delay duration of **2 sec**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = (auditory_ec_sess & (acquisition.TrialSet.Trial & 'delay_duration = 2')).fetch('KEY')[16:22]\n",
    "auditory_long_spiketimes = get_sess_spiketimes_alltrials(sess_key=sess, unit_num=np.random.randint(len(sess), size=len(sess)),\n",
    "                                                          contra=dict(contra_trial, delay_duration=2),\n",
    "                                                         ipsi=dict(ipsi_trial, delay_duration=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_counts = 100\n",
    "unit_count = len(auditory_long_spiketimes)\n",
    "fig, axs = plt.subplots(2, unit_count, figsize=(16, 3))\n",
    "fig.subplots_adjust(wspace=0.4)\n",
    "\n",
    "for ax, k in zip(axs.T, auditory_long_spiketimes.keys()):\n",
    "    plot_spike_raster_and_histogram(auditory_long_spiketimes[k]['contra'], auditory_long_spiketimes[k]['ipsi'], \n",
    "                                    axes = ax, ax_title=k, bin_counts=bin_counts, xlim=(-4, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inagaki 2019 - Reproduce Extended Data Figure 8k - extracellular - fixed 2 second delay\n",
    "#### Display spike rates and PSTH for trials with fixed delay duration of 2 seconds\n",
    "\n",
    "This figure is relatively similar to Figure 5a in Inagaki 2019 paper plotted above. However, this figure expands onto neural response comparision between with and without optogenetic perturbations in fixed 2-second delay task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify segmentation setting to align to \"delay-onset\" event\n",
    "seg_param_key = (analysis.TrialSegmentationSetting & {'event': 'delay_start',\n",
    "                                                      'pre_stim_duration': 2,\n",
    "                                                      'post_stim_duration': 4}).fetch1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define query for trial-segmented spiketimes for a single unit in the one specifed session:\n",
    "1. restrict stimulation power > 0.05 mW\n",
    "2. delay duration of 2 sec only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_unit_segmented_spiketimes(sess_key, unit, trial_key, seg_param_key):\n",
    "    data_keys = (extracellular.TrialSegmentedUnitSpikeTimes & sess_key & {'unit_id': unit} & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & trial_key & 'delay_duration=2') &\n",
    "                 (stimulation.TrialPhotoStimParam & ['photo_stim_power is NULL', 'photo_stim_power > 0.05'])).fetch('KEY')\n",
    "    return pd.DataFrame([dict(**dict(zip(*(analysis.RealignedEvent.RealignedEventTime & k).fetch('trial_event', 'realigned_event_time'))), \n",
    "                              segmented_spike_times=(extracellular.TrialSegmentedUnitSpikeTimes & k).fetch1('segmented_spike_times'),\n",
    "                              delay_duration=(acquisition.TrialSet.Trial & k).fetch1('delay_duration')) \n",
    "                         for k in data_keys])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots for example units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example unit ***11***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess_spiketimes = get_sess_spiketimes(sess_key=extracellular.UnitSpikeTimes & {'session_id': 'HI152_060218'},\n",
    "                                      unit_num=11)\n",
    "# spike raster and histogram - Fig 6 - fixed delay duration of 2s\n",
    "bin_counts = 100\n",
    "unit_count = len(sess_spiketimes)\n",
    "fig, axs = plt.subplots(2, unit_count, figsize=(16, 3))\n",
    "fig.subplots_adjust(wspace=0.4)\n",
    "\n",
    "for ax, k, is_stim in zip(axs.T, sess_spiketimes.keys(), (False, False, True, True)):\n",
    "    plot_spike_raster_and_histogram(sess_spiketimes[k]['contra'], sess_spiketimes[k]['ipsi'], \n",
    "                                    axes = ax, ax_title=k, bin_counts=bin_counts, shade_bar=is_stim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example unit ***21***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess_spiketimes = get_sess_spiketimes(sess_key=extracellular.UnitSpikeTimes & {'session_id': 'HI152_060218'},\n",
    "                                      unit_num=21)\n",
    "# spike raster and histogram - Fig 6 - fixed delay duration of 2s\n",
    "bin_counts = 100\n",
    "unit_count = len(sess_spiketimes)\n",
    "fig, axs = plt.subplots(2, unit_count, figsize=(16, 3))\n",
    "fig.subplots_adjust(wspace=0.4)\n",
    "\n",
    "for ax, k, is_stim in zip(axs.T, sess_spiketimes.keys(), (False, False, True, True)):\n",
    "    plot_spike_raster_and_histogram(sess_spiketimes[k]['contra'], sess_spiketimes[k]['ipsi'], \n",
    "                                    axes = ax, ax_title=k, bin_counts=bin_counts, shade_bar=is_stim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inagaki 2019 - Reproduce Extended Data Figure 8k - extracellular - random delay\n",
    "\n",
    "This next set of figures presents the spiking pattern from the same unit above, in response to varying delay duration, limited to control trials only.\n",
    "\n",
    "Again, several steps are taken to reproduce the figure:\n",
    "\n",
    "1. Extract trials with different delay_duration \n",
    "2. Sort trials based on delay_duration\n",
    "3. Display spike raster and PSTH \n",
    "\n",
    "Let's construct several helper functions as above, with a few minor tweaks (namely the sorting of trials based on delay duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spike_raster_and_histogram(contra_spike_times, ipsi_spike_times, axes, ax_title='', bin_counts=200):\n",
    "    # get event timing\n",
    "    events = ['sampling_start', 'delay_start'] \n",
    "    event_times = np.around([np.median(np.hstack([ipsi_spike_times[e], contra_spike_times[e]])) for e in events], 4)\n",
    "    \n",
    "    # restructure data for spike raster  \n",
    "    ipsi_c_trial_idx, ipsi_c_spike_times, ipsi_c_cue_start = zip(*((np.full_like(r[1].segmented_spike_times, r_idx),\n",
    "                                                  r[1].segmented_spike_times, \n",
    "                                                  np.full_like(r[1].segmented_spike_times, r[1].cue_start))\n",
    "                                            for r_idx, r in enumerate(ipsi_spike_times.iterrows())))                                        \n",
    "    ipsi_c_trial_idx = np.hstack(ipsi_c_trial_idx)\n",
    "    ipsi_c_spike_times = np.hstack(ipsi_c_spike_times)  \n",
    "    ipsi_c_cue_start = np.hstack(ipsi_c_cue_start)\n",
    "    \n",
    "    contra_c_trial_idx, contra_c_spike_times, contra_c_cue_start = zip(*((np.full_like(r[1].segmented_spike_times, r_idx),\n",
    "                                                  r[1].segmented_spike_times, \n",
    "                                                  np.full_like(r[1].segmented_spike_times, r[1].cue_start))\n",
    "                                            for r_idx, r in enumerate(contra_spike_times.iterrows())))                                        \n",
    "    contra_c_trial_idx = np.hstack(contra_c_trial_idx)\n",
    "    contra_c_spike_times = np.hstack(contra_c_spike_times)  \n",
    "    contra_c_cue_start = np.hstack(contra_c_cue_start) \n",
    "    \n",
    "    # spiketime histogram\n",
    "    time_range = (-2, 4) \n",
    "\n",
    "    # select only spike times prior to go-cue (cue_start) to compute the PSTH\n",
    "    ipsi_spk_rates = np.nanmean(np.vstack([next(map(lambda x: np.where(x[1][1:] < r.cue_start, x[0]/np.diff(x[1]), np.nan), \n",
    "                                                    [np.histogram(r.segmented_spike_times,\n",
    "                                                                  bins=bin_counts, range=(time_range[0], time_range[-1]))]))\n",
    "                                           for _, r in ipsi_spike_times.iterrows()]), axis=0)\n",
    "    ipsi_edges = np.histogram_bin_edges(ipsi_spike_times.segmented_spike_times[0],\n",
    "                                        bins=bin_counts, range=(time_range[0], time_range[-1]))\n",
    "    \n",
    "    contra_spk_rates = np.nanmean(np.vstack([next(map(lambda x: np.where(x[1][1:] < r.cue_start, x[0]/np.diff(x[1]), np.nan), \n",
    "                                                [np.histogram(r.segmented_spike_times,\n",
    "                                                              bins=bin_counts, range=(time_range[0], time_range[-1]))]))\n",
    "                                       for _, r in contra_spike_times.iterrows()]), axis=0)\n",
    "    contra_edges = np.histogram_bin_edges(contra_spike_times.segmented_spike_times[0],\n",
    "                                        bins=bin_counts, range=(time_range[0], time_range[-1]))\n",
    "    \n",
    "    # plot\n",
    "    # spike raster\n",
    "    ax_top = axes[0]\n",
    "    ax_top.plot(contra_c_spike_times, contra_c_trial_idx , '|b', markersize=2)\n",
    "    ax_top.plot(ipsi_c_spike_times, ipsi_c_trial_idx + contra_c_trial_idx.max(), '|r', markersize=2)\n",
    "    # cue-start times\n",
    "    ax_top.plot(contra_c_cue_start, contra_c_trial_idx , '|k', markersize=1)\n",
    "    ax_top.plot(ipsi_c_cue_start, ipsi_c_trial_idx + contra_c_trial_idx.max(), '|k', markersize=1)\n",
    "    # event markers\n",
    "    for e in event_times:  \n",
    "        ax_top.axvline(x=e, linestyle='--', color='k')\n",
    "    ax_top.set_xticklabels([])\n",
    "    ax_top.set_yticklabels([])\n",
    "    ax_top.set_ylabel(ax_title)\n",
    "    ax_top.set_xlim(time_range);\n",
    "    \n",
    "    # spike histogram\n",
    "    ax_bot = axes[1]\n",
    "    ax_bot.plot(contra_edges[1:], contra_spk_rates, 'b')\n",
    "    ax_bot.plot(ipsi_edges[1:], ipsi_spk_rates, 'r')\n",
    "    for e in event_times:\n",
    "        ax_bot.axvline(x=e, linestyle='--', color='k')\n",
    "    ax_bot.set_xlim(time_range);\n",
    "    ax_bot.set_xlabel('Time from delay onset (s)')\n",
    "    ax_bot.set_ylabel('Spikes per s')\n",
    "    \n",
    "    # Hide the spines\n",
    "    ax_top.spines['right'].set_visible(False)\n",
    "    ax_top.spines['top'].set_visible(False)\n",
    "    ax_top.spines['left'].set_visible(False)\n",
    "    ax_top.spines['bottom'].set_visible(False)\n",
    "    ax_bot.spines['right'].set_visible(False)\n",
    "    ax_bot.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define query for trial-segmented spiketimes for a single unit in the one specifed session:\n",
    "1. restrict stimulation power > 0.05 mW\n",
    "2. query also the `delay_duration`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get trial-segmented spiketimes for a single unit in the one specifed session - get also delay_duration and restrict stim_power>0.05\n",
    "def query_unit_segmented_spiketimes(sess_key, unit, trial_key, seg_param_key):\n",
    "    data_keys = (extracellular.TrialSegmentedUnitSpikeTimes & sess_key & {'unit_id': unit} & seg_param_key &\n",
    "                 (acquisition.TrialSet.Trial & trial_key) &\n",
    "                 (stimulation.TrialPhotoStimParam & ['photo_stim_power is NULL', 'photo_stim_power > 0.05'])).fetch('KEY')\n",
    "    return pd.DataFrame([dict(**dict(zip(*(analysis.RealignedEvent.RealignedEventTime & k).fetch('trial_event', 'realigned_event_time'))), \n",
    "                              segmented_spike_times=(extracellular.TrialSegmentedUnitSpikeTimes & k).fetch1('segmented_spike_times'),\n",
    "                              delay_duration=(acquisition.TrialSet.Trial & k).fetch1('delay_duration')) \n",
    "                         for k in data_keys])\n",
    "\n",
    "# shift to delay-onset and add a column with sorted trial idx based on delay-duration\n",
    "def conditioning(data):\n",
    "    data = data.sort_values(by='delay_duration', ascending=False)\n",
    "    return data\n",
    "\n",
    "def get_sess_sorted_spiketimes(sess_key, unit_num):\n",
    "    return dict(correct_ctrl={k: conditioning(query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                              unit=unit_num,\n",
    "                                                                              trial_key=trial_k,\n",
    "                                                                              seg_param_key=seg_param_key)) \n",
    "                              for k, trial_k in zip(('contra', 'ipsi'), (correct_contra_trial_ctrl, correct_ipsi_trial_ctrl))},\n",
    "                incorrect_ctrl={k: conditioning(query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                                       unit=unit_num,\n",
    "                                                                                       trial_key=trial_k,\n",
    "                                                                                       seg_param_key=seg_param_key)) \n",
    "                                for k, trial_k in zip(('contra', 'ipsi'), (incorrect_contra_trial_ctrl, incorrect_ipsi_trial_ctrl))},\n",
    "                correct_stim={k: conditioning(query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                              unit=unit_num,\n",
    "                                                                              trial_key=trial_k,\n",
    "                                                                              seg_param_key=seg_param_key)) \n",
    "                              for k, trial_k in zip(('contra', 'ipsi'), (correct_contra_trial_stim, correct_ipsi_trial_stim))},\n",
    "                incorrect_stim={k: conditioning(query_unit_segmented_spiketimes(sess_key=sess_key,\n",
    "                                                                                unit=unit_num,\n",
    "                                                                                trial_key=trial_k,\n",
    "                                                                                seg_param_key=seg_param_key)) \n",
    "                                for k, trial_k in zip(('contra', 'ipsi'), (incorrect_contra_trial_stim, incorrect_ipsi_trial_stim))}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots for example units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example unit ***11***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess_spiketimes = get_sess_sorted_spiketimes(sess_key=extracellular.UnitSpikeTimes & {'session_id': 'HI152_060218'},\n",
    "                                      unit_num=11)\n",
    "bin_counts = 150\n",
    "fig, axs = plt.subplots(2, 1, figsize=(6, 6))\n",
    "plot_spike_raster_and_histogram(sess_spiketimes['correct_ctrl']['contra'], sess_spiketimes['correct_ctrl']['ipsi'], \n",
    "                                axes = axs,\n",
    "                                ax_title='correct_ctrl', bin_counts=bin_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example unit ***21***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess_spiketimes = get_sess_sorted_spiketimes(sess_key=extracellular.UnitSpikeTimes & {'session_id': 'HI152_060218'},\n",
    "                                      unit_num=21)\n",
    "bin_counts = 150\n",
    "fig, axs = plt.subplots(2, 1, figsize=(6, 6))\n",
    "plot_spike_raster_and_histogram(sess_spiketimes['correct_ctrl']['contra'], sess_spiketimes['correct_ctrl']['ipsi'], \n",
    "                                axes = axs,\n",
    "                                ax_title='correct_ctrl', bin_counts=bin_counts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}