notebooks/Guo-Inagaki-2017-examples.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Guo, Inagaki et al., 2017 - A DataJoint example\n",
    "\n",
    "The data and results presented in this notebook pertain to the paper:\n",
    ">Zengcai V. Guo, Hidehiko K. Inagaki, Kayvon Daie, Shaul Druckmann, Charles R. Gerfen & Karel Svoboda. \"Maintenance of persistent activity in a frontal thalamocortical loop\" (2017) Nature\n",
    "(https://dx.doi.org/10.1038/nature22324)\n",
    "\n",
    "The study sheds new insights into the persistnent neural activity of the frontal thalamocortical circuit, suggesting that reciprocal excitation between the anterior lateral motor cortex (ALM) and the thalamus is needed to maintain persistent activity in motor preparation. Intracellular and extracellular recordings were performed on mouse ALM and thalamus neurons during tectile discrimination task with and without optogenetic perturbation. The study reported near-complete collapse of ALM activity upon photoinhibition of the thalamus, and photoinhibition of the ALM resulted in diminished thalamic activity. \n",
    "\n",
    "This notebook provides demonstrations of working with a ***DataJoint*** data pipeline in querying data, apply data conditioning and reproduce some key figures in the paper. The orignal data available in NWB 2.0 format had been ingested into a DataJoint data pipeline (data pipeline schema is given below). As a validation of complete ingestion of the original data into DataJoint, figures 3b,e, 6b,e and 4b,e,h will be reproduced in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the data\n",
    "\n",
    "Data consists of both extracellular and whole-cell recording at the ALM and thalamus regions. The recordings were performed on mice performing tactile discrimination task with and without unilateral photoinhibition. The behavioral data includes detailed description of the trial structure (e.g. trial timing, trial instruction, trial response) as well as the lick traces (both for left and right licks).\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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting root@127.0.0.1:3306\n"
     ]
    }
   ],
   "source": [
    "# import necessary Python packages including DataJoint\n",
    "\n",
    "from datetime import datetime\n",
    "import os\n",
    "os.chdir('..')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "import datajoint as dj\n",
    "\n",
    "from pipeline import (reference, subject, acquisition, stimulation, analysis,\n",
    "                      intracellular, extracellular, behavior, utilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The architecture of this data pipeline\n",
    "\n",
    "This data pipeline is comprised of three core *components*:\n",
    "\n",
    "+ **subject**: the meta information about the animal, e.g. genetic information, virus injection\n",
    "+ **acquisition**: the organization of the experiment - session acquisition, trial structure, behavior data\n",
    "+ **electrophysiology**: intracellular and extracellular recordings, as well as results of spike sorting"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
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       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node1\"><title>subject.Subject</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"subject_id           \r",
       "------------------------------\r",
       "→ subject.Species\r",
       "→ reference.AnimalSource\r",
       "sex=&quot;U&quot;              \r",
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       "<g class=\"node\" id=\"node4\"><title>subject.Subject.Allele</title>\n",
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       "</g>\n",
       "<!-- subject.AlleleAlias -->\n",
       "<g class=\"node\" id=\"node5\"><title>subject.AlleleAlias</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"allele_alias         \r",
       "------------------------------\r",
       "→ subject.Allele\r",
       "\">\n",
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       "</g>\n",
       "<!-- subject.Allele -->\n",
       "<g class=\"node\" id=\"node6\"><title>subject.Allele</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"allele               \r",
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       "<datajoint.diagram.Diagram at 0x118558b92b0>"
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     "execution_count": 2,
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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": 3,
   "metadata": {},
   "outputs": [
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       "                <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=\"nonprimary\">species</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">animal_source</p>\n",
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       "                                <p id=\"nonprimary\">date_of_birth</p>\n",
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       "                                <p id=\"nonprimary\">subject_description</p>\n",
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       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm00229327</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229329</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00234218</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00237800</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Charles River</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00239123</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00242747</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Charles River</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00260487</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 31</p></div>\n",
       "            "
      ],
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       "*subject_id    species        animal_source  sex     date_of_birth  subject_descri\n",
       "+------------+ +------------+ +------------+ +-----+ +------------+ +------------+\n",
       "anm00229327    Mus musculus   Jackson        M       None           None          \n",
       "anm00229329    Mus musculus   Jackson        M       None           None          \n",
       "anm00234218    Mus musculus   Jackson        M       None           None          \n",
       "anm00237800    Mus musculus   Charles River  M       None           None          \n",
       "anm00239123    Mus musculus   Jackson        M       None           None          \n",
       "anm00242747    Mus musculus   Charles River  M       None           None          \n",
       "anm00260487    Mus musculus   Jackson        M       None           None          \n",
       "   ...\n",
       " (Total: 31)"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
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       "<title>%3</title>\n",
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       "<!-- 1 -->\n",
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       "<g class=\"node\" id=\"node16\"><title>acquisition.TrialSet.EventTime</title>\n",
       "<g id=\"a_node16\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
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       "<g class=\"node\" id=\"node2\"><title>reference.TrialType</title>\n",
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       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node6\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node6\"><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",
       "\">\n",
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       "</g>\n",
       "<!-- reference.TrialType&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>reference.TrialType-&gt;acquisition.TrialSet.Trial</title>\n",
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       "</a>\n",
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       "</g>\n",
       "<!-- acquisition.Session.ExperimentType -->\n",
       "<g class=\"node\" id=\"node19\"><title>acquisition.Session.ExperimentType</title>\n",
       "<g id=\"a_node19\"><a xlink:title=\"→ acquisition.Session\r",
       "→ acquisition.ExperimentType\r",
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       "</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",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session.Experimenter -->\n",
       "<g class=\"node\" id=\"node9\"><title>acquisition.Session.Experimenter</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Experimenter\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"968,-169 806,-169 806,-150 968,-150 968,-169\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"887\" y=\"-157\">acquisition.Session.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Experimenter&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>reference.Experimenter-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M990.425,-212.973C963.235,-199.178 925.562,-180.064 903.991,-169.121\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"node\" id=\"node5\"><title>stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ stimulation.PhotoStimulation\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_photostim  \r",
       "\">\n",
       "<ellipse cx=\"186\" 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=\"186\" y=\"-14.4\">stimulation.TrialSegmentedPhotoStimulus</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>acquisition.TrialSet.Trial-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M536.551,-78.97C444.162,-63.033 250.039,-29.5467 198.685,-20.6882\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.TrialPhotoStimInfo -->\n",
       "<g class=\"node\" id=\"node8\"><title>stimulation.TrialPhotoStimInfo</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
       "------------------------------\r",
       "photo_stim_type      \r",
       "photo_stim_period    \r",
       "photo_stim_power     \r",
       "photo_loc_galvo_x    \r",
       "photo_loc_galvo_y    \r",
       "photo_loc_galvo_z    \r",
       "\">\n",
       "<ellipse cx=\"810\" cy=\"-17.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"106.001\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"810\" y=\"-14.4\">stimulation.TrialPhotoStimInfo</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;stimulation.TrialPhotoStimInfo -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>acquisition.TrialSet.Trial-&gt;stimulation.TrialPhotoStimInfo</title>\n",
       "<path d=\"M613.917,-78.9006C650.977,-67.4847 716.983,-47.1525 762.088,-33.2586\" 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=\"edge7\"><title>acquisition.TrialSet.Trial-&gt;acquisition.TrialSet.EventTime</title>\n",
       "<path d=\"M562.071,-78.9006C525.178,-65.6423 454.82,-40.3573 417.928,-27.0991\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"node\" id=\"node21\"><title>behavior.TrialSegmentedLickTrace</title>\n",
       "<g id=\"a_node21\"><a xlink:title=\"→ behavior.LickTrace\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_lt_left    \r",
       "segmented_lt_right   \r",
       "\">\n",
       "<ellipse cx=\"586\" 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=\"586\" y=\"-14.4\">behavior.TrialSegmentedLickTrace</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>acquisition.TrialSet.Trial-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M586,-78.7611C586,-66.5766 586,-44.4319 586,-30.6473\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulation -->\n",
       "<g class=\"node\" id=\"node7\"><title>stimulation.PhotoStimulation</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ acquisition.Session\r",
       "photostim_datetime   \r",
       "------------------------------\r",
       "→ stimulation.PhotoStimulationInfo\r",
       "photostim_timeseries=null \r",
       "photostim_start_time=null \r",
       "photostim_sampling_rate=null \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"173,-177 9,-177 9,-142 173,-142 173,-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=\"17\" y=\"-157.4\">stimulation.PhotoStimulation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulation&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>stimulation.PhotoStimulation-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M102.271,-141.89C122.127,-112.628 162.779,-52.7202 179.046,-28.7484\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.TrialResponse -->\n",
       "<g class=\"node\" id=\"node10\"><title>reference.TrialResponse</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"trial_response       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"586.5,-177 465.5,-177 465.5,-142 586.5,-142 586.5,-177\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"473.5\" y=\"-158\">reference.TrialResponse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialResponse&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>reference.TrialResponse-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M540.524,-141.797C552.455,-128.077 568.882,-109.185 578.378,-98.2652\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.LickTrace -->\n",
       "<g class=\"node\" id=\"node11\"><title>behavior.LickTrace</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "lick_trace_left      \r",
       "lick_trace_right     \r",
       "lick_trace_start_time \r",
       "lick_trace_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"769\" cy=\"-88.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"72.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"769\" y=\"-85.4\">behavior.LickTrace</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- behavior.LickTrace&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>behavior.LickTrace-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M731.526,-73.3703C689.698,-57.5993 624.404,-32.9803 597.84,-22.9642\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node12\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node12\"><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=\"343,-106 179,-106 179,-71 343,-71 343,-106\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"261\" y=\"-86\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>analysis.TrialSegmentationSetting-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M242.845,-70.797C227.758,-56.9173 206.919,-37.7454 195.117,-26.8875\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>analysis.TrialSegmentationSetting-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M338.846,-70.9728C418.281,-54.1081 535.159,-29.2939 573.393,-21.1766\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node13\"><title>subject.Subject</title>\n",
       "<g id=\"a_node13\"><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=\"934.5,-319 839.5,-319 839.5,-284 934.5,-284 934.5,-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=\"847.5\" y=\"-299.4\">subject.Subject</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node17\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node17\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"946.5,-248 827.5,-248 827.5,-213 946.5,-213 946.5,-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=\"835.5\" y=\"-228.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;acquisition.Session -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>subject.Subject-&gt;acquisition.Session</title>\n",
       "<path d=\"M887,-283.797C887,-272.949 887,-258.867 887,-248.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimDevice -->\n",
       "<g class=\"node\" id=\"node14\"><title>stimulation.PhotoStimDevice</title>\n",
       "<g id=\"a_node14\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"161.5,-319 20.5,-319 20.5,-284 161.5,-284 161.5,-319\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"28.5\" y=\"-300\">stimulation.PhotoStimDevice</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulationInfo -->\n",
       "<g class=\"node\" id=\"node15\"><title>stimulation.PhotoStimulationInfo</title>\n",
       "<g id=\"a_node15\"><a xlink:title=\"→ reference.ActionLocation\r",
       "→ stimulation.PhotoStimDevice\r",
       "photo_stim_excitation_lambda \r",
       "------------------------------\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=\"182,-248 0,-248 0,-213 182,-213 182,-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=\"8\" y=\"-228.4\">stimulation.PhotoStimulationInfo</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimDevice&#45;&gt;stimulation.PhotoStimulationInfo -->\n",
       "<g class=\"edge\" id=\"edge15\"><title>stimulation.PhotoStimDevice-&gt;stimulation.PhotoStimulationInfo</title>\n",
       "<path d=\"M91,-283.797C91,-272.949 91,-258.867 91,-248.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulationInfo&#45;&gt;stimulation.PhotoStimulation -->\n",
       "<g class=\"edge\" id=\"edge16\"><title>stimulation.PhotoStimulationInfo-&gt;stimulation.PhotoStimulation</title>\n",
       "<path d=\"M91,-212.797C91,-201.949 91,-187.867 91,-177.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;stimulation.PhotoStimulation -->\n",
       "<g class=\"edge\" id=\"edge17\"><title>acquisition.Session-&gt;stimulation.PhotoStimulation</title>\n",
       "<path d=\"M827.367,-227.176C705.653,-221.966 420.019,-207.356 182,-177 179.133,-176.634 176.219,-176.239 173.28,-175.819\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge18\"><title>acquisition.Session-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M887,-212.797C887,-199.077 887,-180.185 887,-169.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;behavior.LickTrace -->\n",
       "<g class=\"edge\" id=\"edge19\"><title>acquisition.Session-&gt;behavior.LickTrace</title>\n",
       "<path d=\"M844.722,-212.893C827.821,-204.395 809.453,-192.519 797,-177 780.23,-156.101 773.412,-124.949 770.706,-106.001\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node18\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node18\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<ellipse cx=\"677\" cy=\"-159.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"72.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"677\" y=\"-156.4\">acquisition.TrialSet</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge20\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
       "<path d=\"M836.7,-212.973C800.419,-201.052 752.049,-185.159 718.083,-173.999\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.ExperimentType -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>acquisition.Session-&gt;acquisition.Session.ExperimentType</title>\n",
       "<path d=\"M931.791,-212.973C969.285,-199.138 1021.28,-179.954 1050.89,-169.026\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge22\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M655.894,-142.497C637.622,-128.642 611.9,-109.138 597.295,-98.0646\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent -->\n",
       "<g class=\"node\" id=\"node20\"><title>reference.ExperimentalEvent</title>\n",
       "<g id=\"a_node20\"><a xlink:title=\"event                \r",
       "------------------------------\r",
       "description          \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"331,-177 191,-177 191,-142 331,-142 331,-177\" stroke=\"none\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;1 -->\n",
       "<g class=\"edge\" id=\"edge23\"><title>reference.ExperimentalEvent-&gt;1</title>\n",
       "<path d=\"M290.941,-141.973C324.454,-123.473 375.296,-95.4088 384.534,-90.3093\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;analysis.TrialSegmentationSetting -->\n",
       "<g class=\"edge\" id=\"edge24\"><title>reference.ExperimentalEvent-&gt;analysis.TrialSegmentationSetting</title>\n",
       "<path d=\"M261,-141.797C261,-130.949 261,-116.867 261,-106.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x11855a67240>"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
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       "            <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=\"nonprimary\">species</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">animal_source</p>\n",
       "                                <span class=\"djtooltiptext\">source of the animal, Jax, Charles River etc.</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">sex</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">date_of_birth</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">subject_description</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:42</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:45</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:49</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:52</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229329</td>\n",
       "<td>2017-06-27 09:34:56</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td></tr><tr><td>anm00229329</td>\n",
       "<td>2017-06-27 09:35:00</td>\n",
       "<td>Mus musculus</td>\n",
       "<td>Jackson</td>\n",
       "<td>M</td>\n",
       "<td>None</td>\n",
       "<td>None</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 173</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  species        animal_source  sex     date_of_birth  subject_descri\n",
       "+------------+ +------------+ +------------+ +------------+ +-----+ +------------+ +------------+\n",
       "anm00229327    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "anm00229327    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "anm00229327    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "anm00229327    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "anm00229327    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "anm00229329    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "anm00229329    2017-06-27 09: Mus musculus   Jackson        M       None           None          \n",
       "   ...\n",
       " (Total: 173)"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "            .Relation{\n",
       "                border-collapse:collapse;\n",
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       "            .Relation th{\n",
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       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
       "            }\n",
       "            .Relation td{\n",
       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
       "            }\n",
       "            .Relation tr:nth-child(odd){\n",
       "                background: #ffffff;\n",
       "            }\n",
       "            .Relation tr:nth-child(even){\n",
       "                background: #f3f1ff;\n",
       "            }\n",
       "            /* Tooltip container */\n",
       "            .djtooltip {\n",
       "            }\n",
       "            /* Tooltip text */\n",
       "            .djtooltip .djtooltiptext {\n",
       "                visibility: hidden;\n",
       "                width: 120px;\n",
       "                background-color: black;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
       "                /* Position the tooltip text - see examples below! */\n",
       "                position: absolute;\n",
       "                z-index: 1;\n",
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       "                color: black;\n",
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       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
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       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
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       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">subject_id</p>\n",
       "                                <span class=\"djtooltiptext\">id of the subject (e.g. ANM244028)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_time</p>\n",
       "                                <span class=\"djtooltiptext\">session time</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">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\">is this a good or bad trial</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>1</td>\n",
       "<td>2.36285</td>\n",
       "<td>3117.27</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>2</td>\n",
       "<td>3117.27</td>\n",
       "<td>3126.26</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>3</td>\n",
       "<td>3126.26</td>\n",
       "<td>3135.9</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>4</td>\n",
       "<td>3135.9</td>\n",
       "<td>3144.07</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>5</td>\n",
       "<td>3144.07</td>\n",
       "<td>3152.95</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>6</td>\n",
       "<td>3152.95</td>\n",
       "<td>3160.72</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>7</td>\n",
       "<td>3160.72</td>\n",
       "<td>3168.56</td>\n",
       "<td>non-performing</td>\n",
       "<td>N/A</td>\n",
       "<td>0</td>\n",
       "<td>0</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 42292</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *trial_id    start_time     stop_time     trial_type     trial_response trial_stim_pre trial_is_good \n",
       "+------------+ +------------+ +----------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+\n",
       "anm00229327    2017-06-27 09: 1            2.36285        3117.27       non-performing N/A            0              0             \n",
       "anm00229327    2017-06-27 09: 2            3117.27        3126.26       non-performing N/A            0              0             \n",
       "anm00229327    2017-06-27 09: 3            3126.26        3135.9        non-performing N/A            0              0             \n",
       "anm00229327    2017-06-27 09: 4            3135.9         3144.07       non-performing N/A            0              0             \n",
       "anm00229327    2017-06-27 09: 5            3144.07        3152.95       non-performing N/A            0              0             \n",
       "anm00229327    2017-06-27 09: 6            3152.95        3160.72       non-performing N/A            0              0             \n",
       "anm00229327    2017-06-27 09: 7            3160.72        3168.56       non-performing N/A            0              0             \n",
       "   ...\n",
       " (Total: 42292)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition.TrialSet.Trial()"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-243 1158,-243 1158,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- reference.WholeCellDevice -->\n",
       "<g class=\"node\" id=\"node1\"><title>reference.WholeCellDevice</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"515.5,-239 383.5,-239 383.5,-204 515.5,-204 515.5,-239\" stroke=\"none\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.Cell -->\n",
       "<g class=\"node\" id=\"node18\"><title>intracellular.Cell</title>\n",
       "<g id=\"a_node18\"><a xlink:title=\"→ acquisition.Session\r",
       "cell_id              \r",
       "------------------------------\r",
       "cell_type            \r",
       "→ reference.ActionLocation\r",
       "→ reference.WholeCellDevice\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"498.5,-168 400.5,-168 400.5,-133 498.5,-133 498.5,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"408.5\" y=\"-148.4\">intracellular.Cell</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.WholeCellDevice&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>reference.WholeCellDevice-&gt;intracellular.Cell</title>\n",
       "<path d=\"M449.5,-203.797C449.5,-192.949 449.5,-178.867 449.5,-168.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.VMVALUnit -->\n",
       "<g class=\"node\" id=\"node2\"><title>extracellular.VMVALUnit</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "------------------------------\r",
       "in_vmval             \r",
       "\">\n",
       "<ellipse cx=\"1051.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"1051.5\" y=\"-9.9\">extracellular.VMVALUnit</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"node\" id=\"node3\"><title>intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ intracellular.CurrentInjection\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_current_injection \r",
       "\">\n",
       "<ellipse cx=\"540.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential -->\n",
       "<g class=\"node\" id=\"node4\"><title>intracellular.MembranePotential</title>\n",
       "<g id=\"a_node4\"><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=\"112.5\" cy=\"-79.5\" 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=\"112.5\" y=\"-76.4\">intracellular.MembranePotential</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"node\" id=\"node13\"><title>intracellular.TrialSegmentedMembranePotential</title>\n",
       "<g id=\"a_node13\"><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=\"269.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"269.5\" y=\"-9.9\">intracellular.TrialSegmentedMembranePotential</text>\n",
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       "</g>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential&#45;&gt;intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>intracellular.MembranePotential-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M150.505,-62.8863C185.535,-48.4951 235.38,-28.0174 257.792,-18.8101\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.Voltage -->\n",
       "<g class=\"node\" id=\"node5\"><title>extracellular.Voltage</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "------------------------------\r",
       "voltage              \r",
       "voltage_start_time   \r",
       "voltage_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"854.5\" cy=\"-79.5\" 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=\"854.5\" y=\"-76.4\">extracellular.Voltage</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node6\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"760,-239 641,-239 641,-204 760,-204 760,-239\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</a>\n",
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       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node7\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<ellipse cx=\"700.5\" cy=\"-150.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"72.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
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       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
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       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node14\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node14\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.ActionLocation\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"948,-168 791,-168 791,-133 948,-133 948,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"869.5\" y=\"-147.4\">extracellular.ProbeInsertion</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>acquisition.Session-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M740.98,-203.973C767.638,-193.088 802.407,-178.893 829.058,-168.012\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>acquisition.Session-&gt;intracellular.Cell</title>\n",
       "<path d=\"M640.698,-204.06C597.343,-192.142 539.419,-176.219 498.729,-165.033\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node8\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "stop_time=null       \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"760.5,-89 640.5,-89 640.5,-70 760.5,-70 760.5,-89\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"648.5\" y=\"-78\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M700.5,-132.797C700.5,-119.077 700.5,-100.185 700.5,-89.2652\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M679.253,-69.9349C645.081,-56.1591 579.033,-29.5334 552.23,-18.7285\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node10\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"803.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"803.5\" y=\"-9.9\">extracellular.TrialSegmentedUnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M714.391,-69.8012C735.257,-56.7345 774.235,-32.326 792.946,-20.6089\" 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=\"edge9\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M667.656,-69.9166C656.262,-67.1335 643.368,-64.1999 631.5,-62 495.92,-36.8696 329.415,-19.7931 282.4,-15.2252\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node9\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "spike_times          \r",
       "unit_cell_type       \r",
       "unit_x               \r",
       "unit_y               \r",
       "unit_z               \r",
       "spike_waveform       \r",
       "\">\n",
       "<ellipse cx=\"1051.5\" cy=\"-79.5\" 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=\"977.5\" y=\"-77.4\">extracellular.UnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.VMVALUnit -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.VMVALUnit</title>\n",
       "<path d=\"M1051.5,-61.8896C1051.5,-50.6919 1051.5,-36.1749 1051.5,-26.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M997.688,-64.5045C938.995,-49.2396 849.02,-25.8389 816.113,-17.2803\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel -->\n",
       "<g class=\"node\" id=\"node11\"><title>reference.Probe.Channel</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"→ reference.Probe\r",
       "channel_id           \r",
       "------------------------------\r",
       "channel_x_pos        \r",
       "channel_y_pos        \r",
       "channel_z_pos        \r",
       "shank_id             \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"1101,-160 978,-160 978,-141 1101,-141 1101,-160\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"986\" y=\"-149\">reference.Probe.Channel</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge12\"><title>reference.Probe.Channel-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M1041.02,-140.761C1042.92,-129.81 1046.23,-110.812 1048.62,-97.0697\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection -->\n",
       "<g class=\"node\" id=\"node12\"><title>intracellular.CurrentInjection</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"→ intracellular.Cell\r",
       "------------------------------\r",
       "current_injection    \r",
       "current_injection_start_time \r",
       "current_injection_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"341.5\" cy=\"-79.5\" 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=\"341.5\" y=\"-76.4\">intracellular.CurrentInjection</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>intracellular.CurrentInjection-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M386.656,-63.864C432.526,-48.9966 500.623,-26.9249 528.221,-17.98\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.Voltage -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>extracellular.ProbeInsertion-&gt;extracellular.Voltage</title>\n",
       "<path d=\"M865.869,-132.797C863.511,-121.949 860.449,-107.867 858.098,-97.0492\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge15\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M913.094,-132.973C942.96,-121.65 982.278,-106.744 1011.37,-95.7131\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe -->\n",
       "<g class=\"node\" id=\"node15\"><title>reference.Probe</title>\n",
       "<g id=\"a_node15\"><a xlink:title=\"probe_name           \r",
       "channel_counts       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1082,-239 997,-239 997,-204 1082,-204 1082,-239\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1005\" y=\"-220\">reference.Probe</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;reference.Probe.Channel -->\n",
       "<g class=\"edge\" id=\"edge16\"><title>reference.Probe-&gt;reference.Probe.Channel</title>\n",
       "<path d=\"M1039.5,-203.797C1039.5,-190.077 1039.5,-171.185 1039.5,-160.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge17\"><title>reference.Probe-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M998.781,-203.973C971.964,-193.088 936.99,-178.893 910.181,-168.012\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node16\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node16\"><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=\"622.5,-97 458.5,-97 458.5,-62 622.5,-62 622.5,-97\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"466.5\" y=\"-78\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge18\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M540.5,-61.8896C540.5,-50.6919 540.5,-36.1749 540.5,-26.0728\" 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=\"edge19\"><title>analysis.TrialSegmentationSetting-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M607.888,-61.9734C670.287,-46.6701 758.186,-25.113 790.732,-17.1312\" 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=\"edge20\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M471.063,-61.9734C406.452,-46.5955 315.308,-24.9026 282.169,-17.0154\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ActionLocation -->\n",
       "<g class=\"node\" id=\"node17\"><title>reference.ActionLocation</title>\n",
       "<g id=\"a_node17\"><a xlink:title=\"→ reference.BrainLocation\r",
       "→ reference.CoordinateReference\r",
       "coordinate_ap        \r",
       "coordinate_ml        \r",
       "coordinate_dv        \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"934,-239 787,-239 787,-204 934,-204 934,-239\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"795\" y=\"-219.4\">reference.ActionLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>reference.ActionLocation-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M862.679,-203.797C864.094,-192.949 865.93,-178.867 867.341,-168.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge22\"><title>reference.ActionLocation-&gt;intracellular.Cell</title>\n",
       "<path d=\"M786.701,-207.168C780.546,-206.086 774.417,-205.017 768.5,-204 673.081,-187.592 561.414,-169.459 498.72,-159.376\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.MembranePotential -->\n",
       "<g class=\"edge\" id=\"edge23\"><title>intracellular.Cell-&gt;intracellular.MembranePotential</title>\n",
       "<path d=\"M400.497,-139.467C341.625,-127.413 242.654,-107.149 177.458,-93.7999\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.CurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge24\"><title>intracellular.Cell-&gt;intracellular.CurrentInjection</title>\n",
       "<path d=\"M423.631,-132.973C406.408,-121.969 383.887,-107.581 366.785,-96.6543\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x11855be2550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dj.Diagram(extracellular) - 1) + (dj.Diagram(intracellular) - 1)"
   ]
  },
  {
   "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": 8,
   "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\">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\">coordinate_ref</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">coordinate_ap</p>\n",
       "                                <span class=\"djtooltiptext\">in mm, anterior positive, posterior negative</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">coordinate_ml</p>\n",
       "                                <span class=\"djtooltiptext\">in mm, always postive, number larger when more lateral</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">coordinate_dv</p>\n",
       "                                <span class=\"djtooltiptext\">in mm, always postive, number larger when more ventral (deeper)</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_x</p>\n",
       "                                <span class=\"djtooltiptext\">(mm)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_y</p>\n",
       "                                <span class=\"djtooltiptext\">(mm)</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">unit_z</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>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>1</td>\n",
       "<td>4</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.25484</td>\n",
       "<td>-4.1364</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>2</td>\n",
       "<td>9</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.37573</td>\n",
       "<td>-4.47608</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>3</td>\n",
       "<td>13</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.47317</td>\n",
       "<td>-4.08813</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>4</td>\n",
       "<td>14</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.49753</td>\n",
       "<td>-3.99115</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>5</td>\n",
       "<td>27</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.8124</td>\n",
       "<td>-4.37956</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>6</td>\n",
       "<td>28</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.83676</td>\n",
       "<td>-4.28257</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td></tr><tr><td>anm00229327</td>\n",
       "<td>2017-06-27 09:34:38</td>\n",
       "<td>ADunit</td>\n",
       "<td>32</td>\n",
       "<td>Thalamus</td>\n",
       "<td>N/A</td>\n",
       "<td>N/A</td>\n",
       "<td>left</td>\n",
       "<td>bregma</td>\n",
       "<td>2.50</td>\n",
       "<td>-1.50</td>\n",
       "<td>0.00</td>\n",
       "<td>7</td>\n",
       "<td>28</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>Wide width spiking</td>\n",
       "<td>1.83676</td>\n",
       "<td>-4.28257</td>\n",
       "<td>-1.57826</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 1936</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*subject_id    *session_time  *probe_name    *channel_count *brain_region  *brain_subregi *cortical_laye *hemisphere    *coordinate_re *coordinate_ap *coordinate_ml *coordinate_dv *unit_id    channel_id     spike_time unit_cell_type unit_x      unit_y       unit_z       spike_wave\n",
       "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +--------+ +------------+ +---------+ +----------+ +----------+ +--------+\n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           1           4              =BLOB=     Wide width spi 1.25484     -4.1364      -1.57826     =BLOB=    \n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           2           9              =BLOB=     Wide width spi 1.37573     -4.47608     -1.57826     =BLOB=    \n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           3           13             =BLOB=     Wide width spi 1.47317     -4.08813     -1.57826     =BLOB=    \n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           4           14             =BLOB=     Wide width spi 1.49753     -3.99115     -1.57826     =BLOB=    \n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           5           27             =BLOB=     Wide width spi 1.8124      -4.37956     -1.57826     =BLOB=    \n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           6           28             =BLOB=     Wide width spi 1.83676     -4.28257     -1.57826     =BLOB=    \n",
       "anm00229327    2017-06-27 09: ADunit         32             Thalamus       N/A            N/A            left           bregma         2.50           -1.50          0.00           7           28             =BLOB=     Wide width spi 1.83676     -4.28257     -1.57826     =BLOB=    \n",
       "   ...\n",
       " (Total: 1936)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracellular.UnitSpikeTimes * extracellular.ProbeInsertion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All together, the full pipeline architecture is shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<g class=\"node\" id=\"node1\"><title>6</title>\n",
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       "<g class=\"node\" id=\"node42\"><title>acquisition.TrialSet.EventTime</title>\n",
       "<g id=\"a_node42\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
       "→ reference.ExperimentalEvent.proj(trial_event=&quot;event&quot;)\r",
       "------------------------------\r",
       "event_time=null      \r",
       "\">\n",
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       "</g>\n",
       "<!-- 6&#45;&gt;acquisition.TrialSet.EventTime -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>6-&gt;acquisition.TrialSet.EventTime</title>\n",
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       "<!-- reference.CoordinateReference -->\n",
       "<g class=\"node\" id=\"node2\"><title>reference.CoordinateReference</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"coordinate_ref       \r",
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       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation -->\n",
       "<g class=\"node\" id=\"node13\"><title>reference.ActionLocation</title>\n",
       "<g id=\"a_node13\"><a xlink:title=\"→ reference.BrainLocation\r",
       "→ reference.CoordinateReference\r",
       "coordinate_ap        \r",
       "coordinate_ml        \r",
       "coordinate_dv        \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1270.5,-303 1123.5,-303 1123.5,-268 1270.5,-268 1270.5,-303\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.CoordinateReference&#45;&gt;reference.ActionLocation -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>reference.CoordinateReference-&gt;reference.ActionLocation</title>\n",
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       "</g>\n",
       "<!-- reference.WholeCellDevice -->\n",
       "<g class=\"node\" id=\"node3\"><title>reference.WholeCellDevice</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1421,-303 1289,-303 1289,-268 1421,-268 1421,-303\" stroke=\"none\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.Cell -->\n",
       "<g class=\"node\" id=\"node41\"><title>intracellular.Cell</title>\n",
       "<g id=\"a_node41\"><a xlink:title=\"→ acquisition.Session\r",
       "cell_id              \r",
       "------------------------------\r",
       "cell_type            \r",
       "→ reference.ActionLocation\r",
       "→ reference.WholeCellDevice\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1330,-232 1232,-232 1232,-197 1330,-197 1330,-232\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1240\" y=\"-212.4\">intracellular.Cell</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.WholeCellDevice&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>reference.WholeCellDevice-&gt;intracellular.Cell</title>\n",
       "<path d=\"M1337.09,-267.797C1325.45,-256.949 1310.35,-242.867 1298.75,-232.049\" 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=\"node4\"><title>reference.TrialType</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"trial_type           \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"510,-232 412,-232 412,-197 510,-197 510,-232\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"420\" y=\"-213\">reference.TrialType</text>\n",
       "</a>\n",
       "</g>\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",
       "\">\n",
       "<polygon fill=\"none\" points=\"800,-153 680,-153 680,-134 800,-134 800,-153\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"688\" y=\"-142\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialType&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>reference.TrialType-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M510.006,-199.395C513.046,-198.571 516.062,-197.766 519,-197 582.916,-180.325 657.803,-162.996 701.942,-153.006\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.ExperimentType -->\n",
       "<g class=\"node\" id=\"node5\"><title>acquisition.ExperimentType</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"experiment_type      \r",
       "\">\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session.ExperimentType -->\n",
       "<g class=\"node\" id=\"node48\"><title>acquisition.Session.ExperimentType</title>\n",
       "<g id=\"a_node48\"><a xlink:title=\"→ acquisition.Session\r",
       "→ acquisition.ExperimentType\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"355.5,-224 180.5,-224 180.5,-205 355.5,-205 355.5,-224\" stroke=\"none\"/>\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.ExperimentType&#45;&gt;acquisition.Session.ExperimentType -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>acquisition.ExperimentType-&gt;acquisition.Session.ExperimentType</title>\n",
       "<path d=\"M245.262,-267.797C251.227,-254.077 259.441,-235.185 264.189,-224.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.Voltage -->\n",
       "<g class=\"node\" id=\"node6\"><title>extracellular.Voltage</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "------------------------------\r",
       "voltage              \r",
       "voltage_start_time   \r",
       "voltage_sampling_rate \r",
       "\">\n",
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       "</g>\n",
       "<!-- reference.Experimenter -->\n",
       "<g class=\"node\" id=\"node7\"><title>reference.Experimenter</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"experimenter         \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"139.5,-303 22.5,-303 22.5,-268 139.5,-268 139.5,-303\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"30.5\" y=\"-284\">reference.Experimenter</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session.Experimenter -->\n",
       "<g class=\"node\" id=\"node20\"><title>acquisition.Session.Experimenter</title>\n",
       "<g id=\"a_node20\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Experimenter\r",
       "\">\n",
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       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"81\" y=\"-212\">acquisition.Session.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",
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       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node8\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "spike_times          \r",
       "unit_cell_type       \r",
       "unit_x               \r",
       "unit_y               \r",
       "unit_z               \r",
       "spike_waveform       \r",
       "\">\n",
       "<ellipse cx=\"1914\" cy=\"-143.5\" 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=\"1840\" y=\"-141.4\">extracellular.UnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node18\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node18\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
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       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"1784\" y=\"-69.4\">extracellular.TrialSegmentedUnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M1884.18,-126.67C1855.84,-111.63 1814.74,-89.8169 1795.17,-79.4291\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.VMVALUnit -->\n",
       "<g class=\"node\" id=\"node40\"><title>extracellular.VMVALUnit</title>\n",
       "<g id=\"a_node40\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "------------------------------\r",
       "in_vmval             \r",
       "\">\n",
       "<ellipse cx=\"1998\" cy=\"-72.5\" 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=\"1998\" y=\"-69.4\">extracellular.VMVALUnit</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.VMVALUnit -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.VMVALUnit</title>\n",
       "<path d=\"M1933.91,-126.148C1950.98,-112.12 1974.87,-92.4961 1988.14,-81.6011\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- subject.AlleleAlias -->\n",
       "<g class=\"node\" id=\"node9\"><title>subject.AlleleAlias</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"allele_alias         \r",
       "------------------------------\r",
       "→ subject.Allele\r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"860.5,-303 763.5,-303 763.5,-268 860.5,-268 860.5,-303\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"771.5\" y=\"-284\">subject.AlleleAlias</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"node\" id=\"node10\"><title>stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<g id=\"a_node10\"><a xlink:title=\"→ stimulation.PhotoStimulation\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_photostim  \r",
       "\">\n",
       "<ellipse cx=\"983\" cy=\"-72.5\" 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=\"983\" y=\"-69.4\">stimulation.TrialSegmentedPhotoStimulus</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge9\"><title>acquisition.TrialSet.Trial-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M770.285,-133.901C823.975,-118.655 933.665,-87.5086 970.57,-77.0296\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.TrialPhotoStimInfo -->\n",
       "<g class=\"node\" id=\"node14\"><title>stimulation.TrialPhotoStimInfo</title>\n",
       "<g id=\"a_node14\"><a xlink:title=\"→ acquisition.TrialSet.Trial\r",
       "------------------------------\r",
       "photo_stim_type      \r",
       "photo_stim_period    \r",
       "photo_stim_power     \r",
       "photo_loc_galvo_x    \r",
       "photo_loc_galvo_y    \r",
       "photo_loc_galvo_z    \r",
       "\">\n",
       "<ellipse cx=\"580\" cy=\"-72.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"106.001\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"580\" y=\"-69.4\">stimulation.TrialPhotoStimInfo</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;stimulation.TrialPhotoStimInfo -->\n",
       "<g class=\"edge\" id=\"edge10\"><title>acquisition.TrialSet.Trial-&gt;stimulation.TrialPhotoStimInfo</title>\n",
       "<path d=\"M720.059,-133.901C694.09,-122.701 648.223,-102.921 616.074,-89.0569\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"node\" id=\"node15\"><title>intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<g id=\"a_node15\"><a xlink:title=\"→ intracellular.CurrentInjection\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_current_injection \r",
       "\">\n",
       "<ellipse cx=\"1239\" cy=\"-72.5\" 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=\"1239\" y=\"-69.4\">intracellular.TrialSegmentedCurrentInjection</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge11\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M772.481,-133.921C783.959,-131.105 796.997,-128.15 809,-126 940.751,-102.396 975.155,-106.363 1108,-90 1151.23,-84.6755 1202.45,-78.1657 1225.99,-75.1629\" 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=\"edge12\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M770.33,-133.917C782.282,-130.886 796.192,-127.789 809,-126 1179.52,-74.2567 1277.16,-120.885 1650,-90 1694.62,-86.3041 1747.25,-78.9383 1771.1,-75.4362\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"node\" id=\"node34\"><title>intracellular.TrialSegmentedMembranePotential</title>\n",
       "<g id=\"a_node34\"><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=\"1510\" cy=\"-72.5\" 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=\"1510\" y=\"-69.4\">intracellular.TrialSegmentedMembranePotential</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"edge\" id=\"edge13\"><title>acquisition.TrialSet.Trial-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M770.693,-133.974C782.571,-130.988 796.333,-127.907 809,-126 1055.62,-88.8629 1120.61,-112.501 1369,-90 1416.37,-85.7084 1472.46,-78.5096 1497.19,-75.2263\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.RealignedEvent -->\n",
       "<g class=\"node\" id=\"node39\"><title>analysis.RealignedEvent</title>\n",
       "<g id=\"a_node39\"><a xlink:title=\"→ analysis.TrialSegmentationSetting\r",
       "→ acquisition.TrialSet.Trial\r",
       "\">\n",
       "<ellipse cx=\"776\" cy=\"-72.5\" 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=\"776\" y=\"-69.4\">analysis.RealignedEvent</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;analysis.RealignedEvent -->\n",
       "<g class=\"edge\" id=\"edge14\"><title>acquisition.TrialSet.Trial-&gt;analysis.RealignedEvent</title>\n",
       "<path d=\"M744.559,-133.761C751.068,-121.286 763.023,-98.3718 770.168,-84.6771\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;acquisition.TrialSet.EventTime -->\n",
       "<g class=\"edge\" id=\"edge15\"><title>acquisition.TrialSet.Trial-&gt;acquisition.TrialSet.EventTime</title>\n",
       "<path d=\"M679.823,-137.852C586.722,-130.265 402.294,-113.655 247,-90 232.621,-87.8097 216.951,-84.8459 203.138,-82.0344\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"node\" id=\"node49\"><title>behavior.TrialSegmentedLickTrace</title>\n",
       "<g id=\"a_node49\"><a xlink:title=\"→ behavior.LickTrace\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_lt_left    \r",
       "segmented_lt_right   \r",
       "\">\n",
       "<ellipse cx=\"356\" cy=\"-72.5\" 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=\"356\" y=\"-69.4\">behavior.TrialSegmentedLickTrace</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge16\"><title>acquisition.TrialSet.Trial-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M692.529,-133.97C604.237,-118.105 419.162,-84.8494 368.855,-75.8099\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulation -->\n",
       "<g class=\"node\" id=\"node12\"><title>stimulation.PhotoStimulation</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"→ acquisition.Session\r",
       "photostim_datetime   \r",
       "------------------------------\r",
       "→ stimulation.PhotoStimulationInfo\r",
       "photostim_timeseries=null \r",
       "photostim_start_time=null \r",
       "photostim_sampling_rate=null \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1164,-161 1000,-161 1000,-126 1164,-126 1164,-161\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1008\" y=\"-141.4\">stimulation.PhotoStimulation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulation&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge17\"><title>stimulation.PhotoStimulation-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M1058.29,-125.973C1037.58,-111.54 1008.52,-91.2854 993.267,-80.6561\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulationInfo -->\n",
       "<g class=\"node\" id=\"node33\"><title>stimulation.PhotoStimulationInfo</title>\n",
       "<g id=\"a_node33\"><a xlink:title=\"→ reference.ActionLocation\r",
       "→ stimulation.PhotoStimDevice\r",
       "photo_stim_excitation_lambda \r",
       "------------------------------\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=\"1209,-232 1027,-232 1027,-197 1209,-197 1209,-232\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1035\" y=\"-212.4\">stimulation.PhotoStimulationInfo</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;stimulation.PhotoStimulationInfo -->\n",
       "<g class=\"edge\" id=\"edge18\"><title>reference.ActionLocation-&gt;stimulation.PhotoStimulationInfo</title>\n",
       "<path d=\"M1177.88,-267.797C1165.46,-256.949 1149.33,-242.867 1136.95,-232.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node37\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node37\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.ActionLocation\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"1795.5,-232 1638.5,-232 1638.5,-197 1795.5,-197 1795.5,-232\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"1717\" y=\"-211.4\">extracellular.ProbeInsertion</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge19\"><title>reference.ActionLocation-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M1270.71,-269.541C1273.85,-269.002 1276.95,-268.485 1280,-268 1404.7,-248.144 1550.5,-232.047 1638.2,-223.129\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge20\"><title>reference.ActionLocation-&gt;intracellular.Cell</title>\n",
       "<path d=\"M1217.33,-267.797C1230.54,-256.949 1247.68,-242.867 1260.85,-232.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.AnimalSourceAlias -->\n",
       "<g class=\"node\" id=\"node16\"><title>reference.AnimalSourceAlias</title>\n",
       "<g id=\"a_node16\"><a xlink:title=\"animal_source_alias  \r",
       "------------------------------\r",
       "→ reference.AnimalSource\r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"991,-374 849,-374 849,-339 991,-339 991,-374\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"857\" y=\"-355\">reference.AnimalSourceAlias</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel -->\n",
       "<g class=\"node\" id=\"node17\"><title>reference.Probe.Channel</title>\n",
       "<g id=\"a_node17\"><a xlink:title=\"→ reference.Probe\r",
       "channel_id           \r",
       "------------------------------\r",
       "channel_x_pos        \r",
       "channel_y_pos        \r",
       "channel_z_pos        \r",
       "shank_id             \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"1955.5,-224 1832.5,-224 1832.5,-205 1955.5,-205 1955.5,-224\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1840.5\" y=\"-213\">reference.Probe.Channel</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>reference.Probe.Channel-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M1896.53,-204.761C1899.71,-193.81 1905.21,-174.812 1909.2,-161.07\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection -->\n",
       "<g class=\"node\" id=\"node19\"><title>intracellular.CurrentInjection</title>\n",
       "<g id=\"a_node19\"><a xlink:title=\"→ intracellular.Cell\r",
       "------------------------------\r",
       "current_injection    \r",
       "current_injection_start_time \r",
       "current_injection_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"1281\" cy=\"-143.5\" 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=\"1281\" y=\"-140.4\">intracellular.CurrentInjection</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge22\"><title>intracellular.CurrentInjection-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M1270.83,-125.797C1262.97,-112.875 1252.31,-95.3646 1245.54,-84.2481\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Species -->\n",
       "<g class=\"node\" id=\"node21\"><title>subject.Species</title>\n",
       "<g id=\"a_node21\"><a xlink:title=\"species              \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"733,-445 647,-445 647,-410 733,-410 733,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"655\" y=\"-426\">subject.Species</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject -->\n",
       "<g class=\"node\" id=\"node28\"><title>subject.Subject</title>\n",
       "<g id=\"a_node28\"><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=\"737.5,-374 642.5,-374 642.5,-339 737.5,-339 737.5,-374\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"650.5\" y=\"-354.4\">subject.Subject</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Species&#45;&gt;subject.Subject -->\n",
       "<g class=\"edge\" id=\"edge23\"><title>subject.Species-&gt;subject.Subject</title>\n",
       "<path d=\"M690,-409.797C690,-398.949 690,-384.867 690,-374.049\" 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=\"node22\"><title>reference.TrialResponse</title>\n",
       "<g id=\"a_node22\"><a xlink:title=\"trial_response       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"649.5,-232 528.5,-232 528.5,-197 649.5,-197 649.5,-232\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"536.5\" y=\"-213\">reference.TrialResponse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.TrialResponse&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge24\"><title>reference.TrialResponse-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M625.168,-196.973C655.357,-183.178 697.185,-164.064 721.135,-153.121\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- behavior.LickTrace -->\n",
       "<g class=\"node\" id=\"node23\"><title>behavior.LickTrace</title>\n",
       "<g id=\"a_node23\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "lick_trace_left      \r",
       "lick_trace_right     \r",
       "lick_trace_start_time \r",
       "lick_trace_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"370\" cy=\"-143.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"72.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"370\" y=\"-140.4\">behavior.LickTrace</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- behavior.LickTrace&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge25\"><title>behavior.LickTrace-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M366.611,-125.797C364.086,-113.353 360.698,-96.6554 358.437,-85.509\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Hemisphere -->\n",
       "<g class=\"node\" id=\"node24\"><title>reference.Hemisphere</title>\n",
       "<g id=\"a_node24\"><a xlink:title=\"hemisphere           \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1077.5,-445 966.5,-445 966.5,-410 1077.5,-410 1077.5,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"974.5\" y=\"-426\">reference.Hemisphere</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation -->\n",
       "<g class=\"node\" id=\"node25\"><title>reference.BrainLocation</title>\n",
       "<g id=\"a_node25\"><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=\"1179,-374 1037,-374 1037,-339 1179,-339 1179,-374\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1045\" y=\"-354.4\">reference.BrainLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Hemisphere&#45;&gt;reference.BrainLocation -->\n",
       "<g class=\"edge\" id=\"edge26\"><title>reference.Hemisphere-&gt;reference.BrainLocation</title>\n",
       "<path d=\"M1042.82,-409.797C1056.34,-398.949 1073.89,-384.867 1087.37,-374.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;reference.ActionLocation -->\n",
       "<g class=\"edge\" id=\"edge27\"><title>reference.BrainLocation-&gt;reference.ActionLocation</title>\n",
       "<path d=\"M1129.54,-338.797C1143.54,-327.949 1161.7,-313.867 1175.65,-303.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.WhiskerConfig -->\n",
       "<g class=\"node\" id=\"node26\"><title>reference.WhiskerConfig</title>\n",
       "<g id=\"a_node26\"><a xlink:title=\"whisker_config       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1352,-445 1230,-445 1230,-410 1352,-410 1352,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1238\" y=\"-426\">reference.WhiskerConfig</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node27\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node27\"><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=\"982,-161 818,-161 818,-126 982,-126 982,-161\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"826\" y=\"-142\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;stimulation.TrialSegmentedPhotoStimulus -->\n",
       "<g class=\"edge\" id=\"edge28\"><title>analysis.TrialSegmentationSetting-&gt;stimulation.TrialSegmentedPhotoStimulus</title>\n",
       "<path d=\"M920.092,-125.797C936.98,-111.758 960.381,-92.304 973.357,-81.5164\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;intracellular.TrialSegmentedCurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge29\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedCurrentInjection</title>\n",
       "<path d=\"M981.199,-125.973C1064.45,-109.028 1187.12,-84.059 1226.41,-76.0626\" 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=\"edge30\"><title>analysis.TrialSegmentationSetting-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M982.271,-127.108C985.213,-126.709 988.13,-126.337 991,-126 1282.32,-91.7673 1357.82,-115.939 1650,-90 1694.59,-86.041 1747.24,-78.8048 1771.1,-75.3853\" 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=\"edge31\"><title>analysis.TrialSegmentationSetting-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M982.29,-127.258C985.227,-126.816 988.137,-126.395 991,-126 1158.18,-102.925 1201.18,-107.808 1369,-90 1416.3,-84.9805 1472.42,-78.1513 1497.18,-75.094\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;analysis.RealignedEvent -->\n",
       "<g class=\"edge\" id=\"edge32\"><title>analysis.TrialSegmentationSetting-&gt;analysis.RealignedEvent</title>\n",
       "<path d=\"M870.299,-125.973C843.502,-111.062 805.537,-89.9361 787.019,-79.6313\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;behavior.TrialSegmentedLickTrace -->\n",
       "<g class=\"edge\" id=\"edge33\"><title>analysis.TrialSegmentationSetting-&gt;behavior.TrialSegmentedLickTrace</title>\n",
       "<path d=\"M817.705,-127.292C814.77,-126.841 811.861,-126.409 809,-126 656.82,-104.272 617.481,-109.509 465,-90 430.069,-85.5307 389.042,-78.9621 368.727,-75.6208\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Subject.Allele -->\n",
       "<g class=\"node\" id=\"node35\"><title>subject.Subject.Allele</title>\n",
       "<g id=\"a_node35\"><a xlink:title=\"→ subject.Subject\r",
       "→ subject.Allele\r",
       "\">\n",
       "<polygon fill=\"none\" points=\"745.5,-295 634.5,-295 634.5,-276 745.5,-276 745.5,-295\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"690\" y=\"-283\">subject.Subject.Allele</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;subject.Subject.Allele -->\n",
       "<g class=\"edge\" id=\"edge34\"><title>subject.Subject-&gt;subject.Subject.Allele</title>\n",
       "<path d=\"M690,-338.797C690,-325.077 690,-306.185 690,-295.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session -->\n",
       "<g class=\"node\" id=\"node44\"><title>acquisition.Session</title>\n",
       "<g id=\"a_node44\"><a xlink:title=\"→ subject.Subject\r",
       "session_time         \r",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"443.5,-303 324.5,-303 324.5,-268 443.5,-268 443.5,-303\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"332.5\" y=\"-283.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Subject&#45;&gt;acquisition.Session -->\n",
       "<g class=\"edge\" id=\"edge35\"><title>subject.Subject-&gt;acquisition.Session</title>\n",
       "<path d=\"M642.188,-344.719C588.567,-332.628 501.537,-313.003 443.544,-299.927\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ProbeSource -->\n",
       "<g class=\"node\" id=\"node29\"><title>reference.ProbeSource</title>\n",
       "<g id=\"a_node29\"><a xlink:title=\"probe_source         \r",
       "------------------------------\r",
       "number_of_channels   \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1486,-445 1370,-445 1370,-410 1486,-410 1486,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1378\" y=\"-426\">reference.ProbeSource</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential -->\n",
       "<g class=\"node\" id=\"node30\"><title>intracellular.MembranePotential</title>\n",
       "<g id=\"a_node30\"><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=\"1510\" cy=\"-143.5\" 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=\"1510\" y=\"-140.4\">intracellular.MembranePotential</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential&#45;&gt;intracellular.TrialSegmentedMembranePotential -->\n",
       "<g class=\"edge\" id=\"edge36\"><title>intracellular.MembranePotential-&gt;intracellular.TrialSegmentedMembranePotential</title>\n",
       "<path d=\"M1510,-125.797C1510,-113.353 1510,-96.6554 1510,-85.509\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimDevice -->\n",
       "<g class=\"node\" id=\"node31\"><title>stimulation.PhotoStimDevice</title>\n",
       "<g id=\"a_node31\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"616.5,-303 475.5,-303 475.5,-268 616.5,-268 616.5,-303\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"483.5\" y=\"-284\">stimulation.PhotoStimDevice</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimDevice&#45;&gt;stimulation.PhotoStimulationInfo -->\n",
       "<g class=\"edge\" id=\"edge37\"><title>stimulation.PhotoStimDevice-&gt;stimulation.PhotoStimulationInfo</title>\n",
       "<path d=\"M616.564,-269.348C619.409,-268.869 622.229,-268.417 625,-268 796.261,-242.255 841.149,-253.453 1013,-232 1017.58,-231.429 1022.26,-230.811 1026.99,-230.163\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.CorticalLayer -->\n",
       "<g class=\"node\" id=\"node32\"><title>reference.CorticalLayer</title>\n",
       "<g id=\"a_node32\"><a xlink:title=\"cortical_layer       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1212,-445 1096,-445 1096,-410 1212,-410 1212,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1104\" y=\"-426\">reference.CorticalLayer</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.CorticalLayer&#45;&gt;reference.BrainLocation -->\n",
       "<g class=\"edge\" id=\"edge38\"><title>reference.CorticalLayer-&gt;reference.BrainLocation</title>\n",
       "<path d=\"M1142.86,-409.797C1135.63,-398.949 1126.24,-384.867 1119.03,-374.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- stimulation.PhotoStimulationInfo&#45;&gt;stimulation.PhotoStimulation -->\n",
       "<g class=\"edge\" id=\"edge39\"><title>stimulation.PhotoStimulationInfo-&gt;stimulation.PhotoStimulation</title>\n",
       "<path d=\"M1109.29,-196.797C1103.63,-185.949 1096.28,-171.867 1090.63,-161.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Allele -->\n",
       "<g class=\"node\" id=\"node36\"><title>subject.Allele</title>\n",
       "<g id=\"a_node36\"><a xlink:title=\"allele               \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"830.5,-374 755.5,-374 755.5,-339 830.5,-339 830.5,-374\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"763.5\" y=\"-355\">subject.Allele</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- subject.Allele&#45;&gt;subject.AlleleAlias -->\n",
       "<g class=\"edge\" id=\"edge40\"><title>subject.Allele-&gt;subject.AlleleAlias</title>\n",
       "<path d=\"M797.599,-338.797C800.587,-327.949 804.464,-313.867 807.443,-303.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- subject.Allele&#45;&gt;subject.Subject.Allele -->\n",
       "<g class=\"edge\" id=\"edge41\"><title>subject.Allele-&gt;subject.Subject.Allele</title>\n",
       "<path d=\"M768.329,-338.973C747.737,-325.178 719.205,-306.064 702.868,-295.121\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.Voltage -->\n",
       "<g class=\"edge\" id=\"edge42\"><title>extracellular.ProbeInsertion-&gt;extracellular.Voltage</title>\n",
       "<path d=\"M1717,-196.797C1717,-185.949 1717,-171.867 1717,-161.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge43\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M1764.19,-196.973C1796.74,-185.57 1839.67,-170.534 1871.23,-159.481\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Virus -->\n",
       "<g class=\"node\" id=\"node38\"><title>reference.Virus</title>\n",
       "<g id=\"a_node38\"><a xlink:title=\"virus                \r",
       "------------------------------\r",
       "→ reference.VirusSource\r",
       "virus_lot_number=&quot;&quot;  \r",
       "virus_titer=null     \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1602,-374 1520,-374 1520,-339 1602,-339 1602,-374\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1528\" y=\"-355\">reference.Virus</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.RealignedEvent.RealignedEventTime -->\n",
       "<g class=\"node\" id=\"node43\"><title>analysis.RealignedEvent.RealignedEventTime</title>\n",
       "<g id=\"a_node43\"><a xlink:title=\"→ analysis.RealignedEvent\r",
       "→ acquisition.TrialSet.EventTime\r",
       "------------------------------\r",
       "realigned_event_time=null \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"882.5,-19 669.5,-19 669.5,-0 882.5,-0 882.5,-19\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"776\" y=\"-7\">analysis.RealignedEvent.RealignedEventTime</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.RealignedEvent&#45;&gt;analysis.RealignedEvent.RealignedEventTime -->\n",
       "<g class=\"edge\" id=\"edge44\"><title>analysis.RealignedEvent-&gt;analysis.RealignedEvent.RealignedEventTime</title>\n",
       "<path d=\"M776,-59.4484C776,-47.6431 776,-29.9315 776,-19.3638\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.CurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge45\"><title>intracellular.Cell-&gt;intracellular.CurrentInjection</title>\n",
       "<path d=\"M1281,-196.797C1281,-185.949 1281,-171.867 1281,-161.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.MembranePotential -->\n",
       "<g class=\"edge\" id=\"edge46\"><title>intracellular.Cell-&gt;intracellular.MembranePotential</title>\n",
       "<path d=\"M1330.13,-198.698C1368.76,-187.056 1422.34,-170.913 1460.96,-159.278\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.EventTime&#45;&gt;analysis.RealignedEvent.RealignedEventTime -->\n",
       "<g class=\"edge\" id=\"edge47\"><title>acquisition.TrialSet.EventTime-&gt;analysis.RealignedEvent.RealignedEventTime</title>\n",
       "<path d=\"M202.277,-62.9569C216.288,-60.0771 232.307,-57.0718 247,-55 392.677,-34.4591 562.358,-22.2327 669.469,-15.9383\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;stimulation.PhotoStimulation -->\n",
       "<g class=\"edge\" id=\"edge48\"><title>acquisition.Session-&gt;stimulation.PhotoStimulation</title>\n",
       "<path d=\"M443.86,-271.53C451.292,-270.193 458.805,-268.972 466,-268 579.472,-252.675 874.857,-277.339 980,-232 1001.08,-222.908 1000.67,-212.065 1018,-197 1032.1,-184.742 1048.38,-171.395 1061,-161.226\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.Experimenter -->\n",
       "<g class=\"edge\" id=\"edge49\"><title>acquisition.Session-&gt;acquisition.Session.Experimenter</title>\n",
       "<path d=\"M324.173,-270.139C321.072,-269.412 318,-268.695 315,-268 246.453,-252.116 166.431,-234.324 119.958,-224.065\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;behavior.LickTrace -->\n",
       "<g class=\"edge\" id=\"edge50\"><title>acquisition.Session-&gt;behavior.LickTrace</title>\n",
       "<path d=\"M382.339,-267.89C379.645,-240.946 374.352,-188.018 371.659,-161.088\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge51\"><title>acquisition.Session-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M443.835,-271.329C451.271,-270.027 458.791,-268.868 466,-268 851.542,-221.554 951.02,-248.467 1339,-232 1442.53,-227.606 1562,-222.362 1638.45,-218.984\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge52\"><title>acquisition.Session-&gt;intracellular.Cell</title>\n",
       "<path d=\"M443.84,-271.376C451.275,-270.065 458.794,-268.892 466,-268 798.069,-226.88 887.253,-282.662 1218,-232 1222.47,-231.315 1227.08,-230.442 1231.68,-229.455\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet -->\n",
       "<g class=\"node\" id=\"node46\"><title>acquisition.TrialSet</title>\n",
       "<g id=\"a_node46\"><a xlink:title=\"→ acquisition.Session\r",
       "------------------------------\r",
       "trial_counts         \r",
       "\">\n",
       "<ellipse cx=\"740\" cy=\"-214.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"72.5\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"740\" y=\"-211.4\">acquisition.TrialSet</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.TrialSet -->\n",
       "<g class=\"edge\" id=\"edge53\"><title>acquisition.Session-&gt;acquisition.TrialSet</title>\n",
       "<path d=\"M443.548,-272.422C451.102,-270.918 458.73,-269.413 466,-268 551.226,-251.436 572.774,-248.564 658,-232 667.123,-230.227 676.81,-228.309 686.208,-226.43\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;acquisition.Session.ExperimentType -->\n",
       "<g class=\"edge\" id=\"edge54\"><title>acquisition.Session-&gt;acquisition.Session.ExperimentType</title>\n",
       "<path d=\"M356.215,-267.973C333.024,-254.178 300.891,-235.064 282.493,-224.121\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.AnimalSource -->\n",
       "<g class=\"node\" id=\"node45\"><title>reference.AnimalSource</title>\n",
       "<g id=\"a_node45\"><a xlink:title=\"animal_source        \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"933,-445 813,-445 813,-410 933,-410 933,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"821\" y=\"-426\">reference.AnimalSource</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.AnimalSource&#45;&gt;reference.AnimalSourceAlias -->\n",
       "<g class=\"edge\" id=\"edge55\"><title>reference.AnimalSource-&gt;reference.AnimalSourceAlias</title>\n",
       "<path d=\"M884.377,-409.797C891.767,-398.949 901.359,-384.867 908.727,-374.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.AnimalSource&#45;&gt;subject.Subject -->\n",
       "<g class=\"edge\" id=\"edge56\"><title>reference.AnimalSource-&gt;subject.Subject</title>\n",
       "<path d=\"M829.167,-409.973C800.3,-399.088 762.651,-384.893 733.792,-374.012\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet&#45;&gt;acquisition.TrialSet.Trial -->\n",
       "<g class=\"edge\" id=\"edge57\"><title>acquisition.TrialSet-&gt;acquisition.TrialSet.Trial</title>\n",
       "<path d=\"M740,-196.797C740,-183.077 740,-164.185 740,-153.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent -->\n",
       "<g class=\"node\" id=\"node47\"><title>reference.ExperimentalEvent</title>\n",
       "<g id=\"a_node47\"><a xlink:title=\"event                \r",
       "------------------------------\r",
       "description          \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"971,-232 831,-232 831,-197 971,-197 971,-232\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"839\" y=\"-213\">reference.ExperimentalEvent</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;6 -->\n",
       "<g class=\"edge\" id=\"edge58\"><title>reference.ExperimentalEvent-&gt;6</title>\n",
       "<path d=\"M830.853,-198.47C827.529,-197.935 824.232,-197.44 821,-197 585.745,-164.946 522.401,-198.798 288,-161 262.625,-156.908 232.656,-147.344 225.561,-145.017\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ExperimentalEvent&#45;&gt;analysis.TrialSegmentationSetting -->\n",
       "<g class=\"edge\" id=\"edge59\"><title>reference.ExperimentalEvent-&gt;analysis.TrialSegmentationSetting</title>\n",
       "<path d=\"M900.758,-196.797C900.601,-185.949 900.397,-171.867 900.24,-161.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.VirusSource -->\n",
       "<g class=\"node\" id=\"node50\"><title>reference.VirusSource</title>\n",
       "<g id=\"a_node50\"><a xlink:title=\"virus_source         \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1617.5,-445 1504.5,-445 1504.5,-410 1617.5,-410 1617.5,-445\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1512.5\" y=\"-426\">reference.VirusSource</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.VirusSource&#45;&gt;reference.Virus -->\n",
       "<g class=\"edge\" id=\"edge60\"><title>reference.VirusSource-&gt;reference.Virus</title>\n",
       "<path d=\"M1561,-409.797C1561,-398.949 1561,-384.867 1561,-374.049\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe -->\n",
       "<g class=\"node\" id=\"node51\"><title>reference.Probe</title>\n",
       "<g id=\"a_node51\"><a xlink:title=\"probe_name           \r",
       "channel_counts       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"1936.5,-303 1851.5,-303 1851.5,-268 1936.5,-268 1936.5,-303\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"1859.5\" y=\"-284\">reference.Probe</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;reference.Probe.Channel -->\n",
       "<g class=\"edge\" id=\"edge61\"><title>reference.Probe-&gt;reference.Probe.Channel</title>\n",
       "<path d=\"M1894,-267.797C1894,-254.077 1894,-235.185 1894,-224.265\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.Probe&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge62\"><title>reference.Probe-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M1851.6,-267.973C1823.68,-257.088 1787.27,-242.893 1759.36,-232.012\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x11855d020b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datajoint as dj\n",
    "all_erd = (dj.Diagram(reference) + dj.Diagram(subject)\n",
    "           + dj.Diagram(acquisition) + dj.Diagram(analysis)\n",
    "           + dj.Diagram(stimulation) + dj.Diagram(behavior)\n",
    "           + dj.Diagram(extracellular) + dj.Diagram(intracellular))\n",
    "dj.Diagram(all_erd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduced analysis and figures  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we reproduce selected figures from [Guo, Inagaki et al. 2017](https://dx.doi.org/10.1038/nature22324), demonstrating the usage of the DataJoint pipeline in retrieving specifically desired data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reproduce Figure 4b, e, f\n",
    "First, we wish to demonstrate the queries and reproduction of figures 4b, 4e and 4h in the paper. \n",
    "\n",
    "These figures represents population-level membrane potentials of intracellular recordings, in response to photostimulation, of the entire study. To investigate the responses to photostimulation, we wish to visualize the membrane potentials responses time-locked to the onset of the \"delay\" period, where photostimulation was performed. This trial-based segmentation has already been performed and results stored in DataJoint, however appropriate queries are still required.  \n",
    "\n",
    "Specifically, we need to first query membrane potentials recordings from all sessions and categorize based on: \n",
    "+ Photostim location: contralateral ALM, Thalamus, and M1\n",
    "+ Trial condition: good trials without stimulation (control) and with stimulation (stim)\n",
    "\n",
    "In this data pipeline, each recording session contains the acquired intracellular recordings, and the photostimulation, which specifies brain location the stimulation was performed on. We need to use this photostimulation information to constrain the queries of trial-segmented membrane potentials. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick recap of the pipeline organization around intracellular recording"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"256pt\" viewBox=\"0.00 0.00 459.00 256.00\" width=\"459pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 252)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-252 455,-252 455,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- reference.WholeCellDevice -->\n",
       "<g class=\"node\" id=\"node1\"><title>reference.WholeCellDevice</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"device_name          \r",
       "------------------------------\r",
       "device_desc=&quot;&quot;       \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"148.5,-177 16.5,-177 16.5,-142 148.5,-142 148.5,-177\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"24.5\" y=\"-158\">reference.WholeCellDevice</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.Cell -->\n",
       "<g class=\"node\" id=\"node7\"><title>intracellular.Cell</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ acquisition.Session\r",
       "cell_id              \r",
       "------------------------------\r",
       "cell_type            \r",
       "→ reference.ActionLocation\r",
       "→ reference.WholeCellDevice\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"275.5,-106 177.5,-106 177.5,-71 275.5,-71 275.5,-106\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"185.5\" y=\"-86.4\">intracellular.Cell</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.WholeCellDevice&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>reference.WholeCellDevice-&gt;intracellular.Cell</title>\n",
       "<path d=\"M116.992,-141.973C139.623,-131.128 169.115,-116.997 191.791,-106.132\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- 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",
       "------------------------------\r",
       "session_directory=&quot;&quot; \r",
       "session_note=&quot;&quot;      \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"286,-177 167,-177 167,-142 286,-142 286,-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=\"175\" y=\"-157.4\">acquisition.Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.Session&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>acquisition.Session-&gt;intracellular.Cell</title>\n",
       "<path d=\"M226.5,-141.797C226.5,-130.949 226.5,-116.867 226.5,-106.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.MembranePotential -->\n",
       "<g class=\"node\" id=\"node3\"><title>intracellular.MembranePotential</title>\n",
       "<g id=\"a_node3\"><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=\"112.5\" cy=\"-17.5\" 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=\"112.5\" y=\"-14.4\">intracellular.MembranePotential</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- intracellular.CurrentInjection -->\n",
       "<g class=\"node\" id=\"node4\"><title>intracellular.CurrentInjection</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ intracellular.Cell\r",
       "------------------------------\r",
       "current_injection    \r",
       "current_injection_start_time \r",
       "current_injection_sampling_rate \r",
       "\">\n",
       "<ellipse cx=\"341.5\" cy=\"-17.5\" 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=\"341.5\" y=\"-14.4\">intracellular.CurrentInjection</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation -->\n",
       "<g class=\"node\" id=\"node5\"><title>reference.BrainLocation</title>\n",
       "<g id=\"a_node5\"><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=\"448.5,-248 306.5,-248 306.5,-213 448.5,-213 448.5,-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=\"314.5\" y=\"-228.4\">reference.BrainLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation -->\n",
       "<g class=\"node\" id=\"node6\"><title>reference.ActionLocation</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ reference.BrainLocation\r",
       "→ reference.CoordinateReference\r",
       "coordinate_ap        \r",
       "coordinate_ml        \r",
       "coordinate_dv        \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"451,-177 304,-177 304,-142 451,-142 451,-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=\"312\" y=\"-157.4\">reference.ActionLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;reference.ActionLocation -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>reference.BrainLocation-&gt;reference.ActionLocation</title>\n",
       "<path d=\"M377.5,-212.797C377.5,-201.949 377.5,-187.867 377.5,-177.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;intracellular.Cell -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>reference.ActionLocation-&gt;intracellular.Cell</title>\n",
       "<path d=\"M341.332,-141.973C317.512,-131.088 286.447,-116.893 262.635,-106.012\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.MembranePotential -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>intracellular.Cell-&gt;intracellular.MembranePotential</title>\n",
       "<path d=\"M199.194,-70.9728C181.014,-59.9689 157.242,-45.5806 139.19,-34.6543\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- intracellular.Cell&#45;&gt;intracellular.CurrentInjection -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>intracellular.Cell-&gt;intracellular.CurrentInjection</title>\n",
       "<path d=\"M254.045,-70.9728C272.518,-59.8891 296.714,-45.3719 314.971,-34.4173\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x11856062208>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(intracellular.Cell) + 1 - 1 + reference.BrainLocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we get all identified cells (ALM) with intracellular recordings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_keys = intracellular.Cell.fetch('KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we obtain the photostim information for all of the recording sessions for various subset of cells, meeting specific criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All photostimulation for \"right\"(contra) \"ALM\" stimulation with associated intracellular recordings\n",
    "region_dict = {'brain_region':'ALM', 'hemisphere':'right'}\n",
    "contraALM_photostims = (stimulation.PhotoStimulation & cell_keys & region_dict).fetch('KEY')\n",
    "\n",
    "# All photostimulation for \"left\" \"VM\" (thalamus) stimulation with associated intracellular recordings\n",
    "region_dict = {'brain_region':'VM', 'hemisphere':'left'}\n",
    "thal_photostims = (stimulation.PhotoStimulation & cell_keys & region_dict).fetch('KEY')\n",
    "\n",
    "# All photostimulation for \"left\" \"M1\" stimulation with associated intracellular recordings\n",
    "region_dict = {'brain_region':'M1', 'hemisphere':'left'}\n",
    "m1_photostims = (stimulation.PhotoStimulation & cell_keys & region_dict).fetch('KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use these photostimulation information to group the cells based on the location of the stimulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cells recorded with contra ALM stimulation\n",
    "contraALM_stim_cells = (intracellular.Cell & contraALM_photostims).fetch('KEY')\n",
    "\n",
    "# cells recorded with ispi thalamus stimulation\n",
    "thal_stim_cells = (intracellular.Cell & thal_photostims).fetch('KEY')\n",
    "\n",
    "# cells recorded with ipsi M1 stimulation\n",
    "m1_stim_cells = (intracellular.Cell & m1_photostims).fetch('KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We quickly check how many cells fall into each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALM cells with contra-ALM photostim: 9\n",
      "ALM cells with thalamus photostim: 16\n",
      "ALM cells with M1 photostim: 11\n"
     ]
    }
   ],
   "source": [
    "print(f'ALM cells with contra-ALM photostim: {len(contraALM_stim_cells)}')\n",
    "print(f'ALM cells with thalamus photostim: {len(thal_stim_cells)}')\n",
    "print(f'ALM cells with M1 photostim: {len(m1_stim_cells)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, whole-cell recordings sessions (session-key) with photostimulation at contralateral ALM, Thalamus, and M1 are stored in the variables `contraALM_stim_cells`, `thal_stim_cells` and `m1_stim_cells`, respectively.\n",
    "\n",
    "Next, we will extract (`.fetch()`) the trial-segmented membrane potential and photostim data from each of the selected cells, with trials further grouped into *with (`stim`)* or *without (`ctrl`)* photostimulation categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define some trial restrictor \n",
    "\n",
    "# restriction phrase to select good trials in which stimulation was\n",
    "# present (and has valid response with either left or right lick)\n",
    "stim_trial_cond = 'trial_is_good = True AND trial_stim_present = True AND trial_response in (\"correct\", \"incorrect\") AND trial_type in (\"lick left\", \"lick right\")'\n",
    "\n",
    "# restriction phrase to select good trials in which stimulation was NOT\n",
    "# present and subject responded correctly\n",
    "ctrl_trial_cond = 'trial_is_good = True AND trial_stim_present = False AND trial_response = \"correct\" AND trial_type in (\"lick left\", \"lick right\")'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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': 'pole_out', 'pre_stim_duration': 1.5, 'post_stim_duration': 3}).fetch1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get sampling rate and build time vector for...\n",
    "\n",
    "# ...membrane potential\n",
    "fs = intracellular.MembranePotential.fetch('membrane_potential_sampling_rate', limit=1)[0]\n",
    "mp_tvec = np.arange(-float(seg_param_key['pre_stim_duration']), float(seg_param_key['post_stim_duration']), 1/fs)\n",
    "\n",
    "# ...photostimulation\n",
    "stim_fs = (stimulation.PhotoStimulation & cell_keys).fetch('photostim_sampling_rate', limit=1)[0]\n",
    "stim_tvec = np.arange(-float(seg_param_key['pre_stim_duration']), float(seg_param_key['post_stim_duration']), 1/stim_fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we define a helper function `query_segmented_intracellular()` to query trial-segmented intracellular recordings based on the session-key, and trial-restrictor defined above.\n",
    "This function performs 3 steps:\n",
    "1. For each trial, retrieve the Vm (membrane potential) and photostim data, time-locked to the specified event (i.e. `pole out`)\n",
    "2. From the photostim data, determine the onset of photostim\n",
    "3. Realign Vm to the estimated onset of photostim, segmented by the `pre_stim_dur` and `post_stim_dur` defined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_stim_dur = -0.2\n",
    "post_stim_dur = 1.3\n",
    "tvec = np.arange(pre_stim_dur, post_stim_dur, 1/fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_segmented_intracellular(cells, ctrl_trial_cond, stim_trial_cond, seg_param_key):\n",
    "    ctrl_trials = (intracellular.TrialSegmentedMembranePotential & cells & seg_param_key\n",
    "             & (acquisition.TrialSet.Trial & ctrl_trial_cond)).proj()\n",
    "    stim_trials = (intracellular.TrialSegmentedMembranePotential & cells & seg_param_key\n",
    "                 & (acquisition.TrialSet.Trial & stim_trial_cond)).proj()\n",
    "    \n",
    "    aom = (stimulation.TrialSegmentedPhotoStimulus & stim_trials).fetch('segmented_photostim')\n",
    "    aom = np.nanmean(np.vstack([d[:len(stim_tvec)] for d in aom]), axis=0)\n",
    "    \n",
    "    thres_val = np.mean(aom[stim_tvec < 0]) + 10 * np.std(aom[stim_tvec < 0])\n",
    "    stim_onset = stim_tvec[aom > thres_val][0]\n",
    "    \n",
    "    def make_data(trial_keys):\n",
    "        for cell in cells:\n",
    "            cell_Vm = []\n",
    "            for k in trial_keys & cell: \n",
    "                Vm = (intracellular.TrialSegmentedMembranePotential & k).fetch1('segmented_mp_wo_spike')[:len(mp_tvec)]\n",
    "\n",
    "                realigned_Vm = Vm[np.logical_and(mp_tvec - stim_onset >= pre_stim_dur, mp_tvec - stim_onset < post_stim_dur)]\n",
    "                \n",
    "                cell_Vm.append(realigned_Vm[:len(tvec)])\n",
    "            \n",
    "            yield np.nanmean(np.vstack(cell_Vm), axis=0)\n",
    "                 \n",
    "    # stack data across trials and cells\n",
    "    return {'ctrl': np.vstack([d for d in list(make_data(ctrl_trials))]),\n",
    "            'stim': np.vstack([d for d in list(make_data(stim_trials))])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query trial-segmented data based on the cell and trial restrictors    \n",
    "trial_segmented_ic = {ic_loc: query_segmented_intracellular(ic_loc_key, ctrl_trial_cond, stim_trial_cond, seg_param_key)\n",
    "                      for ic_loc, ic_loc_key in zip(('thalamus', 'm1', 'contraALM'), (thal_stim_cells, m1_stim_cells, contraALM_stim_cells))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define a plotting function `plot_with_sem()` to plot the Vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_with_sem(data1, data2, tvec, ax):\n",
    "    for d, c, s in zip((data1, data2), ('b', 'k'), ('b', 'gray')):\n",
    "        v_mean = np.nanmean(d, axis=0)\n",
    "        v_sem = np.nanstd(d, axis=0) / np.sqrt(d.shape[0])\n",
    "        ax.plot(tvec[:len(v_mean)], v_mean, c)\n",
    "        ax.fill_between(tvec[:len(v_mean)], v_mean - v_sem, v_mean + v_sem, alpha=0.5, facecolor=s)          \n",
    "    ax.axvline(x=0, linestyle='--', color='k')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig4, axs = plt.subplots(len(trial_segmented_ic), 1, figsize=(4, 8))\n",
    "fig4.subplots_adjust(hspace=0.6)\n",
    "\n",
    "for avg, ax, title in zip(trial_segmented_ic.values(), axs, ('Thalamus', 'M1', 'contra-ALM')):\n",
    "    plot_with_sem(avg['stim'], avg['ctrl'], tvec, ax)\n",
    "    ax.set_xlim(pre_stim_dur, post_stim_dur)\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.set_ylabel('Vm (mV)')\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduce Figure 3b, 6b - Extracellular"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following parts of this example relates to the extracellular recording results of this study, namely the neuronal spiking patterns in response to photostimulation.\n",
    "\n",
    "Fairly similar to the routine layed out above, we wish to query neuronal spike times, segmented time-locked to the \"delay\" period, categorized by recording locations and photostimulation locations:\n",
    "+ Recording at ALM, stimulation at ALM\n",
    "+ Recording at ALM, stimulation at Thalamus\n",
    "+ Recording at Thalamus, stimulation at Thalamus\n",
    "+ Recording at Thalamus, stimulation at ALM\n",
    "\n",
    "Also with two trial-based conditions: good trials without stimulation (control) and with stimulation (stim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"318pt\" viewBox=\"0.00 0.00 591.50 318.00\" width=\"592pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 314)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"white\" points=\"-4,4 -4,-314 587.5,-314 587.5,4 -4,4\" stroke=\"none\"/>\n",
       "<!-- acquisition.TrialSet.Trial -->\n",
       "<g class=\"node\" id=\"node1\"><title>acquisition.TrialSet.Trial</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"→ acquisition.TrialSet\r",
       "trial_id             \r",
       "------------------------------\r",
       "start_time=null      \r",
       "stop_time=null       \r",
       "→ reference.TrialType\r",
       "→ reference.TrialResponse\r",
       "trial_stim_present   \r",
       "trial_is_good        \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"120,-89 0,-89 0,-70 120,-70 120,-89\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"8\" y=\"-78\">acquisition.TrialSet.Trial</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node4\"><title>extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "→ acquisition.TrialSet.Trial\r",
       "→ analysis.TrialSegmentationSetting\r",
       "------------------------------\r",
       "segmented_spike_times \r",
       "\">\n",
       "<ellipse cx=\"217\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"217\" y=\"-9.9\">extracellular.TrialSegmentedUnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- acquisition.TrialSet.Trial&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge1\"><title>acquisition.TrialSet.Trial-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M80.8484,-69.9349C114.213,-56.2277 178.546,-29.798 205.095,-18.891\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes -->\n",
       "<g class=\"node\" id=\"node2\"><title>extracellular.UnitSpikeTimes</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ extracellular.ProbeInsertion\r",
       "unit_id              \r",
       "------------------------------\r",
       "→ reference.Probe.Channel\r",
       "spike_times          \r",
       "unit_cell_type       \r",
       "unit_x               \r",
       "unit_y               \r",
       "unit_z               \r",
       "spike_waveform       \r",
       "\">\n",
       "<ellipse cx=\"426\" cy=\"-79.5\" 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=\"352\" y=\"-77.4\">extracellular.UnitSpikeTimes</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge2\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M378.836,-63.9445C330.437,-49.008 258.272,-26.7372 229.498,-17.8571\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.VMVALUnit -->\n",
       "<g class=\"node\" id=\"node9\"><title>extracellular.VMVALUnit</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ extracellular.UnitSpikeTimes\r",
       "------------------------------\r",
       "in_vmval             \r",
       "\">\n",
       "<ellipse cx=\"431\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"431\" y=\"-9.9\">extracellular.VMVALUnit</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.UnitSpikeTimes&#45;&gt;extracellular.VMVALUnit -->\n",
       "<g class=\"edge\" id=\"edge3\"><title>extracellular.UnitSpikeTimes-&gt;extracellular.VMVALUnit</title>\n",
       "<path d=\"M427.288,-61.8896C428.156,-50.6919 429.281,-36.1749 430.064,-26.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel -->\n",
       "<g class=\"node\" id=\"node3\"><title>reference.Probe.Channel</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ reference.Probe\r",
       "channel_id           \r",
       "------------------------------\r",
       "channel_x_pos        \r",
       "channel_y_pos        \r",
       "channel_z_pos        \r",
       "shank_id             \r",
       "\">\n",
       "<polygon fill=\"none\" points=\"408.5,-160 285.5,-160 285.5,-141 408.5,-141 408.5,-160\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"293.5\" y=\"-149\">reference.Probe.Channel</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.Probe.Channel&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge4\"><title>reference.Probe.Channel-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M357.005,-140.761C369.627,-129.737 391.584,-110.56 407.341,-96.7974\" fill=\"none\" stroke=\"#000000\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion -->\n",
       "<g class=\"node\" id=\"node5\"><title>extracellular.ProbeInsertion</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ acquisition.Session\r",
       "→ reference.Probe\r",
       "→ reference.ActionLocation\r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"583.5,-168 426.5,-168 426.5,-133 583.5,-133 583.5,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"505\" y=\"-147.4\">extracellular.ProbeInsertion</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- extracellular.ProbeInsertion&#45;&gt;extracellular.UnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge5\"><title>extracellular.ProbeInsertion-&gt;extracellular.UnitSpikeTimes</title>\n",
       "<path d=\"M485.876,-132.797C473.364,-121.869 457.096,-107.66 444.675,-96.8111\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.BrainLocation -->\n",
       "<g class=\"node\" id=\"node6\"><title>reference.BrainLocation</title>\n",
       "<g id=\"a_node6\"><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=\"576,-310 434,-310 434,-275 576,-275 576,-310\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"442\" y=\"-290.4\">reference.BrainLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.ActionLocation -->\n",
       "<g class=\"node\" id=\"node8\"><title>reference.ActionLocation</title>\n",
       "<g id=\"a_node8\"><a xlink:title=\"→ reference.BrainLocation\r",
       "→ reference.CoordinateReference\r",
       "coordinate_ap        \r",
       "coordinate_ml        \r",
       "coordinate_dv        \r",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"578.5,-239 431.5,-239 431.5,-204 578.5,-204 578.5,-239\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"505\" y=\"-218.4\">reference.ActionLocation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- reference.BrainLocation&#45;&gt;reference.ActionLocation -->\n",
       "<g class=\"edge\" id=\"edge6\"><title>reference.BrainLocation-&gt;reference.ActionLocation</title>\n",
       "<path d=\"M505,-274.797C505,-263.949 505,-249.867 505,-239.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting -->\n",
       "<g class=\"node\" id=\"node7\"><title>analysis.TrialSegmentationSetting</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"trial_seg_setting    \r",
       "------------------------------\r",
       "→ reference.ExperimentalEvent\r",
       "pre_stim_duration    \r",
       "post_stim_duration   \r",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"302,-97 138,-97 138,-62 302,-62 302,-97\" stroke=\"none\"/>\n",
       "<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"146\" y=\"-78\">analysis.TrialSegmentationSetting</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- analysis.TrialSegmentationSetting&#45;&gt;extracellular.TrialSegmentedUnitSpikeTimes -->\n",
       "<g class=\"edge\" id=\"edge7\"><title>analysis.TrialSegmentationSetting-&gt;extracellular.TrialSegmentedUnitSpikeTimes</title>\n",
       "<path d=\"M219.227,-61.8896C218.707,-50.6919 218.031,-36.1749 217.562,-26.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "<!-- reference.ActionLocation&#45;&gt;extracellular.ProbeInsertion -->\n",
       "<g class=\"edge\" id=\"edge8\"><title>reference.ActionLocation-&gt;extracellular.ProbeInsertion</title>\n",
       "<path d=\"M505,-203.797C505,-192.949 505,-178.867 505,-168.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x11855d3d518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick recap on the table structure related to extracellular recording\n",
    "dj.Diagram(extracellular.UnitSpikeTimes) + 1 - 1 + reference.BrainLocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we define a helper function to query and fetch the trial-segmented spike times for a single unit in a specifed session: `query_unit_segmented_spiketimes()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get trial-segmented spiketimes for a single unit in the 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) & (stimulation.TrialPhotoStimInfo & 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": "markdown",
   "metadata": {},
   "source": [
    "Here, a function is defined to construct and plot the spike raster and the PSTH: `plot_spike_raster_and_histogram()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spike_raster_and_histogram(contra_spike_times, ipsi_spike_times, axes, ax_title='', bin_counts=200, shade_bar=False):\n",
    "    # get event timing\n",
    "    events = ['pole_in', 'pole_out', 'cue_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 = 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 = (np.hstack([ipsi_spike_times.trial_start, contra_spike_times.trial_start]).min(),\n",
    "                  np.hstack([ipsi_spike_times.trial_stop, contra_spike_times.trial_stop]).max())\n",
    "\n",
    "    ipsi_spk_counts, ipsi_edges = np.histogram(np.hstack([r for r in ipsi_spike_times.segmented_spike_times]),\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 for r in contra_spike_times.segmented_spike_times]),\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_ylabel(ax_title)\n",
    "    ax_top.set_xlim(-1.5, 2.5);\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(-1.5, 2.5);\n",
    "    \n",
    "    # shade-bar for photostim\n",
    "    if shade_bar:\n",
    "        for ax in axes:\n",
    "            ax.axvspan(event_times[1], event_times[1] + 1.2, 0.95, 1, 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": [
    "As described above, we now specify the queries for:\n",
    "+ Recording at ALM, stimulation at ALM\n",
    "+ Recording at ALM, stimulation at Thalamus\n",
    "+ Recording at Thalamus, stimulation at Thalamus\n",
    "+ Recording at Thalamus, stimulation at ALM\n",
    "\n",
    "Also with two trial-based conditions:\n",
    "+ without stimulation (control) \n",
    "+ with stimulation (stim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# blue - correct contra trial (licking-right) ; red - correct ipsi trial (licking left); and photostim_period at 'delay' period\n",
    "correct_contra_trial_stim =  {'trial_is_good': True, 'trial_stim_present': True,\n",
    "                              'trial_type': 'lick right', 'photo_stim_period': 'delay'}\n",
    "correct_ipsi_trial_stim =  {'trial_is_good': True, 'trial_stim_present': True,\n",
    "                            'trial_type': 'lick left', 'photo_stim_period': 'delay'}\n",
    "correct_contra_trial_ctrl =  {'trial_is_good': True, 'trial_stim_present': False,\n",
    "                              'trial_type': 'lick right', 'trial_response': 'correct'}\n",
    "correct_ipsi_trial_ctrl =  {'trial_is_good': True, 'trial_stim_present': False,\n",
    "                            'trial_type': 'lick left', 'trial_response': 'correct'}\n",
    "\n",
    "ec_alm_insert = (extracellular.ProbeInsertion & {'brain_region': 'ALM', 'hemisphere': 'left'})\n",
    "ec_thal_insert = (extracellular.ProbeInsertion & {'brain_region': 'thalamus', 'hemisphere': 'left'})\n",
    "\n",
    "alm_photostim = (stimulation.PhotoStimulation & {'brain_region': 'ALM', 'hemisphere': 'left'})\n",
    "thal_photostim = (stimulation.PhotoStimulation & {'brain_region': 'thalamus', 'hemisphere': 'left'})\n",
    "\n",
    "# ALM probe with ALM or Thalamus photostim\n",
    "alm_insert_alm_stim = (acquisition.Session & ec_alm_insert) & (acquisition.Session & alm_photostim).fetch('KEY')\n",
    "alm_insert_thal_stim = (acquisition.Session & ec_alm_insert) & (acquisition.Session & thal_photostim).fetch('KEY')\n",
    "# Thalamus probe with ALM or Thalamus photostim\n",
    "thal_insert_alm_stim = (acquisition.Session & ec_thal_insert) & (acquisition.Session & alm_photostim).fetch('KEY')\n",
    "thal_insert_thal_stim = (acquisition.Session & ec_thal_insert) & (acquisition.Session & thal_photostim).fetch('KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example unit - Fig 3b & 6b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the all helper functions and selections defined, now for a particular cell, we can invoke the helper functions `query_unit_segmented_spiketimes()` and `plot_spike_raster_and_histogram()` to reproduce the figures.\n",
    "\n",
    "We pick some arbitrary neuronal units for plotting the spike raster and spike histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get segmented spike times for one unit - ALM-insert, thal-stim\n",
    "def make_unit_spiketimes(unit, sess):\n",
    "    return {k: query_unit_segmented_spiketimes(sess, unit, v, seg_param_key) \n",
    "            for k, v in zip(('contra_ctrl', 'ipsi_ctrl', 'contra_stim', 'ipsi_stim'), \n",
    "                            (correct_contra_trial_ctrl, correct_ipsi_trial_ctrl, \n",
    "                             correct_contra_trial_stim, correct_ipsi_trial_stim))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 units with ALM insert and Thalamus photostim\n",
    "s1 = (alm_insert_thal_stim & {'subject_id': 'anm00322807', 'session_time': '2017-06-27 09:43:03'}).fetch1('KEY')  #20151235\n",
    "s2 = (alm_insert_thal_stim & {'subject_id': 'anm00322805', 'session_time': '2017-06-27 09:42:26'}).fetch1('KEY')  #20151202\n",
    "s3 = (alm_insert_thal_stim & {'subject_id': 'anm00322807', 'session_time': '2017-06-27 09:43:07'}).fetch1('KEY')  #20151263\n",
    "alm_insert_thal_stim_unit = [make_unit_spiketimes(unit=unit_no, sess=session_no)\n",
    "                             for unit_no, session_no in zip((18, 19, 9), (s1, s2, s3))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qKXpr1cc/rbdGtfv97A3YIsWm0cLb5eXlqtjzZ0Vw+CpVCpnmoGurTbnY0PrUg9Wf20PNadXKcVjKqAitOFPVGuxWZWn4ZDaA0i+VdcLZ6aIDVelLdSE+qnL6K+6rHP9WJM+nxaKLC5wI9La8WvbAoz5eNSuwIO9ZEUyeVqSfy0YcY+qQNFi2nqipP5p8DK1uy7bH9ItGh2YTeT1yzIpOoLD6SNYn6QAaZ/37OsvqL6vv+XeLRoteiZiAVqj+fN1xsliYAVgVwLcAzKlf3wCwamwDypXvKO4s1e81CavFfEwUo6jDrPva6qSofY0n7ZPXSdT08kLOISxqV3MYZZp0PLmgcMdby+M/uSBqjqfkUdbn67IMHZu8uZeUrJW6VQf/Xna7RxlUa7CrNrDVy2wAqkOoyWArjtEIQKUOcQhddPpDyMlraAEbs6iNTR8O2DT0vrxqwQaGll4ENeoK3i9jf2PseRGK6uA2Rntx3XKQZR4tXbN9BXSULl9Eg2bHlWh5k4/Rqk4ORcuHQ4ZDdOfT4mQxNiM5wd2gTH7jyjucygTJvQEbYt5agWiD42EpCrmyCj0q0eri9VkOQKg9VkfOwFqrXf5dc+BDNEkH1dpTxh3nogixjJbL+riTze8rtOYWRJrD3g56rR6HSg0sv1qVWQBHeAN93nnnNfPN5VVzCDUZHA5FGYo4dAhddYjb5bWCvmoKYEhZtxbJFbXfFsoERRroOXkVV5hH7hBpeUNjYjmRlqNn2Uwr6KQhpP+19kJywm1MaFtkkd8QCthpadZTYl6uKGAQ6lvDl7DGs+kpdKtyyGmPWUxUjSL5baTHyV9sRnLCenuZ/Kxcs3ENDHqtHtIvZN5SWnxw5IsBPL88VoYrae2NVh51bO5wfUL4cnLSaXTU8zQpKzlpi9rk4HzzPvGOsCyvtRNqn/PA2+B55Mt7ki9Oq+d/0qRGHvmvhbEraA1lI82xaI+mU6lzBrYlmRUXp7XxWedHjTaVmaPtIsZYdao9GkaHOCZIUEWdHBFj1hQhrtUaf6YTWiTHtN8iTdFoxQnrkKxS1fIa0IFNQRfOd1HULeTIxgaNtP4N6ezYyLQM3sh8Xmf5/zqw6AvZa82eF6VZ9Sq0RduuogVmoL6h8a9Sb1UZuPJotb7mQGGc/MVmJCes7ZwukRdWayDqznC0gbEmplzphYREq8cSQl6ftsKU9VoKQKNDrl59PrmPtxXw8qGocigSbq2eY+osoondG3pCoD3S0drQFiCWwg0pHctIxwhlTMRLcwga99s2ptpVqcxyutk4mFuchgshOY5B2fyC11EdIY6QhRz/XlY1fVAV/VXOtaJAgkFBp65K5TUUdJJjxvku0mEhvRq7wCoz7paTq7UXWoCxsrX6osCkT5aPtWuWjeQBA5lmRY9jFwy8Dg3KmJkR8nZg2f920Gp9zXMgTv5iM5IT1qPL5DeuMKP9/XTTJz5BN910UzzzESvCQmVcFL30eeRLgBofWjTTmixSgPv76aabbmrwz41L2Sik/B7bJ0VGQksPbfGwhFVboddqdBNAN33iEw16fcRJc3T5Pe48hx6HaVtEeLq2OIo1FCEBtpXtqdQ5A1udzBr83HTTTW68yqDKSEK7dZVVuqK9nLyONhQ5IMT4lzLYyuKkFZqGHx2RVapaXrX+5zLr52zMYk7T/Za9le2WdX5D92Mj0FZanbYoH6OIB3/PsveWc25tL7N4iQ3IWPkUWzg0/lUs5Fsd67J1t5ceJ39mAjA5dMU2oFzFjMROBD4h241GxEQF5YpRE36trtg6NR64Ex6D0Ko2dnEgf8cKZFG7oTJiUUBEeYeZO7radhKfbjnOvg0tTa7q5TjwqJfFU9m9zvk+qsqYdlZmW0XMvLfylamzVXTfwWodw017yMnlcsT/BcyXC9U5UuBoHTnyagUFiu7F5Ak98bLsQdFYx0aYY3/ze9xOVAnL3sknm0X5JaxFgJXXyheyS1X0RTeeDmpBLJmW7/NTKUYmzQRgHoAH65/zxO8HYyo3rkJGh1ZvRY9yuAK2HCAtv0coqqnVwb9LpyzUbhma+vsbETfpqElnsGgSWG/+h6KjGl2xAql9D5U3IgxNEXK/KNAUmnSkixZT3lhrdMi6+CWj2dpCpswiIj82VRnYrshsMEJqLVRDCxKtDoluKGEDpSLEVTr9ISPYDmKdlnq+XITYp/vFq1YmVj/E0lMW7dTnaB058mrYxNycjemPMgEmOcYhZ1Ci7HyOCUIofkTLT3W0tjTbYAXLrDrKtlkmn0LHEP9lgjhl2+0kpM3Q/JXme8UyGZOp4ssh4LAM7W8JrTp5HdbEK1odax0nBTm06rLq1eqy8ip5hvZkSudOOv5FNFj8Go5ojiZLoYUWDxIhpaDlraNpD7XsC+tlEKJmZ1fWzx8faXxxenl/S8UhjX3IYdbQgQhxB68g1Bd0tChJM9+NvKF5Eprbst+tFzg7iOg9xH4eyUVtq9GbKoxZK+0K/WO+BKwFNEJ6vFV6yqKd+hyt3ZbH8jZW9HHTi7At6uomeS8KCoTyxbSvtRdabBlt5s7hlfQU7cctuq/ZC0lPrKMcqsNChN+R87HKykMVTn67sOYd79u8PT6VImQnSsAArAVgJwBv91dMOePyBDYzUP+tGphWlL/MLw2RZsSl8Y6ZkLFCotGk3BtSVpLelVcOO3Sh9svksZwWnz+ULuuOoUHj3zoBRNYvBcEaM774sRYU0vGWzrBVVjP0ocUb/3R5o4S1zNURmdX4IeWlKv4ZWlxxhJSyVka+xMXrseZLDIoc21h9Jct42uR+vhCtsc5B1Siz4CVFXjUZ8b9bWaxUzWf79VUqq9RpGyu/k/IibBm5CUXjYgJFRXVY4LZJs60yYBRoswY081+Wbtm2pDPkF2h0WjyF6Iix6co986XKGMQE37oFm444OSzMAPwXgH8CeBbAtQBeAvDn2AaUyyHgYJkOcbuIiajGGG+JdpxmJc3k3xKCGCfDcsi1iJ7utOlRBEtwrYiDxoOgs4l/uR+RtyHH1Hpzlr8MKWnX6OX9IF+klGVlfiudfzanVWlcOyOzGj9kKNcyEYxWnCTfjhYhjlk8l5EZmS7mXKFDLBdZso3hjq6E0AItJv+afI0OVCarNBw2Vn4nFnSRAYUYudXqDaVZdcky2pM/Ds02yfYs/S94y0WIJd8xi1tOkyxTtPDjwRur7iI9GnKYywadqlig9opsW/1eVYS4LqivAnBH/fc2AH4eU7lxhRmQDhGf+NL4WcxbQhnaM6wJaCi6VUYBaO1ZjhlR87FznnftEY8XLI0faYiJdKXi71v3/N7l0MpQc6w1cGWg9TOUY/esxYA2PjERYgvaHPBlpKNlLSxCdVvlKo4Qd1xmBR+llasVOYwtX5Qvpi5rARd6N8DnK1rAhWiVbRTRGYMqjVELgYcm/rXTY0LGfzjQTnvNZat2iNuWV1h/pGNgaMzKjEu7QamiOVGko0N6nf+OyJfbMhITmbbuteMcWjbE8k+07zF9pXwvdbTtSEMbQacYQft7/fMOACv77zGVG1chM037W7gTpT3mCQkaT/ffNSdO5uP1y3qlg6m1y+u32jNQA2yHULYledLKSIdD0m45krIfNEEPHSFjKQ1Os9JHQf6LovxFK+qQAtOid/5T8h8RhShEYyyqNrDDKrOFDqHGd74Cfa4W1aEtkjmKFj8yb6xzIBewZfmXaNXRKFp8tlNnCURtcZKyUSXNvv4Q2mmvueyIllcisYc0zGsD7S5iLBtWFIgKIeRUBvLltowUyXkoEFQlip8iNn+P0X3K9xoi//xsJKKNoFOMsP4KwJoATgVwPYBfA/hdTOXGpRPN7s+dO5fmTp/eMI5ewcoXZjQnWaZLB9hyfHk++UKXrFerQ+NJ1mc52gJzp0yhuXPn5uvRBllz7nlakXKQ+zAth0BTnFpfyLY0x1t+KvTPnT7d8e/vTZqk81KV8GpjzO/L71bb1riGFgZuDKo2sJ2RWYOvuXPn5sfL6h9Zls+/0Jzg81mbs6EoSYxTK+Ulhn6GuXPn0twpU6LzB2kp07Yl+8OMofH34Lqa0yTHWUOr9BctpMouVmVZ0VqVV0fl1QgADNnYYl5txMi49VvaU4l2Hc6C+WDyr9GgyVcn5EyzjzIttm3LjnmdPX06zR0uR7/bKBF0Kiu4NQAHAJhYppy4OJFhJjQjKcvGODFFE0tT3DIPvxfY7pCDNAQWP7Iey4G06pVpsat86QiWiXpynoocEiuKbW3DkG2UEdKyyiJURo55jAHQ5pfFR+NepQaWX5XILHdErf6Rv2Pk24rQaAtW6fz6vitYXJppRbSWNQxVGpLYJx+WXJZBpww7Hxc5ZkXl2mkzhOrGqLfltYjnUCSyVC+00d9FUc6iPelFaHUuFTmew+kwVtVWmcVnFxbUHYfjKU7+ojIB4wFsCGBTf8U2oFwNIo3Onz1tGs2eNk1jqvm7znz+e9lBtoRROoCaIbcmWZGTy5zb2dOm0ezZs4uNueV8SQdcKytpsRSIFTmWBi/Et/Wik0Hn7NmzHf++3MorN9cteeB1FiEm2kvUvJ+0KApltcP7QaKEsJa5KpXZgsj30HiViWJIJ1sr6/euc9nT9qrG6gPrfpFhKKh/9uzZNHv77e08ZdGOIS6r6yowukPjL+stG8EuK18SMXOuGvS2vEqeFTs1e/Zsmm3ZsUC5UnlC4x6yzSFbFKKniDYGdc7y4I1GnxXw0NquAh10TlX+Rzfi5LAwA/ApAE8B+Hd98/8/AdwZ24ByMRJ1ZVyD8j/j7UyOqqI90imSClw6DjIttGpldQ9teI9VVLx+7twWRZqkE21FAbVIIKfDaovzrDk/xjmIQ/xrfSZPiwgtNCylK+/HRBFDiwYLkha7bNXGtVqZlY6oQNt7aInyfSMjwxxljaBm0GKMM78XkiNEnDJRBTplHCuoV+VfO5GlrMNahrZY56Qah6b35TVHLZp4Ml8qs3Q6lwNp12Q5mUe2odUp82r2X9PbIYc6MNbqi8Dc3mjBHs0OahjOKHKLGBad1VuIk8XCDMD9ANaOrTDiasBQlLVJk5pPWdCiibEKs0w+y/hrzpvmrFlvqWuRW41HILwg4ApHc7h4PisiqTmT2tFmvk5/JjIfL83Zl2m8zzg9kk6hvGpA40ggGV2WTnKRUpftcRplX8r5qBlTa/6F5qV2AkN+nlVtYDsnswoqUa6WM2q9tKnBWrzJdCuP5ZRbC9A6jT1hXMo6tq3Qa7SRc664bGnvTsQ6x7xcDIoCEVq+1h2a3pZXS48zqA4h5z2k4y054XXFOI9FDq1VnrdVFIjRykuZ1RZuWhtF9oHT3+PoCZ1VBq30ab5MnCwWZnDnIq4UW2HE1QwprBBvQHJDFpqo7cIymJzG0OpbltUijLyMJvRQjh3T2tfolFEZiw9rpa85+1rfS96sPuCOMKdHW4GzOmp9fY03gGXbnkdLEWv35F5l6YRrUQENltIPKV/Oo7zXyFeVbA2fzDIMq3KVhlpL4+ly0amV1wxcCaPXE8aljPPYSn6rjHcupLzyBbb2knKM7o4dA7lw0vSYtmAPOTrWHKmXrPKqXF4tJ5PBdIhjnLwYOSn6TRSeg5pT7z9jvmt0snFvWsRJhN4T0uZuWR+kk05zRN09obPKIFZfWYvfWFk0E4Dj69dFAG4E8AV27/jYBpSrmXgprNwh8ulSsVkr1qJOijGmVoRYK8+VvNzmYCkmGUHhbVgRJ024Zf9Jx0vrX61dImfAtHy8T3j9ci+nFlng5XgZXw/va+Yg1mo1qvmTJTRnnLcR8/KFFiXSfluGUtYrjbrV7/679vJgfp5VZViHT2YZCudrLCxn1zJIFooWhaH2W8AQ/zGOdr5gc3qxM6ajDO2Wjiuqw9hSNSSvvg654OTypemCIoSCEf5TOr3+k9Oj1cdl07Ivks6Kzg0fFnk1oEZIhxOa/ZLpGkJBGWkjrLJ+zpZxyDXaQkGQInSyz4v6gUagQxyr3+wxiZNJMwHoD12xDShXgzctckZkTtZra/2qEOXuc/T3u9uG83xtjZUB8nSxNvr7jbpYO7JuXi4H346Wt163NLC5KrhR4TTV+VFtqyEguXrreYbuac4Ev8fScv0jofTXEJ2+wrd0AAAgAElEQVQiKjuvr1a3UfXx9055rdYYK6WRHM2sjSEIwZ/Xl/8t59S8vprK/9B4ipe5cm1JcGdAG896riquTspsjh8BVbmWdUQ1x0XWVeRg8vp8uTKOYnjRkh93dg3965X2ZECj30r332OeMLQDa2xiFxocXl/xNNkXfFFbtOjUYDluUrZknbxNa8ErHWktjd9v8DYy5JWau3hIx8ZEiIVeD2W12jTtgvbdKmDZfZ5eq+VsOE8f0t1eZuuLOJNOxZ41kcXzWLbaYEdruCURt/qKzXVNbdZqNerzdrCgYV4d/64VlW0V9mFknmjYxMXJpJkAfCO2kpJXY/5rThcRDQwM0CXYsek+hkiOu0+ob6fSlGU9nU/oHF2MUF//UDvSGWQ/RPVDeYcmCPobNEke6nQec8wADQwM5Nrngs4NKdBQDFogRGsr16ZgJOc7o7mcv8eVjRZg0eqQdspLV39/PV+dn4GBARo45pickzREl9L3PC3XTyqjym9xf2jctDHixr2guiEiheOjTeEqro7JbIF1Gxioz1fJdyyk4yLRNNCR9cc6ztwx4O8BWAIlroH61RRB1Rw0jjIR4jILjCJY/RLbXwJD8upR75chVuTTJWoEMawhb1ak+XtDDo6cE1LeQvVYYyYdZOnIVxch7piNlSzJ31xmc/2vZNbWBVrXa21qU3do7MR3jQapz4Wvl8/H7YRoeGgI6/cGBgZogOWXvGkug2bfgqbF1vd5h1Gz89TclooCW8b9Gp51YGCAgIFc+5Iuaa/lbkVtbGVbWrrkMdY8t6iehqqNuULCentsJSUvdRUVw5wqPIH7OQdVSb+21p/r/KbJyJ3Eel0yGi3LN9UnHM0im6855rl6+/upH/1DdYf0v3xSL9sO0qF8t5SRtkUwJ/CUL+P9Dn6/v59oRl9/szD09+fHSel7/8nzNUUPeJWKo8/zFc2zfvQ3G+cQRGco+SuRrY7JbJXOmAbTK1LyeVRBk6a1pcZnE3XoyYF0lJRFElHzE5ucPsrfzn2q6WLu8C7TPrlMavWHZFtrQ6PP2kX2layfvHPiF5Ez+vqHAuxDegn6iX7eSPt6ZT/m/GspU8KhzdEvF+faeBQpTbfvtLflVUAba45c/yt9Jqe7LxMSRyufzKuKMaNB6n0pnryepiekYry9HbfakaIt1YNm30POnG9fk2uuOng+2TfRtiVw39IvWp9wnjX1JvvFmge8P9U2KZ+Pw9rNVaTyC9LjZNJMAP4BYC0Ak7UrtgHlKsSsWbNo1qxZMVnbhjWfQqtfq3xoAsfYe48PfrCZf6nUNPo0xSMdz1b9CFnWUoaaw1ymTH9/8/hb/Sr51vKFylkoGu8yYxlZd1UGtjMyW8AwH692+qYUqmjI11F/1NrfT/kIsQCfszzCOWu77WjWdtsR9fcPFeXRFD5PuaGQ8zYkX1renFFl5TVjphki/9oAN3SyPv9dvgqQzzeLPvjBWflXBNBPkyY1vmtGtR/9Zr3U36/SIenj94gaDhwfA97/8jUGtc/F3FKmWm/LawCe35DMWrYlZBPL2L2QIy3va7RZdVpOufZb2hjNMdbsapG9j+GNKD83tfqlzFlttArPv0U/l0Up79qrRZxm/93qH9m3ofnH7xXZ3oJ+iZNJMwF4BcCDAOYp14OxDShXIXphw3crk64qZyCG/1jHLxTsKINQ2aqczoYw5vmPpVnL18oCIFSm1QWFLC9orcrAdkVm+Xi12z/dQsxiUTN2zmA08x9yMK1Ii3RYNdo0R1CW5844/67VWfbSy9bqV1zd3lHWHHHrt+RDyy/7ipfTxs2na33OodwfsfLaiNKzPaTU7JQVOYMxiLEL1m+r3TYcolwezcbG2McytBTZEY1Xnm7ppLJOspZW5GNoDqiUO5lP0qv1AS8n6w3Ry+W7DFhdcTJpJgBzYyspeRWCD1ZVTqaGGGF09LRfn7Ui0oTQ8x8SGHlfc3zlasyqS8sboxRluvUpH62GJrxU1rzvW3HoW3HQtAMD2qmPwxDsqgxsZTIL4AgAcwDMOe+884I8lZHXTspzOyg7t+R89fzzCLEWLdFkTRofSQufjzyNR5l4Hn6PR3hkZMe/H+Z/8/w+3dPu8/J8fX3umjSpRpMm1ZocV83xLeMISyeY8yt55EbZf+f88T7j/V/kmBj3e05exaXq4nzf1gio5SLmOQah39fq5vXLtmTfFelPvvDj91rRG5pMNxaNzUEX2bZm60J5XL15msvabl6P5ShaDrQFvf68jyHBZce3KeXO1+3vi3fNVf4t/0Ib47J8FuSPk0kzoUcc4nYdkBBilUARDYaT0xSNkN/577wBrNUVlt0+F2BOA69TGg+rrpCRkmU04yNXi7Kc9SkFwqd5Za3Rbo1FCWNWiFbaKZtPpFciW92S2TJPdDopz92Cxb+UGwtlnAYrsqLpAimP/p5Gl+Wg8jT5eNTf7+tz8lrk/HLHWuoP6ZxLncLba9YXzXpU+83p5n3VwpzseXnVdC7vEx900BxQXk4Ta8suaG1ZNlHTj5rtkPc1WI6i1n5jwZp3CLW5oslJKE8oXwiWI1dGL4TsjZYmfQyrfW0eyYWn1A0hv0W2wWm0+rKIl8j8cTJpJgAfV+69JrbiwKUypzmE/L4lQO0gtHrhKHKOLCHXXjazVlG8jZgIsZ+EnBYZBZHGh6fxcnlF2TzptYnLL8mnFAh5/rmmVKRD7JW1NMKhMYqZDzGOqlzZtupUl8lexTUcMquhjEPcrsz2Iiz+rQiINofz+k+vx6f5+Rl6emLJitcLsi0ZOdXkTjoYDR3jIsSabrCuvEOtp3NaNb0k72t6TfIp9aa/VxI9L6/aXMv3W61+6QzKyLE2V/k9PgZyHnGEHEUpI7E62HLAZB08TyjoZDnlkl+NLs2+Spok2rkXQig/9zE0euX4ywUlly9LvxXJVVHfWHPT4ksbb4Y4mYzNSE5Yq3grtkGh6NBGRzQbmLIrphCsyRbqaA2WkynTeR0h4fWIcTBkn0mnUk5SzfnkeXk+/t3qd8tIlhEMTfk5Jdq8epfQ+i9mPpRdtcaU0VBSeVViYLWrapnVUNYhrkLZ9xKKHGK+sOPzlDtnmkHmMiZlUjqFloHSZMySUWnspOzLNnw5t12i1kRf2Uu2K3Wa7Defhzv40mmR/csdfd53RcZZ/B6R8lrWxvAymg7UHCmZz9Kd2n0ZOCnSu9pWvCI9wiPEfX01taxcEPp7Ui4lJN2hhQNHaNER+m3di+m/SZP0p/DWwoZfnF6pezgtcsFuweK7zNyJSIuTv9iM5IS1ikc8TczLSXLccfNp/vz5JPOGnMgysBysso6P5RTKiamVsSZ8fz/R/Pnz6bjj5geFQtbBnXK5F9Dnl3sJeZ1SufC8Elxx8TZ4PZLOIl74d8k/N3iyXut3TFtFea0oQFlElO+kga1UZjXMn98sryajaMxNeX+kQuPfci4159Z/97DK8TKa42jlkW3x/82Q6XLfLqfL7xlu5m0+AfObaOHXyivbadwIeweX0yP7gtMo9zpa/csdY9nXsv8jfo8IeQ3pHW5jQ86dh7V1ooyN4ulam3Isiuiy7HiIb19m/vzGnJVpUk5lvZYfosmUzM/nn1VOa0Ojx7pXZLccf43x1+jTdFNMmxZ9sU6s9KOK8ksExj5O/mIzkhPWo8vkN64mwq0VE08vcqrKQKufK2UNmqBb5bSJo7Wl0SSNnCzH81vKIdbQau1reSU0w6vVIw251V6IRouGUB1F8ykWMfMrJk9E++3K1LDIbLvwciJfGKlavnsB0rmUC1Oi/GKS/3M6n/t+Mcgdu1ot75jKbQOyXX6Pp/l2fb3SoZ00Sd8O1YlLq1/2B6eb/7O75J/3Fe8n3v/SUBfNPaFrT6URIK9SxjiKnJUi++DrLLJzoXtaUEaOFc+r2UAtX0jfcsdes7F8TnH6LPsi6eXyrS3SpO/AaQrpQy1ird0r0qV80cnr5mMgfRpJF0cor7XdUSI2klyUp6BsnPyZCcbZiFWdkWgJGBHRIYfMpJkzZxLPGyO0sdCE3XLcPF1FCloKiTZZZVl/X04oYCYdcsjMJodXThxLSDWFL9vWnHhNALXIgDTosm8kr7xMEY2Sf2vsfV6Nfk0Rx6AVB8wyqBwRTyarMqYdlVkLM2fm5dVkEs1OjL/PP0OGWmK4nWatPY1/rjPkHNZ0jdQJPI/mCFh9xx1hy+m0IsRWeX5fOp4A0bbbziRgZqHTKy+NRm0RwWmXesZ/WltSZF2c7pAODd2rl+l5eZV9we8fcogbM82B0pxKy2mR4yLHR+roMnKg3S9ykmU5CZ/G+Zfp2v5ZS8/INMljiEZLz1kyX2SzZZqsM193Q2dJefCLTc2p1sZeLjI4XzJNQ4x9l/cCcmkhTibNBGAQwPz6OYnyrMSWz0iMYUjub4rd5yURu5LQthBIurSVIKep2alrjvrIsv6+dAT6+ppfeNCEKiSknLcQzZIOb1y4YQq1o/UNv2ftMZLGlfe/P8ZJ0hgSUEmDhOaUFhmBEDQDYLUdUW/bxpU6KLNFxMfuR5TRB23sicJ9qRmF4YTWnsW/j8L09dkRYs+P1EH++9CfW/Tz9vRomqyHR4d5JJqPgYyg1mqNNvl4aWPny/o9xNzZDl3jx+d1hOYMc6dd0kjUrHtked8nvH5fjjspRXPJuHcqjRB51WTJ9VX+lAnN+fLQ+p5Dc7xDdtFDRkZlHikPmvOn2SfNV5D3gfw5zDzdstWh+jy4/Hn6tLyyX2QdlrMbKqelWZ/ex/DlpKxIx97ns+w8b1fKmKQv1o/Txlabr0X1+KIxV0hYvw/3Tzo/ArA7gCy20mCDAUYsh5h3QsjuhiZICNLpKjMIHvwRhJxgobKaMvH8axOyYNCH6rTq9velUeC/+WU5ipI/KeyakHFoEahG2Rr5l3S4MdcMGK/X2mts8a8Jd0z/yvqsdmRaqLoqrk7JbBE/sQ6xNd6h+jVZDBmFTkNfbDXzL2VPcxgs4xNy2jQnks89riMtPcS3YWhleDm5MNadzBqFjl3j5xFLh1n7tzyeJqPIrr+bedL6RDr0vqzvRxlwsJwoBSNCXjWeGgsn+6Wy0CN/q0+kwzXUUdDzaWlW/Vo+y7kqKqM5hLKM5sTJtiy7GMOv5dzzfFZEPhTY0cbaop/rLKkvtG1GPJ3rBl9eziNZNr8YsXkI0S/rK4k4mQwmAhmAdwA4H8AdAE4HsHls5dqlMSI7TxoYqfBNjgOTMQS52tHqKXKWtMnOJ4p01ng+a0EgJ7PFf0PJNQugNSGlceF1+b131gqe161F77kTW0SbZqj8uaaSVukgcEPtf0twPvk9aVDLCqtUTCGlpEHcr8TAUodkVtLevLiJc4i5gvPzwT+es/qHj42ca2X73Nehfdd+a9DatIyrnLf+vv/UnEteljt3cs5LZ9XTL42RNtd5Wenwanl5lJqXaTidTl4txxYIv1TnjTCnTTrO2gLDl9O2XvC6ZL/L8eH3LQdSYETIa1gf1oZ0rEzT5mirCMliGfss8xeVLXKw/IKgTDuWHeII+SCajbGcPsspj3HC+X1rAcJ1tjX3tfnA69XoKqpLSyvioSLEyWNUJmBNAEcCWATgk7GVGxcRhVc9fLDkhIqJEJdZPfAyfqCtAYsxtLEDqzm30iHWHEiLNs2wWpPSMi5anbbT3ty2vKfR6euSK0kprNwhlscqcWPMHXptblgOrnTOQvlj7pUVaHG/HXkaNpn1kHOlrEOsOU5W/0gHzcoj71uyGjIuMcpXa9P941ezvHKZtS4/f/kc9xEaTZ6802v9wYXc2uCdS96WtiUi5HiGHFn33clryCGOvawtF7LvZZTbl5P9IvtNLq6ImvvaQ9bBMCLkVdoL/ruvr9ghlI/u5WLUcjplmhXVjLXX0vZYbcv8oTa9jeG8cmg0a08qixbjcu5IvjVdFepn2W6oLyza3Hyv1a9mmfDyIPWUbFs77lAGvjRayqQXmZUSvl6cHJoJwKoAPgzg1wBuBvBZAJvEVhy4miaCnDTcISxyRFXOmxVYVN6QgIZosQYzNLCac+9p0SJOlmDwurRJW6TMZFRJtseNEC+nGWGZV0KrUxoiZ+xrpEWIPTTDLNuRfGrQ5l/R+Fr3YpWScb8qo9oxmQ0h5BBbkXnpBFnGm88RLVoq2/BlLR3A9/XLpxOxEWJJq5dXqdOkQ6V9SidUy6fJoTTKRY4mb0/rc9/PvK2i6Ku/Vl65sR819urvb3bM+W/JE+fZ4slf/nQMPlZSV8j+lvqN97ViF0aMvGrzyn0vXsRK3ar1u1ZGpoXyWelEtpxrbUtZ0MDv+wixRoNVB5dHrU4NIQfZspNFfcfvl/F1eFmvszU50PSS5J2naXqoqP3Y9Hbr4lljrpCwLqk/wvk8gEMAvI9fsQ0oV04JakwtWrSIFi1apBoLzWhKI1XGgQ4NtIQ2CTT6+X3L6bfadvcd/3J1phlcqy7N+EqHVN735bU+1qIFUni4Ux6zqJBGztMDLCJgERHlX67h9HBemvsv70zJ7SpatIjXL9saBrRrADsusxp833h51dJCCtdylKSTptVhtaHNccuo83tlkDeseX3FeSjST/Ken4f8nG/tAuw80hnk/ac5uFwG/D3+5IVf1n1gEW288aIhumLHuSjd2jah/dbq4+Mlt4Zoc0Lq1IAzM2Lk1eLnc59rllkJyX/ITsg82ry28mk2lahZlng+a2zkGGq0EzVkVrMBfr5odRTd0/rbGoNQH4batOqORX9/g3+Nby7Hmj8hdRC/V2Qzy6YX8VeC/ziZNBOAiwHMMK6fxDagXDnlFWJKG3ReRlOkobKhNkICpQmlVLg88sTLhSaJZtDlfc0AEOl7f3ldUtB5f0kHUNapOQxy0vuy8hFJqB+te5aBk23z+iVPsm1rXsh0Waf8rjlLHXCU2zKs/uqkzKpEG7Li04icg0bk5onfR2pFHeX4y8f/vn7LqMh6OZ0hmkNPVjTI+a7JK29XGnMuk7xOTcY02ZdtaDrR8xXjTPK8sg+tv1XWxq+VS+4dtp1uR6fXs9xg88WBz+vnHdfbUofzvrfSZL46RqS8DgcCiwg1j6bbpTzLfKF2rTyh8kVOahkHTdM7oTml2fDh+k2U10Vy25GUGamTpe6IsYmhvrDSK7K1cTIZmzFXCDiklXLEhJUbBokZM2bQjBkz1DQuPFyRyYkcMxm1erXOl8ZAM8oh58miwTLyM2bMoAMPnJEzErJdOQF9mnz8y5WMpvS5U2vxJ/cZ8bZlXmmcZDtaH0hHaOutG+PPhdPn5/XLPuFpPjos+19zsjg9kjYJbaxlf5VEJQY2dFUhsxK+n7m8SrmTjpt1yXGV9y3DoTm7li6QZSUfGt0yn4SbX45/P8fl1gPNwdVo14yN1h8yXcqQdFr9dylLsQ5uUaR2661nEDBDLctPmOAXl00rXVvgSNr5PS73Wj8WybsckwBGhLyG+PQ2xkrXbFyMQxqyDR6aPGp63BrjViDtkaazLFst5bSoHTnPYvpNq9uyW1q6Rnvot+ff0gVF+oTTrtlDC0XjVzRf2kCc3MVmzBUC5rdSjpiwhqDtoZWwOrbMZIyFdLo1Z0ibKJqjJh9RaOX5hn/NCPKy/LelQDT6NCNq0WMZbt6uVIgavSGllhe8xvgDjciizK8pcfm9KF8ZaP3H6bFoi2ivVVnqusy6Od0YLzn3+QJNRh6846jtpdcMMk/X7lsqo6j/Q7Ir82n3pL7S5InTom0rknPLl9W2P8h03ybvT0lD6KUz7rxakWJpGH09Ll+tLrN6mZiLzw/LQZfpcm85p1s+7ZLOsbzHxy4CI0JeQ/bC2xgrXTo5mtOjyaS2uJSIdZK1JzftOEd5XdGsszRbIcvFtN8qjUWLMzlfJV1Fdo7/5u89+DT+r5Xa0zkuc1bbVfhbEhXVGSd3sRlzhYAFrZQjJqxBypGfrLEdoim9qqFNPHlPc0r9b23yNBu5xh9TaCszXlbSIB0RrX5JizTC3KBohltzrqVC1OgJKTXephdWaRg5DUUOZycE1DKe2kInZIy0qjt9dVJmvXGJ6Wtt3vn+4w6d9mTEp3HFLVE0F7R5weu2wA2GvM+Na+Nefn4UGTuZphlp6ZhyWeTfpSPpy0qHWv7W6rbqyjuqNSo6h1juUda+e6eXKO/Y9vXpkWaej9fH+8tvndD6VNOplr0R+v1UGgHyGtKRtVr+lInm9HDfWM6ZlNeQTpBb9zi0xQqvX0MZXa/JbEz5mHxV2ZyiechR9qmkPBlH8w/kgkTqGNm2NWZFtA9Tf8bJXWzGXKEORoid4clHCDXHjOcf4hjNWwWqRswktZxyaShlee4Qy9W7xj//LQXCMviaIZVOtDSYsn6tXSk83HhqAiQFLR/laaxevUGU48zpsBzsqmH1fysOu0BHjSt1WGb5eBUyimouX5dWP5E+X0Jl5RhadGvgzgWXL66LZFkpr/y3tbjikRuf5h1FntfTwCOqXr78i3VyW0d/f/OpD/w7P5WCO7quXK2us5vHyTp/mL/gV7QlQ9Yl9Yusx/ef/Fc+Ka/ScZM6y0M6z/V8I1ZeiZodQglrrss+smxfSEaL2uD1t0JbDCT/Zcp2yrdoB2Vpsp5qSd0hF4/8HpEuNyE72QrtoblQAnFyZyYA/wRwp3L9E8ArsQ0oVyHz8nEGHwh/j+f3kIMj85ZByPEtcnZCbcascLmB5RExzYhr363fllOh9Rk3btKB5vRagsLrksIWMnx8QSTv+/7Q6LWEMMYxDS00tPq0/G0KbFVGtCsyGxMh5uOnzQHvIPm+nDQp74T5Oenv+0tb+MkFGB873r4sqy36tLKST76A5Q6f51dbBMfKsox48rqlXrTkTKZrMifTtW0Xlrz6aJNVv7y0vcNapNcqr22LkM685Mf/1pxfqx8C/XgdjSB51eRSc4g1WWoul88r57EmNxaKnshY92L1eAhahDgWFThnlaPIqZTQ+NfkTNp4+eRLs/mWbmt13KQ+bxFxMmkmAH3KtRmAqQB+F9uAchUybwmrJoRFq5FQR4YGQhN0PjFkPsvx0hzIIjq4QywNklVOKjDpwPrf3Ii4tvKOBG8zH7VtpoXf14yzXEU2G9JmB8mVrdXnQHOfy3o5vxokvaE8obqkAW0XYizbNq7URZktijYRNY+fjy5qDqzPZ92TF5E+H7kjLee+dIJ8HRq9RXTVavoWJ56fyxfnV9Npmn6T+sff5y+Nhk6E4E6k9l1z/rR8+otwjn9rfLjDqukW66+dQ06xv1ZeOc+jHDvZD7zf/ZzQjLwMCPAFUz3viJFXzX5oMqvNdWlzZF3cQbbsU4wDaTngVttFzlVRfWUcYquuEM8WTaE2YmmJKVNUhxV05E+QpB7V5EzzH7isWLSUXVRUsAiJk8moTMCb4f5S8iEA1wL479gGlMtk2DO9ZMkSWrJkSa4j8sJczL10BDVIBaA5mHwiSCdOa0dTrp5maRg5HZynJUuWELAkV69sTxMGqTCkQpfKnpfhfSEFQhoFeY+3z7dQaHVKI6QZnKlTl9T7oJl/a+yLFGRoHpRVahUIp5x7bRtXeQ2HzHpweSUKGw/NYfSfUsblWPP5p/2xhKawtUujg9PCedCcBBkl8/KqyZ2mFySN0snVaOK0yggpLyf583Ry/mV98rvliMp6/LXJJo7/WCfWG15Zn98SoTnPvIzfk8z559sp/FM1SY+/L/tLM/L+k89B1gen0giSV21eSZklanZi+BxS+qDpt5aXf4b0r1Zey1eUV5vTGpYsWUInn9zwMUJ5tbqKeLZoCrURqqOdfET5vgfy418k35KHkF7R2tfGvWisrXJlweqIk0MzAXgdgC8DuBvAjQA+BeDh2IoDl+p48kHhadpkKtOZcgCNzjKFX9KlCYKcGNJB1JwA6WDx+rjDrUU9+Kd0umWfaRFc2SfewZC8hPb2aY6m5FGOmVaH1e+en9D4abDGuJWynYRoq12Z6rjMlkGRseKOLZ/nMcaFw1oUSjnjCzFN92j1eWhOuaWTLMMiHXVZh2V8fPua/pCOns/LX/yT7fF2+cktnE4tKqylSxq0K/ZPOorq0vjX+k+rQ+tjjW+pT0N6nkaovBbJk5zPnmcOLRjCf/My8lP2JW9LK2/RGHJgpZ0OQbPHVp0yXdIs29faKWojVEc7+TgNWhofQ2vvsPRXeFmez0MuJCWKxiY2T4k64mTSTAAGAfwFwJbs3oOxFQeuQkfpnHPOoXPOOSfXyVKAYjuCD2ConBzQkJMuL66QuaGXxkMKqzSMDTod/5xmTXH7+5qCl7+9gbK2Psg6tAiLFBKivMPNIyrSmGgGkPcT79stt2yMv9WuBTnGZYSqCgFsEe3KVMdlVoMfCy+v8r6VXy7yrHJFyl8bL+lkdwLSOOy77zkEnNPUNndmtX9c9Pk0h9mDz3/59IVHQnl+qe80GeLtSfo0h56nSwfU81/GAZa6hYho/Pg8LfK7z6c57T6q7GnnLxt6+vkn55Mov5CQY8NR/z0i5ZXLA5dZzcaGnFv5dMNyfDXnyUPKQqx9t+rW2gjpk333PYf23fecHD9aGQtlnlTGOPpWnlh6imiR3+X4S39H6oIiXawFNHxeLagQw2es/o5czMTJpJkAHAzg5wAWALgAwLsAzIutOHA1rSS4wnefbn+LJRgxk6RIWKw0LtR8YnhYDmcoEqspFcsR7e93b23z/T1EzY9HOd1y8nJaNSMnjTVvW+aThtSiWxotKUiWIdbraow/N4raeEi0o1DaUT5toioD2zGZ5ZCGT+7HC0Fb1MXAUrZWG8MFP1/9HmJOg3TgpN7jea25Lbdg+TZ5GRmN4fLE71myKvWURhNPl4tkJ6u13L2VV7b/lENe/Gi00HYNvmDgjjJvR9Kn8SF5tPpe3mP9M6LkVeNHnuSk8DiUJvtP61dej7SPGqS64GW1haFGX8yYWWnSxmh0WLT6fJqTGLYYwygAACAASURBVNJtMWlyLMrau5j2vMzy95R8v8unzNLe8/yyTs351eZA7LgV8R6br54eJ5OFGYBVAXwEwNUA/gPgXADvjm2gXscRAOYAmHPeeec1ESs73RvYbjgocmKGHFDL4eOKVhNgXrfmJGsOhjYJOU3Wd23xIennDr3kUQo6LyPplu1p9ci9lFp/cmUlnYkuOq2dRCUG1l/DIbNEfEzjHWLpwMWiXee5U3DOWVheLQMiv1uG0jK2lrHhssbzSecyRJ8Hj0rLdngAwy3i3b1YR1jqHf4pF+MynbcvdY+0KXzOWfpYm5OBMRtR8qrxI8/6V3gcSpP611rUarbSmlfWYkPapxCKHOZQGj/JKOTYWnTELAas9kPOnxyLIr0X6iOrPS+z/mQcKStyDLQ+Co2fvB/SfbJ80T0NRfnq6XFyFJuRnNBNBjAdwJ/LlBOXygzvuDIGtmrEOF5anlbLaXk0/qtyBKUza7VhpcU45UX3ZV3yk/8xB1HnHn33ECo1sPzqpMx6lI0QdxrDvWgqkleNntjHtGUdDkvenOMe5/jFIL/Q1vn3Wzrkd22hzuuUcq/pibxzo9OmtWXliwHLN6LkVYP1b7Cxjkm7MhYak+EIfhQF3bT5GcoTI4ehtFblstU8PELs88iL349tN8YMDLd+riNKdjJyQjicKGxwjz32AABcd911HSalN5H43wPAmOI/6zYBBQjK7BgcrxwS/3sAGFP8j2h5BcbkmOWQ+N8DwJjiP0pmx3WaioSEhISEhISEhIReRjcixB1HlmVHENH53aajHSQeegejhY9exWjo39HAAzA6+BgNPPQyRkv/jgY+Eg/VYrRGiI/oNgEVIPHQOxgtfPQqRkP/jgYegNHBx2jgoZcxWvp3NPCReKgQo9UhTkhISEhISEhISIhCcogTEhISEhISEhLGNEarQ9wT+1HaROKhdzBa+OhVjIb+HQ08AKODj9HAQy9jtPTvaOAj8VAhRuVLdQkJCQkJCQkJCQmxGK0R4oSEhISEhISEhIQoJIc4ISEhISEhISFhTCM5xAkJCQkJCQkJCWMaySFOSEhISEhISEgY00gOcUJCQkJCQkJCwphGcogTEhISEhK6iCzLNsmy7Nosy+7OsuzfWZZ9un5/cpZls7Msu6/+uVa3aU1IGK1Ix64lJCQkJCR0EVmWbQBgAyK6Pcuy1QHcBuAgAB8H8AwRfSvLspMArEVEn+8iqQkJoxYpQpyQMEqRZdn4LMvmZll2df335lmW3VqPNv08y7KJ3aYxISEBIKLHiej2+vcXANwNYCMABwK4pJ7tEjgnOSEhoQMYdof4Pe95DwFIV7rS1bg6hU/DGVaPbwM4i4i2AvAsgMNjKkkym6505a6OIsuyzQC8BcCtANYnoscBoP65XlH5JK/pSlfTFYVhd4ifeuqpwjwLFizAggULhoGa3kTif2zzXwWyLNsYwH4ALqz/zgC8E8Av61mio01FMjvWxyvxP7b5rxJZlq0G4EoAxxHR4hLljsiybE6WZXPuvvvuwvxjfcwS/2ObfwsrdZsADdOmTQMAXHfddd0lpEtI/Cv8EwHTpwMf+xgwdWp3CBtZ+B6AEwGsXv+9NoDniGh5/fcjcI9k20aar4l/oJn/Rx4BjjoK+PGPgY0qmWmjG1mWTYBzhn9GRP9bv70wy7INiOjx+j7jJ7WyRHQ+gPMBYIcddiiMiKU5m/gHbP6/9jVgyy2BD31oGInqAaQ9xAkjA88+C1xwAfCb33Sbkp5HlmX7A3iSiG7jt5WspuHkEadFixZVTmPC6Mevfw1cfTXwxS92m5LeR/0JzkUA7iaiM1nSLACH1b8fBuDXw01bwtjDj34EXHFFt6kYfiSHOGFk4NFH3WdyzmKwG4ADsix7CMBMuK0S3wOwZpZl/qnQxgAesyogovOJaAci2mHdddftNL0JoxC33uo+L70UuPPO7tIyArAbgGkA3pll2R31a18A3wKwV5Zl9wHYq/47IaFjGBx0ZnYsmtrkECeMDDxW990i9qCPdRDRF4hoYyLaDMAHAfyZiD4C4FoA769nS9GmhI7illuAWg2YNAk46aRuU9PbIKIbiSgjou2J6M3163dE9DQRvYuItqp/PtNtWhNGN55+GlixIjnECQm9Cx8hTg5xO/g8gOOzLLsfbk/xRV2mJ2GU4plngPvuA/beGzj5ZOD//g+49tpuU5WQkFCEhQvd51g0tT35Ut0JJ5zQbRK6isS/wr+PEI/FZWsbIKLrAFxX//4ggJ2qbiPN18S/xN/+5j7f9jZgl12AH/4QOPFEt41iXArDdB1pzib+LXiH+PnngaVLgYlj6LT6Yf+nuh122IHmzJkzrG0mjAL419XXXNO9YDe6oL3w1jNIMptQFl/5iruefx5YfXW3j/iww4CZM4FDD+02dW0jyWvCqMXllwMf+Yj7/uijwIYbdpeeihAlsz25Vr/nnntwzz33dJuMriHxr/DvI8TPPQcsWzb8RCWYSPM18S/5v+UWYLvtnDMMOAO7/fZu+8TSpV0gMiGHNGcT/xb/PkIMjL1tEz25ZWL69OkAxu4ZgYl/hX+/hxhwu/5f85rhJSrBRJqviX+gwT+R2zJx8MGNPOPHA9/+NrDPPu5Bz7HHdoHQhCGkOZv4B3T+uUM81nYo9mSEOCGhCY89Bqyxhvs+1qQ0IWEE4f773Ut1b3tb/v7eewPvfKc79H9x9H+wJSQkDCfGcoQ4OcQJPYNly4APf1gxlsuXOyl905vc77EmpQkJIwj+/OGdd87fzzLg9NOd+J5++vDTlZCQUIyFC4FNNnHfx1rsKTnECT2D2293/45z770iYeFCYHAQ89dKDnFCQq/j1luB1VYDXv/65rQpU9zfwZ55ZuO1gISEhN7BwoXAttu6BWxyiBNGPYb5YJFo3Hij+1yyRAhi3XJ+dVbdIR5rUpqQMIJwyy3ADju4fcMavv5199Cnv3946UpISCjGwoXuZInJk8de7KknX6o75ZRTuk1CV9FJ/onco8xaDTjjjI410xJuvBHYfHNgxYpT8MIL7t9yxo8HXrr/UawC4J94o8s41qS0x5HkNfHv8fLLwD/+ARx/vJ1/882Bo48Gzj4b+Mxn9EhyQmeR5mziXwMR8OSTwPrrA+usM/ZiTz3pEO+5557dJqGr6CT/t94K/P3v7nzQXnKIiZxDvN9+wP7774kPfAD42c+Aj30MuPr8x/ABAC+v14cXnl4Tq481Ke1xJHlN/HvMneveBZAv1EmccgowY4Z7we6KKzpMYEIT0pxN/Gvwp5quvz6w7rpjzyHuyS0Td9xxB+64445uk9E1dJL/Sy91n/fem3+bVMPSpe7x53Dgnntc4Hf33YEttrgDW299B049Fbj+euC+6x7Fimw83vqe9bAI66YIcY8hyWvi3/NvvVAnsc46wOGHA7/8JfD44x0mMKEJac4m/jX+vU/gHeKxZmp7MkJ83HHHARi7ZwR2iv9XXnH/FLX11s4Bvekm4H3v0/M+8gjwgQ84h/i669wWi07C7x+eOhWYPv04TJwI3HPPddhnH2DGqo8hW3MD9G0+Dk+sWAebLVzUmyu5MYokr4l/wPF/yy3uDfUNNigud9RRwFlnARdcAHz5yx0mMiGHNGcT/0Az/9whXmcd4Oabh5mwLiP5FWMIv/mN+9fjM84AXvWqhhMq8ac/AW95C/CvfwHjxjmHuAhHHw1MmNC4VlvNOdyxuPFGtyJ93evc78mTgV13Bf7zH+Cdr3sU4zbaEH19wFNYB8ueGGPL1oSEEYJbby2ODntstZU7m/i889KfTyYk9AK8Q7zeeo0IMVHvvohfNZJDPIZwySXu7dF993VG64YbmvP88IfAu9/tVoh//7v7u1XLcfZ49FEX5anVgBNPdFeWARdfHE/bDTe46HDG/nH88svdI9V1lj4GbLQRNtsMWIR1QYuSQ5yQ0Gt48kngoYeK9w9zHHOMO0Tm17/uGFkJCQmRkFsmVqwA/vxnoK8P+OMfu0vbcCA5xGMETz4J/N//AR/9qDu5Yffd3QswL77YyPPii8BJJwF77eUiPdts45zUv/7VHZNk4dxzneCcf747UunrXwf23x+46qpwOY/HHgMefNC1xdHXBxxyCJzHveGG2GwzFyGe8NyisbNkTUgYIYjdP8yx775Ozs85pzM0JSQkxGPhQvdUeO213ZYJADj5ZGDBAuDQQ92/UI5mJId4jODyy53T+rGPud9Tp7rf3ogBwJVXujOAv/QlYNVV3b3dd3f3rPcPXn7ZPfJ873uBLbZo3D/kEPe4RYtCS/itFdIhBuD2TDz3HLDRRth4Y+CpbF2MX74078knJCR0Hbfc4hbbb31rfJnx491e4uuuA/79746RlpCQEIGFC11kePx49wkAf/sbcNBBzlE+6KDRbXp78qW6b3zjG90moavoBP+XXOL+JWq77dzvXXZxE/zGG4F3vcvdu/RS4LWvBXbbrVHOf7/xRnfYvsTllzvH99Ofzt/fZx9glVWck/2Od4Rpu+EG4NWvdvuWAcG/fwV9ww0xYQKwYq11gGfgzoNZffUo3hM6iySviX/AvRi3/fZOlsvg8MPdn3T86EcpUjxcSHM28a/hrrucDwA0IsQA8NWvOmd5772Bww5zWxn59sZRAyIa1mvKlCmUMLz4xz/ctvgf/CB//y1vIXrXu9z3hx8myjKir3ylufzmmxO9733N9wcHid70JqI3vMF9lzj4YKINNiBasSJM31veQvTOdxqJf/mLI372bCIi+tzrr3a/b701XOnIwrDLYZkryWxCEVasIFpjDaIjj2yt/LRpRKutRvT889XS1SF0XSZDV5LXhFawbBnRq19NdOyx7vfDDztTu8sujTzf/a67981vdofGNhAlO9FbJrIs+0mWZU9mWfYvdm9ylmWzsyy7r/65VhVO+s0334ybx9p5HwxV83/ppcBKKwEf+lD+/tSp7jHnsmXAZZe5bbnTpjWX3313FyGW23avv979K9Wxx+qrxUMOcQHe0FnGixe7Ovh2iRz/9b9txoYbAgBevWl92TrWDkjsYSR5TfxfccXNWLy43At1HMcc4x7FXnZZtbQl6EhzNvEv+R8YcDsU/ZPgDTZwT5L5n9p95jPAnnu694VGI8psmbgYwA8BXMrunQTgGiL6VpZlJ9V/f75dok4++WQAY/eMwCr5X74c+OlP3T/A8UcggHNCzz7b7Q++5BLg7W93f6sqMXWqc6rvv98dleTx/e+749E+8hG97f33d0ewXXmlO0JNwy23AIODzun2yPH/6KPu5kYbAQDW2MIxseKJRRhfxHzCsCDJa+L/iScA4LpSL9Rx7LST29J1zjnuCMdR+Ti2h5DmbOIfyPN/223u0zvEEyY0n0OcZU5Wr73WBdImTBgGYocR0RFiIroebvcmx4EALql/vwTAQRXRlVAR/vhHt/fnsMOa03xU9jvfAe67T8/D8/EX5O6/3x2V9MlP2nsGJ01yJ1ZceaV9KMRvf+s28JuG9LHHXANrrAEAWHsbt9P/+QdShDghoVeweLGTd3+OeFlkmYsS33133LnnCQkJ1WLOHPcyfZEMv/a17oX8BQuGh67hRLunTKxPRI8DQP1zvfZJSqgSl1zijlDZb7/mtA03dCdD/M//uBfg3v9+vY5ttnF1+POIiYDp053wHHtsuP1DDgEeftgd8Sbxi18AP/gB8OEPB96Pe/RRFx2uh4w22mZ1LMUEvDhvjP3JekJCD2PxYhc5GteGRfngB90Tp/RiXULC8OO229wJMeMLHr3606QefNB9Llkyek5BHZZj17IsOyLLsjlZls1ZtCg5MsOF555zUdwPfQiYOFHP46O/Bx88FIRtQpa5fN4h/slP3GHdZ5wxtLXXxIEHOgG78sr8/b/+1e1X3nXXgv1Ijz2Wa2SzzTMswrp4+dEUIU5I6AWsWOGMYqv7hz1WWQX4xCfc+eWPPFINbQkJCcVYvtxtndROkpLwp1A88IA77Gm99VxQbTSgXYd4YZZlGwBA/fNJLRMRnU9EOxDRDuv6w+0SOo7/+R/glVfsrRCA+3c5oJ5n+XJ3Uv4xxzTlmzrVbauYOxc44QRX7pOfLKZh7bWBn2x6Knb/1n44/HC3J+mBB4ADDgA23tg57K96VaCC+p9yeGyyiftzjsGFaWGVkNAL8OeStrp/mOOoo9w7BaP1pR0Lw/nSekKCxN13Ay+95PbxF2HDDV2A7cEH3TtA//kP8L//23kahwPtnkM8C8BhAL5V/6zkDzi/973vVVHNsOLFF4FPfcqd17fJJu3VZfF/ww0u0nrWWXEvnVxyCbDttuFJ/tGPur9p3GsvAF/9uvs7O5+wyy5D+fxLb/vv75zsCy6Ifzz6wXE/x8TBAZxwxTz85CebY+JEYLXVgN/9rvlFP4DxT+QixPUX6gBg5ZWBxRPXxWueTRHiELIs2wTuBdjXABgEcD4RfT/LsskAfg5gMwAPAfh/RPRsO22NRHmtEmOd/1rte7jjDrdlol1ssYU7w/z8893b7daTrVGIizFML60Dac4m/vP8z5njPmMixOPHA5tt5gJbq6zi7l1zjVvItrNlqicQez4bgCsAPA5gGYBHABwOYG0A1wC4r/45uaie0XpG4q9+5c7n+9zn9PRrriF6+eXW61++nGjbbV0bDz1UnP+++1zeb30rsoFbbyUaP57oAx9whwe/7W25w4VfeYVolVVcnd/+dgnCFy1yhQB66VvfowsvJHrve4luvDGi7NNPu7Jnnpm7PXvtQ2n+Kq8rQUTPo/KzSAFsAOCt9e+rA7gXwOsBnA7gpPr9kwB8u6iu0SqzCdXgfe8j2mKL6ur77W+d2F9xRXV1VoyOnB8Mt0j9F/t9D4ANqCHP98TUk+Q1oSyOOYZo9dWL/zPAY5993P8H7LffkHmn227rLI1totpziInoQ0S0ARFNIKKNiegiInqaiN5FRFvVP+UpFC3hT3/6E/70pz9VUdWwwZ+1+9Ofup0HHNdd5/4N7owz4urS+L/8cvdYg7cVwqWXutXaRz8a0eCSJS7jRhu50O/XvuYa+cUvhrJMnAjsW1uCd+zwAo4/Po4PAI1zW1ZZBa/6w69x+OHArFn5f8OTGOLfn0HMIsQAMDh5Haz+StoyEQIRPU5Et9e/vwDgbgAboQMnw4xEea0SY53/v/zlT+jrq47/vfd2xz+edRbw/POVVTsS0bGX1sf6nE385/mfM8e9UBcb4d1iCxchvu024N3vdvdmz+4AocONWM+5qitm9Vqr1ahWq5Xz/7uMPfYgmjjRrZT+7//yaQce6O5vsIGLtBZB8r90qYvAvOlNRK96FdFnPhMuv3Cha+vd744k/sgj3d/UXXut+718OdH227u/qPNh7auuosG116bl++wXWWkdn/uc65jjj3cR6KefLiwyxP8f/uA67oYbcumzp55KBNDyl5eVo6V30VGZg4s8zQewBoDnRNqzReWLZHYkymuVGMv8L1hABNRoyy1rldZ7/vlO9CdNIvriF92Dph7CcEWIo2UVwBEA5gCYs+mmmxYyMJbnLFHin/O/dKnzK044Ib78mWc2IsM/+IH7t9o99+wMrRWh2gjxmAeR29i23XaNUG0dK1a4FdZhh7ljgy65pJE2b56LiO6yi/vXNnnaQgxmzHAb2L/+dbcf+NZb7bwvveReWHvuOZe/EL/7HfDjH7s35fbYw90bP94dTjxvHnD66cCRRwIHHYTs+ecx/sbr3WYhif5+4PDDm+/feKMj+tBDXUf99rcxLDuIP+XweNXGbuPxwruejq9rjCLLstUAXAngOCJaXKJcOhkmoRBeF5nHJraIT37S6dQ99wS+8Q2gr8/9S5ZXCWMEUS+tA+nF9YTWcdddwMsvx71Q5+GPXgNcub32cu84ffvb7kX5kYrkEMfg6afdgbrTp7vZc/bZueS77nIv1e2+uztL86qrGo/6fvhD51/+/OfuwOvvf79c0y+/7HYw7LKLOwBi552B228Hli5tzjs46HY+/O1vbotF4Qb5RYvcOUdvfCNw2mn5tL32cm+3fPnLwHnnAZ/7nOP7hRfccRMSl13mPHe/zQFw3vmcOe6Iih12cK+nXnWVTc/Che4183//211nneXub7BBLttqWziF/8S/0ot1IWRZNgHOGf4ZEfn3gNPJMAmV4S9/cS/4rrZa9XVPmQL88pdOFbz//U79bLGFU8MPPFB9ez0I/9I6UOFL6wkJHPIf6mLgj14bNw5485uB973PbRU96STnKr30UvV0DgeSQ1yE668H3vQm4OqrXdT0ox91G4X9WUNo7OndeWcXJX75Zbf99sUXgYsucsp8k03cKRS33hqO8OLxx53TWcd557kzOU87DcieexYHvuoPePllwp13Nhc98UR3/Ml3vwscVLQzlAg44gjg2WeBn/3MHd8g8b3vuQ19s2e7SLE/dcJLkMeiRS6aTJQ/kHDOHPf/jrvt5iTngAOAP/zBdZDE737nHPMZM9w5Lv/5j6vvIx9pOpdt8lYuQvz0QIpcWsiyLANwEYC7iehMlpSMbELbWLzYPRA6+2z3VKyTb5dvu6176nbffW79fvHFwNZbA2eeWVh0xCDLsisA/BXA1lmWPZJl2eFwpzftlWXZfQD2qv9OSKgUc+a4f5n0Tm4MNt/cfb7+9e6PZKdOda8iXXqpexB8772dobXTSA5xCDfc4BzCVVd1Xu8JJ7jtAy+84EK+ddx6K7DWWsBWWwE77uj+2e2SS9zkeP75xr+5HXaY+/OLH/zAaO+RR5zTOXcusHw5nnvOPS585zvdhVNOwdu/8R5chYPwj2vy0dFf/tI5wsccAxx3XARvF1/sorXf+IZzRDW87nXA73/vnlsCbvavvHKzQ+w9/FVXBa64onH/ppvc5667us8DD3RSc801jTwvv+w6aL/9gNe8xtW9447u+uc/3eJDYL3tXMRy8YMpQhzAbgCmAXhnlmV31K99kYxsQpv485+dyrj4YuALX3BqYTiw+ebAuee6tfeBBzp1fMopo+NfsmgYX1pPSODw/1BXZlG76qruaY3/Yy/AuQZvepP7LnaVjhzEbjau6op5qW5gYIAGBgZK7JfuAG6/nWiNNYi22YboyScb9wcHiV7/eqKddhq69YY3EL3nPY0s3/ym22y+4YZEO+6YO72MjjuOaKWViB59VLS3aJE7V23CBBoAaOC736XDD3fvod12G7nzUDbckAZf+1p6GRPpmVU2IJo9e6j4HnsQvfa1RMti3jN74AGi1VZzhWLPWfHYaSci+TLCKac4Qvv7HeP33+/u77ef6z+Pl192Z7v813+53//8J9Eb3+jKfPrTRC+9REQR4//YY0QAXbbbuVEkz5xJNHduHHtdwrDLYZmrSGZ7Ql67iLHC/5IlRJ/6lBPX172O6K9/dfe7wf/y5U6NAO7IqLJqrE10XSZD14ixsV1E4t/x/8orRCuvbB8XG8KjjxK98EL+3ksvEY0bR/TlL1dDZ4WIkp2eFNau4557iNZdl2iTTYjmz88lPfUUEX3ve67r5s6lxYvdAQ39/Y08Cxa4ewDRT3+ar/r++13aKaewm4sXE+2wg5uZf/4z0Xrr0ePv+CABRJ//fD3P3/7mKrz0UjruHXfQfRPrhxJfdBHNm+e+fvWrBXw9/jjRWWcRbbmlc/Yffrh83xx1VPOBhXvt5Y7AePhhR8hpp7n0tdYiOvzwfPn/9/+I1luP6Ic/dK+2rrce0e9+V46GpUuJALr4tV9t/D74YKKLLmrKunCh89X3378kn8OLrhvR0DUiZDaho7j5ZqKttnLifeyxzjnuNgYHnSEHiD78YacGhgldl8nQ1W15PeUUd7LSWMPgoDub99JLu01JPObOdfIzc2Z1dW65pfs7gx7DyHWIZ82aRbNmzWqP/VZx991Em27qHGKxgvzZz5wz+5tLnnbO3NFH0zXXkHrU2t5728es+WPY3vUuoitmvETLa+9wXlud5/995170c6xCr9/yFfrPf+qFvvjFoWPLvvlNolWwhJbuViOaPJlO/8IzBBDNm2fwNDDgCBo3zjU8ZYo70qwVXHihq+Oee9zvFSvc2UhHHOF+T53qIuj/+pfLN2NGvvzll9PQeS377EP0xBNNTcSM/4sTJtHFax7rfpx9tqtv4kSif/wjl++ss1zS5MnDHkUqg64b0dBVJLNdldcewGjkf3DQ6ZNZs5wDPG4cUV+fW69LdJt//0Ru//2poS87i67LZOjqto2t1ZydfOqpjlRfCTrB/+LFbh5+8pOVVtsReP5/+lNH87//XV3d730v0XbbVVdfRRi5DnFXzggcHCQ67zz3d2xrr930tysLFzqnCnA7G1Z8dBrRGmvQGae+SEDz8bqLFtn/KPfMMy6a+9q+ZfQrOO94xp4/HWrydWu/gWoA3fkd5rRut53b4kDOKAFEN/zoHzSYZXThmsf7pGbMm0e00UaOpy9+keiuu8r3DYdfUl5+uft9993kI9VERHTOOTT0HBMguvfefPnnn3crge9/P7+XhCFm/Bet+Vq6YtyH6L45zznedtmF6DWvcVsw6lsviIje/ObGOuBf/2qV6Y6j60Y0dKVziMMY6fwvXUp0/fXuoc306US77uoeAvl1a5a57QnPP6+X7wX+zz3X0Vmr2XRWiK7LZOgaDhv75S8TXXyxnuafJPzP/7RcfccR4v+224gOOIDouefK1emf1L5n70G3kozav9gdeP59nC3m/xFiceKJRBMm9Bz7UbIzMl6qW7QIeOIJdz35ZPVvUbBj1QZ32Q2/+NKdWLL1W3NZjj3WnRrx1a+6DeN/7DsCWLwYa1w5A7ts/gQmL33CHf5bxzrruLMzNay1FvClLw7ivtp/4SD8GhdPORtH3vARTJkCbL89cP/Ta2EQ4/DGB+pHlD3wgDt7qH50xA47uKOO/rRwezy53ycw7bmz8d/7KOcQLVzojk9bssS9CXPaJJNc3gAAIABJREFUae6V7Xaw3Xb5F+v8C3U77+w+P/ABd87cuecC660HbLllvvwaawB/+pPr0CxrmYxVNl0X649/Cr/Z9RugZ54BzjkH+MlP3It4p5wCALjzTuCOO9zpHoA7ErkUli9v/tvBhIRRAiJ3Zugb3gC8/e3Af/+3e1d4pZWAj33MHU9+001OrV1wgRPdXsWRR7rDcm66CXjHO5zJSOgMHnrIHQV6wQXNaUSNkzdH4j+XLVzoXticNcsdKVgGT9ePxZ9079/diUr8BfMexd13OxM9cWJ1dW67rTtc6sEHq6tzuNDbDvHgIPDxjzvHaoMN3LX++u6/Avl5t+3gP/9xBwjXj1X78cF/wP87bkPsvDNwzz0uy6xZzlB86UvAF78IvOUtwDGX74bBbV+PI/75Kdw8r07bOus4rVwEIuD445Fdegnw1a/i43P+G48/7s4sXmklYOVVxiGbPNk17K0W4CQV7hD8N7zB+aLfnfRVLMMEHPDXk/JtPPecOyHjscfckWbbb19Nf02Y4OriDvHqq7ujNQBg3XWdEz446F5BbcPpDWHVTdfB7pP/jaOXfR+X0jR89mdvwbI993HnGJ95JnDttbj0UkfuKae4aVPaId5zT1fwqKOcpa16IZaQ0CX8/e/uf3gOOsi9XT5zJrBgAfDMM84R+OEP3Xm/u+7a244wx4c+5A7OuesuR/ddd3WbotGJCy90qvCuu5pV4gsvuPgL4Bzi5cudOq7KXHcKl17qYjT77OMc23Hj3HFkZfBM/QyQVz1W9wTb/GvoxYvdaafLlrVVTRB33VX9KTG+vpF40kRvO8Sf/rQ7v+yYY1zE8dxzXYj25pudU1bFX6KceKIbuauvBk44AVf+ahw23NAFonfYwR2Le9RR7pihE090gnLaacCD8zL8YOovcCTOxXWH1mnbcUeX+aGH8m3Mm+fCFm94g7u22cb9Q8dnPjMUzVxrLcfm7bcDO+0EZOut4/6W6bbbnJbffntgs82Gqtx5Z3cS3PlXb4g/bH8iJlz1S+e0PfOM+0e93Xd3s/1Xv2qcH1wVdtjBETo46BziHXd0UWGPD33Ife62W7Xtcqy7LlZa+Bgmvmoc7v/41/Hd7wIf/jAw+O0zgK22Ah12GP730hex335unbL77u4UvWjMnes8g803d3Nw6lS3lP797zvGUkJCp/HQQ+5o7512cmrvRz9yD1UOPRTYeOOOrV+HDfvt5051fOEFpyNH8r9m9SKWLXNn648f746wf1L8rc/jj7vPXXd1c+3QQ93xeOyU0p7DkiXunxEvvNDRf8kl7kFoWYfYR4jXfWWB+3LNNW0FUX7wA+Dzny9pt0qACLj//vYfGkv42NhIXJD2hEN8w/dvw9VTvwW6l/0D2kMPuTDFZz/rTn8/8kgMHnEkjnrsS7jlR7e7/QgHHeQc0P/8p7WGf/9796j9M58B3v1uPPWU84E+/nHn773xje4g+CeecErAP1bYZx/nYx5/4etxHo7Eap890j2z849IDjvMnU4NuMJ77eWe3W+zjbv8P8N95zu2BZo82WmdCy90jq74p42dd3ZB4OefB9Y87bPuX+AOPtid5Tt9umv/yitdNL1qTJnilq//+pfbl+C3S3i8//2uTz/84erb9ljH/TlHdsIJ+NqMjXHGGe4s5pO+tiowYwayBQvw8UWn47D6X1BMnQo8/DDw6G1PuGfBfnwsXHCB+0OQP/7Raf3LLnMnkO+zjzvoWftzkYSEHsVzzznjus027s97Tj7ZGcOjjnJPpUYTdt3VOTPbbuvU5le+ov/b/KjGCy+4UN3117dcxe23OzUPuH8eu/JKF4964gng6KPdfRkF9JHgj33Mff5v/f8xn3gin++hh5r/8HRw0P3LqvchFyxwR/NzLF3aON6+Jdx7r/sHCqa/b7jB1XvVVc4h/sAHnIm77TZHy8MPu5gWx7JlzXR4h3hTzHdfHn1U/1dXhiVL3NMaiRUrGltSOvWvjC+95CL4VTvEa6wBbLTRyIwQd3/D/9NP08KJGzXe4NhpJ5p/6KE0H3BHdrEXr/wBBXvvTW4XuD9zZ9ttyx80u2iRewlru+2GXsLyByj4l9teeYXoS19y74lJ+BfbVl5ZbEi/+GKX8O1vu7fn3vhGolVXbRzaGYH58+fT/PnzG6/rcqLquPNOd3vjjd2ZnPSLX7jzTo4/3p2hbLywVgn8i3X+UNKrrqq0+iH+Q5g1i+htb3Ov9pJj9+ijHTnnnkt00yaH0hKsQq88sICIiObMIRqH5bRwm7cX0/zii+6tomnT8vdfesm9cg8Qbb890d//3g6bHF1/ESd0Fb2kEzVeoxi9zP9997kjvtdYw6mSj32s6STJttGr/L/0EtFhhzlxPeigIVVRBbouk6FrypQpRLfc4hg/6SSVgaIx+81v3HzZZRd3csf++zdM9OabN14g+9GP8uX8qQV330209dbu/ekNNnDj4PHII870SrXiTwT6znfcUfmTJ7sDi7gp+9rXXJ5bbzVJD6Nu5Od/85tD/B9/vLPjQyeUDA7SZV+6hwAnK9tt545TG8LDD9OPz1lOANGNNzZuf+UrddOCA2jpqmvqHSTQ3+/62R/a5PHb3zb62xjCtjB//nz68Y/nE+BsY9U44AB36uoDD1Rfd4uIkp2uG9clB36QlmIl2ge/pTM3PIMG3/QmR9b73pd7TXHZssbbqyutRPTss/WE2bOdxE2cSHTmmc6ZWbLEXdbBlIODrv4JE4juuGPo9j77EG22Wbwvud9+roxZ95vf7Ohif6BRCmee6RjeZJMmopYvd7dPO621qtvC0qVOg6yxhqPv8ce7QEQzli0j2ndfd6rEVis9SMvGTST6+MeH0r448XQaWsXsu69d0UUXuXw33KCn//a37lg+7xiffro7fLoVuHHtuhENXZ0+13TZMrc+TagGK1YQ/f73Tj9lmVNFH/mIWyePNQwOugNtxo93zpU89KZFdF0mQ9eUKVMa0SNxAPuKFcUnCgwMONXe1+eq8Hb3jDNc2nPPuX5dfXUXEyFqnEt9el3FLl7srqVLnePr/7jqpZfcfzsB7uTS5cvd/cFB50BnmdPfm2/eiAX95S8uj7d5ANEnPlEwQgpeeolo8LP1IJr/cyhyMat3vYtlvOIKIoBej3/R8cfTkGP6xBPkPNfx4+nGDd9PE/AKffSjjWKf/rTLdxveQg9vt48j9v3vp+XL7eMAd97ZlTnhhPz9Aw5wR/Rvtpk7ur8T8IuLF1+svu7773cO8Rvf2PznHcuXu//oGmZEyU53jevPfkYE0Mk4jU4+2VHzy18SzTz7bJrpj/Wqw/soX/gC+f+naGDRIjeD/Mz118orEx1yCNGvfuVG4KmnXPhw111pKIpbx7PPOsMhJ2YIy5Y1BDoHH30eN47oyivjK6xj5syZNHPmTLe88keYKVi6tLOB4CB23NHRtummlVc9xH8LeOEFt5oHiB7/6GedVp07l+iOO2hpNoFmr/E+F/bPMvuPSXbe2T11CHXu00+7c6re9jYaWqVdd105YufNI1p/faIeMKKhq8ghbme8iFz0aI017GMKex3t8l8VFi92U3Lrrd2UXH99F4F67LHOttsr/Ifw5z+70xknTWo+M74FdF0mQ9eUKVOIvv51Nwn6+oaIfvFF9/9Pe+9tjNlddxFddhm9Y49BWnddpx69Xf6v/2pWhzvuSLTnni5usNJKbsF13HHugSjHvvsSvfWt7vt3vuPq82fx+wjitdcSrY7n6bpDz6Edt1tC48Y5sz1pkvvTFSKiq692ZbbYgujVry53LNoLLxCtuSbR/Cmu4ZmbbkozZ86kxx93dX7zmyzzPvsQAXRY9v/Z+/J4KYqr7ae5cNlkl0VkE9xQUBACKpFBgtEYo1Gjoq+4RcGoMcYsLlEvMdHPaBazuGEUNW/eEE1MJIkbqETjEmVTUS4qIIuoICA73GXO98eZnqnpqaqunume7plbz+9X9/ZU13aq6lSdOnWq+mGqqeHFVPYjW64gAtBsfI3a1zZl71s+91yeDjegB7028lKiCy6gdI8edOIJzQWabiLeQG7VitPv0SN3W6jrf8013FYl6yM+/ZQHBqEAs2bNoov3n0rj+kanwn32WabjRz/K97/xRl5klRlGvBPf5Lp6NVGXLvR256Np+NBGamrir/wOHUo0fnz+HYG7d3NHGzOGV7j77iv5Ek46zWYDP/tZzl1xBS+zAOasNm34+ZBDmDMFafYPf+BXr7xSUqXn8O67wQWkDPLuSJw9mzt00nDppVxhEXySptQ7MjdsYCUubd7MI00qRTRsGG3dqw/tjQ30+VurWCCWfV9y8WKm61e/Ms/w/fd5l2LixGAFnTyZ+2QCJlGdi/Ie4uXLc/dET5yY6I+nKBH3PbyrVxN9//u5DZuxY3nyDvNuUR3ipt8UK1fyBzUdh2jGjJKSip0ndW7UqFG571oDRFu2UDrNmkbXa/RoT5s1N/NuF0C/rvkuffeqdNb7+eflm63nncdz8RlncJq//CXRWWcVCjsXXsjhiFizu88+bGoAsGkGEQ+FP213MxFAe8ZNoPnzWK14xRW8ybphA3/woU8fnqMBlvFM4Vr5revGX3hNAZQ65pjsvJ81G9iwgaV7gGbu/X0C2BSve/eM2cell1Jjx870PdxBBNDxeIp+8QuO+pWvEB09cgcRQH8ecQvRgw8SAXQg6gngaULE449z3jfeyP//+Ef2f/FF/v3kk/xh2G7dzOmUYsoUTlAw20wddhilAFrQY1KJiesxZgxPvyIOOYSLs359pFl7YcQ78TBrOk00cSI1d9yLhjjL6cYbucSPPsolOvjgfGZ1P0T27LP8+9vf5u0WrypeisZGVglceCGrfxctkmr+vv51or59kzEhV8QEc//9lDX4Chmh0v+b32RngUW3PklARlg+8UQepb23h19+Oe8seL+04gd3r9DUrti18eNveMc+iepclALxpZfyhDd9OpmY3CUScfHrokWslWrdmrVMkydztyo3KmK8ymD7dmZ9zwZhUMTOkzo3atQoXl26NgevvEK33caP11xDVNsmTSM67EMpUXLNmAlsH/lFIoDe+9Kl8snwj3/ksz07dmS/EDikZiU9iRPo8tPW0THHFApA117L6/50mjXKY8fy8ArwsLlxI5fp425D+VBMTU1WufD22xzuqM5L6B/4Kt15CX9dadQoXtzQ9OlZyXjNGtaAf/AB5/vb3+Y+oPrXv/IZkganDdHQoZQCqEfrEdSxI+tMsqTecw9n2K0bvbXvCQRwGc44g+WD9KhRtHy/L1Fb7Kbmzl3oXz3Po0MO4ahjxhBdNK6eCKCfHPQHeunO+UQA3TrqL9Kx7VvfIjqu3YvUdPoZdMB+jXTssex/331chHWPvkTLhp1GXbGJNm0qbIoCNDbyuao778z5LVmS6we33MJ+6TSN36sTjXdXR7LPT4aEyy5j0xq3fteuzU7HWVOYMsGId+Jh1jffJALo5bPuJCBnxtvczGa37dqlaPz4FBGxbVKfPsxkrhw7bx6X/M9/Dqemtm1jAfuKK8JJr1RUxASzYgVvx4X5zccMQqW/oYFo/Hii66+n7dtZeLjuOuJDdUD2c9lExJ2tc2fKMwwzxZYtvAthojFPp9lsp3dv96RP7JOozkUlEH/8Ma89LrmEq+TLX+bt1gQdxDBCEPp37GANkNTUygDpNH91fdIk7r577UX03e/Ga25SEeOVgD17ePHgHlgqwuwsdp7UuVGjRrERbsac6+0rZ5DjMM3pNNF1JyxkDSnAhqRr1xIdeCDR8OH04P1NdCuu5co577x8hUE6zfYKANH48fTPP21lUwKcQwTQdX0eoCFDiM6Z3JynUnYPy23cyOY83/gGETU20r69GujCC3kePwyZnbm77859i3vlSiIi+sPVC2lru72JANozkY2R77iDqA32UHO79kR7703U0EC//jVH++1viWj3bho9OmenfPvtRPshY4L4059SCqD+zhCaOpUoz3Jk/Hjepj73XGrovS8LsU1NdP/dDdQWuyjdujX9od+1NHo0EV14Ie1u24naYyft2pmmg4Y00i0TniUC6Mw+/6avTNhJzXCo8cbpNGAA0amnZvLI1M3++xP9txefVrzz7NeoXTuihq276MaL19Gpbf9J6Q4diAD6Oh73P/i2Zw9XLMDnW9xOfdppLJEOGZI1lG7622xKAbQv9qc9vfvxycmIbC8zSnKqr+ff7p0DANG990aSJducrFvnNY424p14mDXzed/zjllBQ4bktwXbCaWoVasUdezIHRrIP9/U1MSWEGEZm7ua6RdeCCe9UlFpE0zYiJL+MWOIjjmGeKDfZ5/soZP06jW0ev8JRAA9d7PiMJ0frr2W9/+9e2NePPYYd7jcvm3sk6jORSUQ//CH+dW1ejWvR1KpZOzUmMKU/s2bee4B+LDMz39OZpof4sXDXXfxIRWAu+5ttwmHi2NEJY5XTU05q69p0wIvUGLnSZ0bdcQRrGW97jpq7tCR7qm9kkaMyB18Wz35BzQeoDF7sZDpuj2P/p01enulqfnHbL5Ad9yRo9rd1TrjDKKaGtp5+Fj6Il6kZrAG8l5Mo9paoleGX8IHOTKV6p7ve/ddovbt+VYHOvFEervzUXTk2DRdfDHRL9teQ+maGt5HX7KEIzzwANtL9urFNpOXX54VBhYvJjoG/86V/6mn6KSTWAv8woFTKd2xI53U+qmsnfKllxIdj6eIAPr8ny/RUailw2t75bfqxx+zNvXmm3n7AGAGPeUU2jk2RWPxKhFA32j1V7754VkWfq/EnbR7yFB6rM3Z9Idj2cZ4sLOCAKLPehxAdPrp9M1vsr6k6Zm5RJ070/rrfkndsJGaatiMc9GZtxCQpl2DD8nRdOihlHYcuhE/LvgM9p49bK+dWTPQqgvrKGt3BvA1VO++y891dbxqbteOaMcOWtfzMBqDdnTQgeNz6ui5cwN0f3NkdJ9Zc5D/+R+W1zt25AubiPh2jtAubHr55Vz9de/OJjDc8Y14J57J9ZxzqLnPPtS6Jk0//GE+Pek00ZAhKerXL0Xf+x5bOdxzTyHdU6dypapObwbBWWfxIjMp396uxAkmTERJv3vFzquvEqV/dANRq1a06+e/o2213WgbOtIVnWYSwOOH6pISIqInnmCBJu+CDVflOW0a/06niWbO5MH8xBN5Zti0ibUsw4aJs3Dsk6jORSEQb97Miouzzsr3dzUKv/lNoORihQn9GzbwwaI2bXgCGJ+5/a9DB+4uS5YUxlmzhm9IOOaY3K7nYYexlqVc9sEmqNTxKp3OHdI+66xAdRo7T+rcqGHDmKj776cP+4yl551jacWKTMmbmyndvz+NqOlONa1StPTmR+lHe99Lp+MxeuThNI0ZQzRhQibsyJH59g9XXcX2TZs3E/3975SuraVmpxU1duxMmw8YTfNxBDloph0dMzfwPPwwERE99xxRW+yil370FLVCEz122fNZoeXc9n+hQQOa6dP2A3NXUaTTvHt2zjm5nbynnsptF48fT81Nabqjw03U7LQi6tyZms89j7p0bMxqqxt69KE9aEM/wM9o8XcepCnHrKTv4E4igJ6f9Skdir1pXE0tDzjuFrV7Z9yCBbl7zx57LHvI4c2+xxMBdOJha+jtt4mosZH2dOuVpaUBrem1L1xBacehNthDNTVEO088jeigg2jWLKIT8CQ117altOPQ1pou9KM2GaG7WzfaceREGgNecDzc8Vt035EPEG3eTM37DaE/4ww+9LdiRVbV+swzHPWSS7jo73YcRf/GMfTJ66v4xa9+xafZWrXi6zEyJxKbL55KBND+nYYyz+7axVqICy807vxB0NjIcvh3v5tr1rPPZtOW447L3RvgnQeKhnt1xu2386D5gx/wsyHvxDO5DhhAK79wBgHy+wQ3bNhAG3zuYXI7hOo62Z07ze7bXLmSJ6mkmEsQmdFfzYiS/vnzeSEFEE3afyWlM5LG6xhNM37wHu3enbtqeNw43k304v33czsXNTV8wcns2ZmdjqlTWSiur2cuB9jgzb0vKHNgg55+Wkwy9klU5/wE4mLa66c/5WrwXh+eTvPaoX17f0V7UuBH/7p1fJdpu3YZ+/UMFi9mc0y3L02cyLtVv/hFTpMMsFb4xz+OxDopFFT6eOWa/3/lKzktqg9i50mdG3XggUQA7X7yOXqk9pv0edueuZK/9BIRQMtuvYd69txAjsMy7j778O5Z27bCTUvf/z6/3LGDF+/77MOHbVw8/TQPpnfcQduvvI4a0JpG43WuzNpa3gbZs4feeX07PQu28fkTzqLPDjySqF8/2tj7YHoHQ+lesJBG4s1SZ5/Nwq+rrXK1E7/7HYd95hmq7zmOFrb+AqUvvIgaO3Siv+JUIoCud26hu2/ZRC8jx0RLakfSX3tdSpvRhc6bkqZv4lba4DLYvvvyltSFF/IJtuZmFh4yWloCcofz+/TJ29Le9p3raT32pr9/je833lPbkXZ270tAxkTippuIWrWiDSu30cfoTSs6H0Y/Ovp5aoZDza1q+ATiVVdRum1beqDNNNrTqi11xue561RPOYXqa4bSJZcQbR85jnb3HUSUTtP3v8/F6diR6JV/bqRmOHQTpvPtW/vvzzufgwcTHXccbd5M9OSsLayBB+hNDKcZ936a49nzz2f1dUR3oR15JC/qXW3xAw/wOb9+/dhsAuAmbm5m89W//S1Xxa+9FvCqxJNP5tsZiDiTdu1YU2zIO+Vn1sye34xhd1K/fsVvjTY0cN/1fjthwQLeWemauRf7hz/Ua34vuIAHgWKvkbWoPGzZwtYKY8cS3YTpdHvH6TTv2Xz10J/+xIPNkCH5p2Gbm1lp0qULHwq45hoeIwG+oojeey93+WtNDR9kaGriiPPm8envq6/2Fin2SVTnwr6HeMcOHgAL7vDOYO1a5t9x44q3tU0KVq/mOa9jR/XZlc8+Y/MHd80EsDb51lsLL+yvOAS5GytGzJjBbDtunJEZSuw8qXOjMhcIP/6LFfQdZAx4Tz+dbQyHD+fV5rZt9J//8Pb1zJm569AAHvuIiLWyGeGTnnuOn70Hd9y7wjJXJvwfMsbZ7lb8scdS4yHDqQmt6Lnek3OZ3HcfvXXjo9nfG6ddl2876X4lq6aGT5+52LOHz64cfjg117Sm/4draPl9c7Lp/PfsXxHAG3DdujTTET0+pIfGsInmrtpO9EarL9BeexEBafpk/prcIbp583gn77TTOJ90Ondty/Dh2Sti6eST88hPNzVTlw4NdOJX0vQuDiYCaPfIsdStG58VyJrHZVZdp+Ev5DhES4Zn6uKmm/iqDYCaUENP1JxKgHBb6w03UCNq6Kj911MjWKBtmPcyfWH4Lrq349U0CCvooq5/JQLoK51eYnlo2rTsXXGN9z9IqRRn9fkhvEA4GX/Pv+Hh6ac5wN/+JunqpeOKK/isw6RJvE5au5bHNoDPjbhdYuFCyt77fOutfBPJRTUP0bcGPWlm4pxO82TsCoXLl+cUUIa8U35m3W8/IoCObDM/a0PixcyZM2nmzJm+9J9/Pssdgwax69uXKWrblheYF17Iv1Mp+bcj6ut5R+Gqq3yzKitM6a9WlJP+5cvVc/Yrr/AC8+ijc+O+O87//ve5cA0NuVuOHniAWO130EFBjvzHPonqnJ9AHLS93Is/XnxRHeaRRziMe6VRkqGi/4MPeO7u0oVN2/zQ2MhyR6UdKlS2v3ua6ktfYnvL2C5NN8Ojj/J8MmSIr01j7Dypc6P69CGqqaHxRzfS8QOXUnrECNaaue7GG7Nt5jbJhg0srACCRm7bNhYorrmGJZcePdQq9DVriIW6VrRn4P7c1uefT3TwwZQePpzObPUode1KdDFm0M6TzyRqaKCP1jTTTJxPN3f7RWHXWLGCspKSd6Bw7aoAmoRn6ervNNFT3SbTzfs9SB99lIs2aRKbf+zbp4mWgG1z/9Xtfwgg6to102e3b2e7JfeUqvhZWvd7BbfeygqNc86Rfldg+HBeWNyIzKfqzjgjR8/SpezXvTulO3Wi7Rt2spnn8uVExx7Lp2G3bMkKsN/AowRwNCLiE38AXYdbsoS9Of4Kugr80a65XU6l3+Jy2l3Tgf7njD20zz5E6Vl/JldL/8Opm7My0W/G/i892fsCGjkinc+zDQ2soYjoCyAzZ+ba5MEH2e9vf8v5nXAC/7/5Zlbmtm3Li9NuXdP0GbrTQozI3hin+nQAEWX7YJ693a23stmEIe+Un1l79qSGth2pBo3KScLUJu3dd1noPe+8nPvd7/IPqjzyCC+I+/Qp5KuzzmLNTdKu+a1Um7ywkCT63QX+mWfmDn1NnFg4tzc08JzRujXRnGcDT/yxT6I6F6YNcUMDK2LGjdOHS6dZGdOunTA5JBQy+t99l3eYe/Qo+Op61UHa/q495rhxXBGu2nvWrOQc1pDgpZdYU9+mDS/GFDuYsfOkzo3q3p0a+u9HgPpqOVmbnXceWwbk0fzFL3InBvRXbKbTtK0zt3PDRdMKXru7aLW1ufRdhd5llynS3G8/km4jNzaywqG2lkYfsiMrWNXV5ed1zTWsXAaITgVrUR8b/mMCiHr0EOg/+2zKJuJeh0DEmlaAcgbYcrgfGdkf7/GD+HWvxkaW8ACuYBWOOooa2u1F7bCTWrcWzq+88w4RQGvRl5ratKW57b9Kn6IXfYqe1NS2PRFAn6E7bTryhKxSfemL64kA+nDk1wlgretVV3GfbtOGd80L2v9b32JBKYLP1tXzTXR53xdz/VxLmUMPzZkyPvEE3zY2vMMH2UCXT/4s++Hexy78F5shvvMOr+S+9CXeiXUvdhbuWxZgxDvlZ9b27Wl+1y/RAQeoFQZhC0RvvcXbljU1zNPpdO77C9dfH1o2oSFJAmEcSBr9ro1h7948Zqg0eFu2sLagc2e+sWTZMnYGtuyxT6I6F6YBznxbAAAgAElEQVRA7F67889/+of9+GPWGIwdy/VodO94DPDSv3Aha4x69+Y7TKsdBe3/1FO8Mjz2WLZL3L2b7y3P2LbS4MGsiQvjRHQE2LiRTWUBNuuRKExi50mdG9WxI30y/EsEqDXdMp7dvl2igaur44ro29e3vZpPynwt1nslAvGdwW7Ti1i9WiODzZnDWyYyLFhANGsWrV3LwZ5/PreLd9JJGcHpMfEa+jRt/MlddOcP1hJANGiQ5+NXLo2iUPLee9mDgTqIn3f+9PaHCieIESP4pe4Tia++Sh/d+wQBfOtbFg0NlHY/KDZxIj129l+ymTU9M5fS7mLljjtoVeY83S9+QfT+jQ/T8DZLadIklsndCycArq+C9nfNJp55xpde2rqVsp/n82LPHqKPPirwfued/HVNY2PuO2mffsoCO8C7M83N3Cc23vWnbKHPav0YtWrFpnRzkblJY++9ecfDXWm5l7K7HSEfRrxTfmYFqA519JOfqOs7CoFoyxY2DwL4//HH81am6bVH5UTSBMJyI2n0p9O5K5r8tvBXr84pxEQ3fbo2WuyTqM6FJRA3N/NgP3y4+e75n3JjIgF8M8WBB/JW6DnnsDLmoYekY3DZ4NK/ciVby7RuzVrGQIdBKhh57f/aa7wFPWIED7oimptZizN2LDdmz578eTPddS4xIZ1mmb1tW9Y4em6lip0ndW5UTQ29dvgl1KaN+pyU8Rj7Kl81ZnRp7B135D4r54FrK5q9wSJCuB/5WbmS2w1g4aupKadEHD5coH/PHu6LF11UVH7uOT9AIU9cfDF3Ip9+nk6zwHf66Z4XmS8I0i230LrlO2kjutF/98l8qvcXv+B3b75JRLk1J8AKdlFuHT+ed9x27pS0//btOfWxH047jVc2smtZpk3jQZrv19di2LDMx1Uod6lH3o7G1VcTtWtHTR32ortxKQ0bRrT+zXXUDIf+VDuFmvv1Z7XyI49QOmPvs2m/I1TZGfFOa8SAlzEOv59S3jw7dwb+8hfgV78CfvhDoLkZ+MlPgG7dylsOi8qD4wC//S0wZQpw5JH6sP37A2+8Afz73zm/2bOB6dOBQYOA88+PsqTJxuzZwNKlwB//yHVqgsmTgf33B+rrgXXr8t1rr/H/3bs57PDhwPHHs/viF4F27eRpEgGffw588gkwYADQsWNpdO3eDaxeDRxwAFBTA3zrW8D11wN9+pSWbsWhvh746leZ8Kee4kFXRKtWwKmnAl//OvDii8AttwBXXw08+CBw113A+PHxlFsCxwEuu4z70eTJwHHHAddeC/z4x0CbNnGXzgfNzfh34zgMHw60bVtiWkceCbz1FjBsmH/Yb38bOOUUYO+9C1717s3/+/cvsTwGuOIK4JBDeLytrWW/QYOYN086CXjoIe5yWdTWAvPnA127FpXf4MH8v1UroEsXSYCf/xy46SbfjuM4wOOPA337el4MG8ZtMHEi9hncHvMeXID+IzN1fNVVwJe/nG2fBx8Enn+ey3LOOUCPHrlk7r8fWLkSaN9eknnHjtzWzz2nJ7axEXj2WWD7duD3v2cmcfH+++zX3Aw88QRw7rnapB6dOhddl/wH+LGD488+Fw88MASTv7QBeGA2cNFFwOuvAyNHoqZ7d0xZ9DxOeQbo+ZfHABB+3HAddlx5J7551nZgwAAsun8+jnjpN3hh+xdwmp4CPUwl57DcKIBOPEa/eohaQ/jSS2wyUylbsC0N1Ub/nj1sd9ymTf5NAxs3Zr6alwCtks6FoSFOp/lap8GDwzUhbW7mq9tuu43r2D0Y1L49b3dPn070ne/wtwTGjWOtiWvS5+60felLbBbz5pvBzn2tWsUKEcdJkeOk6PLL5df0VTtSqRSljjySjcN79cp9O9cP6TTfm5m5FYHOPVd++jlmbN/Omn+AbR0pATypc6MAOrTLmuxni2Uo9xjrXhP2ox+VLUsiyl0W4V5x7CJM+pcto+wOfiT43/9lVWqIA6eU/unT+TSbbtvc/fBF586s9RYPWZ59Nu8O7bMP352pw+9/n7tcHaDsd6vPPZd///nPnNaVV1LWeHjNGqIjj6T04YfTyJG5ncZ0muiowZ/QBxhMX8U/aOHCXDYzZvBFHmTIO6EwIIATACwD8AGAa3Vhh6I9PfSQvq527NhBOwwvhKxGWPqrj/7Nm4kOOYTNdB55hA90usIblXnCDMKvZCAQm7SXe2uT7CM7JcEdETPYvp3tk7/97dz2YadOfAbHNbP4/vd5jH3kEd6Vc68aBXgsP/98PuixZAnfkz53Lp+KfuQR/rLsz37GAlJtLS9yLr54By1bVl39NQh2rF1LO4YO5YoWZyPjBHYQ3XADV2jnzkR33pnIg3ePPsoXD1DC+fWI2rYE8I04KpR7jL3jDjK2vAgbv/1t4ZmFMOnfvZtlu4MOCiW5skBKf+aOanr8cXXEn/yEiXU/mHLwwWwCNWYM+193Hd/q0Lo136BxwQWZq5cEuIbdxx/PvH8nfzCFfvMbTsNxeHUBEP3hD7kDXwcdxP9vuy17f/Err7CSCeCPGLVrl/su1rZtPCQFmWMdYoYrGo7j1AB4D8BxANYCeAPA2UT0riz8QKc3Ldn6KTp1KilbC4uKw4cf8q7Up5+yqc655/LO0IgRMDQgKB1B+RUARo8eTfPnzw+UDxHTuWwZsGxpGo/dtR7bPtmBfz/XhLatGoGmJt5+c/83NAC7drHbvTv3vGsXsHMn2zhs3gxs2sTOfd68mbfo2rXjvUDRtWuH5s5dUXPECOALX2A3aJDUXmPtWuClxz7BR395FbULXsXIPa9iED5U1yOADh3ZKqB1jSRAr168jSm6fv3MbUUqBTt38pbtG2+wmcTEicWn9f77vO3+zDPAYYcBt90GjB0LdO8eLJ0tW4ANG/L7kNinGht5b7t7d2ZE97+4l0wEbN2a3882bWL/M89MNL8O79yLlmxbjwULgCOOKFNBffCHPwDnnQc8+STwla/EXZrwMWAAs/crr8RdkhLQ0JCbmH72M+YR73h17LHMXwsXAjfcwKYmLjp3BmbMYLuMI45gvtq0id/dfjtwySXAffex7dHXvw7MmsU2Pbt3s73Z2rWcxu23A5deyvGWLWObucsu4wm0Qwfg3nuxrX0v9O0LDB3Kw//KlcBHH7G52l//ykk99hhnecQRwIIFZnNsGALxUQCmE9Hxmd/XAQAR/T9Z+AGdhtDqbcu1ad59990AgMtE+5QWBEt/9dK/bBnwzjvAiSfm2biWc4INxK8AMPqww2j+L3/JNqJbt+YJnbud9rj9Hy9g07bWGNX7FOx8by2wehU6bFiFvo2rMACrMQCr0RYNpRW8a9d8AcZ97taNbfNE4UcUgDZsYPu7hkz+PXqwYDx6NI+UH30EvPoqz2QffggAoNpabD9oFDZ0PwhtaluhTRtkXWv3f01urrh76VIAwGVDh7IHERs3v/02/3fRuTMLxvvtx4Lktm3stm7NPW/fznUro7V7d07DI/TnPbdqJa8/V8Bbv17utm3j+IqFBXbtkpd3yxbc3dgIXHwxLrv//tLa2C3n3//OtpGrV7Nfz57AwQcDBx3E/w8+GBgyBPjsM2D58pz74AP+v3FjcXm3a8f13NDAC7DmZlUZE82vg7sOoY92Lce2bTkbWi/KPca+/z5wxhm81nHtieNE2PR/73ssP950UyjJRQ4l/SeeyAtbAJg0iY2aXe3lzp3MH9/+NttFq0DERtzLl/NK6PHHgUcfzb2fPBl45JF8m+rf/56l1+nTgRtvBEaNAlatYh5XjGnTpwO//jU//+AHfG5jwQJgzBg2ZV+zhoetV18FOnUqn0D8DQAnENHFmd9TAIwloitk4Q8efBjVr3hLm+aECRMAAPPmzSupbJUKS/8EAC2K/nJOsIH4FQBGOw7p9MMTMv/nCX5bOuyDnb0GAgMHosPBA9Fp2AC06tI5I022Zic+19YWCnju73btStOsNjSwcPrGG6zReOMNXpW4Ak/fvsDRRwNHHcXuiCMCnUbS9tdNmzivJUtybs0aPsTSqRMLuJ065dxee/HEI9OGb9qUE+xLRfv2LJn06sWuc2dgzx75wmL3bm4DsZxCuSc89RTQvXu4/LpzJ58Oqq/nVaT7f8OGwrCtWrGKbsgQ1iYNGcIH+2SCffv23N+2bCnU/rr/a2sLFyLu/3QaOOywRPNr5w4j6KBDF+ONN9TptsAxNg+W/gkAJPS//z7wr3+xIHrbbSxdXnklv3vvPaCuzkzNv3QpjxsjR/I4+3//xwvVHj34tF+NZ1utuZlVuyefzHy6YgXw8cfAuHGBafvlL3mBAgC/+Q3L7zCcY8O4ZUKWUZ6U7TjOVABTAWDAgAEhZGlhYVEkfPkVyOfZvTEAx+Ih1ONgbEY39O68C4cO3o2DB+7Cgf13YfO/zkO72jR2//pOtNu/H9CvH7q0bQvZgetYUFvLGodRo3J+O3eykLzPPnz0PSpThu7dgWOOYVcqiFgg9pqUuL937+YwKnTqlBOAS71aQ8R//xteWi46dOArAU46Kd9/40YWjFes4NsMhgwBBg5Uq0IrH4H5FRgVSnezaIE44ADenQFYMTB5MnD22bn3e+1lNpa5u2UAC79TfK4Vq6kBzjwz93vw4Nz1HQHx3e/ypuA//+mfrRdhCMRrAYiXqfQDsE4MQEQzAMwA2B4xhDwtLCyKgy+/Avk8O2zYaLrnL8cCYEVZr17t8uTHWW/zllq741NRlTl8dOjA9qmVBMdhzXXbtor7nVoAevRgbf7RR8ddknKhKH69/fbyFM6iinHaaWxSJpog9ejBQnGC4TjAzJms8wi67g9DIH4DwAGO4+wH4CMAkwGcE0K6FhYW4SMwv7Zrx2abFhYWZUdR/No6li8MWFQdevZkV2FwnOI2wUpmGyJqchznCgDPAKgB8CARvVNquhYWFuHD8quFReXA8quFRflQ8qG6wBk6zgYAqyLOZm8An0WcR9SwNCQHUdPxGRGdEGH6JaEMPFsN/aQaaACqgw7Lr5ZfTVANdFgazGDEs2UXiMsBx3HmE9HouMtRCiwNyUG10JFUVEP9VgMNQHXQUQ00JBnVUr/VQIelIVwoLq20sLCwsLCwsLCwaBmwArGFhYWFhYWFhUWLRrUKxDPiLkAIsDQkB9VCR1JRDfVbDTQA1UFHNdCQZFRL/VYDHZaGEFGVNsQWFhYWFhYWFhYWpqhWDbGFhYWFhYWFhYWFEapWIHYc5wzHcd5xHCftOE4iTjCawnGcExzHWeY4zgeO41wbd3mCwnGcBx3HWe84zpK4y1IsHMfp7zjOC47jLM30o+/EXaZqhuXXeFHpPGv5tbyw/BovKp1fgWTybNUKxACWADgNwItxFyQIHMepAXAXgK8AOATA2Y7jHBJvqQLjIQCJvafTEE0AvkdEQwEcCeDyCmyHSoLl13jxECqbZy2/lheWX+PFQ6hsfgUSyLNVKxAT0VIiWhZ3OYrAGAAfENEKImoAMAvAKTGXKRCI6EUAm+IuRykgoo+JaGHmeRuApQD2jbdU1QvLr/Gi0nnW8mt5Yfk1XlQ6vwLJ5NmqFYgrGPsCWCP8Xgs7sMcKx3EGARgJ4L/xlsQigbD8mjBYfrXQwPJrApEUnm0dZ+alwnGcuQD6SF79iIieKHd5QoIj8bNXgcQEx3H2AvBXAFcR0da4y1PJsPxqETUsv4YHy68W5UCSeLaiBWIimhR3GSLAWgD9hd/9AKyLqSwtGo7jtAEz6h+J6PG4y1PpsPxqESUsv4YLy68WUSNpPGtNJpKHNwAc4DjOfo7j1AKYDGB2zGVqcXAcxwHwAIClRPTLuMtjkVhYfk0ALL9aGMLya0KQRJ6N48McdnvCAC8u2oHN29JxF6Ms6NapFcaP7Bh3MeKEbBsvSbA86wPLry0KieNXx3GmApgKAPfdd9+oqVOnxlyi5MPybIuCEc8m0mRizRq2ee/fv79PyOrEmjVr8N7y7TjkwIFxF6Us2PB5c97vlt7+lYaW3l6WX1t2+ycBRDQDuU/g+i5gW3qbWZ5t2e2vgq/JhOM4NY7jnOw4zpWO41ztutBKMH16we8pU6ZgypQp6jClpC/6694Vm66LCROKjjtlyhTceUv1r/D733ML+t9zS4F/XvvL6ipIf3DDSvpZZChnXhJEyrMSWgr4NUkoQ92b8Kusn1cCDr3oeN8wiW7/CkC5+RUAphxzjFmb+fFPmcc2Y5Qwx7q8Wmk8a1TeTL1EwrNinauey42AefuaTDiO8ySA3QDeBpDdXyCiHwcvHUf1ZACIZXAcTEilAADz5s2ThwkCVVwno0FXvfPLzy+M7r1P3AkTJmDL9mZM/+WT+jJUOE5JdQYAvLhwe952zoTMYmLevHnyugrSH9ywkn5WdJ8yzdM8r1C3YCPlWQktee2VNETZzhmY8Ospqc544t/qA9QHzbwVyy68PnDeqngy/4Nm3goAvvmIcWXlztt+nT4dEx56CBg0iNvfbwE7YQLghkuqMOWPiuZXAJjgOEAq5c+zpcxzcaKEOdbt8348mzToypvl2Uy9RDJmi3Wuei43cnmb8SwRaR2At/zCBHT5qKsr+J1KpSiVSqnDqPxkUIWrq9O/05TPKP9USp2OLq6M/mqFog1SAwfm6JfVQ4C6zGsvv3imfcoPqj6jKle4/BUtz8raK8n9Naw21cCIfr9yoHBoNIIqnsyfp4ZgafrRBVAKyNHv5iE6WdrF0psMJJ9f3f6maL/sGOs3B/qNk6bhikWxafjQpeVZd3w2netNw0SNAOXMoz+oLBVUbopqXg0Wx4wXfQMAPwPwZdMEDZwvYdnG0oUD9EJtMejSJffspqsa0EWoOooYVnz2oSuQgBHnoBMRUgClBg7kH34Tp+69X9vJ2stUwFa9N/ETBQV+nk7hTrDh86wGiRaIy4CS6Q8y+fottnTh6uoKBSRZvkHGg7q6/AWsmI/rROFMXOhGMWmWB2ELxOHzq8/CI5VK8Rirmpd0Y6FqsWXi583Hz99v/A/abzLp+fKsau42Ce9FAvt2Hv1iW8sWA15ZyNtnQhhDjPzcfIMoxITYJs6EWU8FsAPALgBbAWwDsNU0g0waUwHMBzD/vvvu8+1g2cby62SmGg9TyJhAJ+R6/XQCmHewUSGohjgM+hOmrUkNHEgpVf17EURA9WtLt0+p4NffTCcF7yDCYcKcYEvmWY/TokUKxEJfKpp+tx8EmXxL1SRHoLGV7uh5tcTueC5Ort7flYPQeJWi4lefek2lUjzGquYl3VioUwr5+WnKZJyuSVoqmGiIvfma5OGnvEsYCgTifAVN4W/3maiwbmRjSBCag8ybQfLJf2fGi74BgBUADkPG3jgE57tCnD17Ns2ePTs/nGrrPKj6Xoe2bfPzM2VylXYniPZGQJZ+HYJs5/ghYSvY2bNn0+zJk6PPKOg2n7e/mbS3Sd2GryEOn2c1MOqv1QZhsC2afq9QSFTaAtAknqrPljAGFNAv0iX+F5Uc3sVAwsYgH4TGqxQ1vypMD6VjrEw76I0bxq6sN33V/yBpBUQgni2V5gT27TwZy9vm3l0d8dkLUw2x7resfnV9IWYN8TMAWpkmaODU0G1pB11lBV2tyDQ1pmYOJmF0HaKYFWQCV51VDxMNc5D4QkphurLybEtFWAvRck+WUeUnTqwy04iBA/UTX9Tl80OwfCuHX4vZ+g+yC1oKTHZWk4IklikM+Glni9X6+uVj2ubh1bsZL/oGAB4C8CKA6wBc7TrTDCROjcygVF9fT/X19fmVEXRLMqiGWKapCSq0BtlO1wjbWfp1SOCqMywY0R8H/ATg4re7w55gy8ezlOD2KhOy9FcCT0YwsdfX11O910RC1Ax7/cJWDoSBYPlWBr9qdrSUPOsuaGT+njRKRtCd1RDhO2b5aTArGXV1+WOWlzbVhQal1IPfLoDpbnrxCE0grpM50wwkzhcFh+rC2qLRpRXGrRbFaog9fsY2iVEMUl7EYBua6tKl8FBllDSatlup29nl0xCXzLMFdv8atEgbYgFGZx6Sggj4KHtAS9QSy4Ri8VlE0J24KBCvhjiaOVbTH7U8Wwn9uESEfqiukgDPwf0gGvpS6yK+ugxHII7A+aKgs4bZCCaq+Zi1FMYCRhTbWKo8yohUhmHztPZx0Vge+uPgw9B4NrECcZmEK6NbcYjUi2CZfzHXuMkW3UEX9yqtoqbseVd4iSYT4m/31hiZBrJYBUR8iJsfzfhVc62YlGdNFSxB3wfVDpZBY2x0qK4cCqc4kKE9S38AZZ2xJtckXHnr1Yh31C+AOzP//wFgtteZZiBxvpCeWi4FJo0QX0MV5Gs1xIKGuByHboJo9qNBKBNhXDwbWCAu10RTpsVc4AWs18/0lLVJWqWm7Y2jW3RnwqaA3BVeOpMJlUZKVY7kItn86qdEcDWE7iJF8PedB9021S2avBp/bx/yxveO8aXYFBsKbkY8G/SMSLFhy40MvxrLWGIbatIs+O1XB7o0wxe8zXhS+QIYlfmfkjnTDCTOF7FrnEq1kykxTOz0x4y8wbpl1ENYE2wsPBu4v5ZjZ4Oo/BpiP1SThljQ/mavSXTNJtzdHa8w7LZ3sXWVHCSbX/2UCK6GWCbw+e2UigsdbzhZvqJgJP6Xmcmoyh2kL/iNLUF2Yb35Bhmvkrygq6vL/5gOkb68JsKtrM2KP1Pj236BwvFzaQJxNgDwHRO/AE4P1XZOKQg6uBbbmUPSfEhX7y0IeYN1OQeWMBZCxaURygTrunLzbGI1xGVCi1zACvyZHa+8k5A7kbrCcBANcbKRbH412PFSzjF+WkPx9hBvuCCLL92iqxRNrLtQ8zHbMDKZUJUvSJyEouBLhao6k5mOmLZzUCFalrcX3jYzMfvgMEa84xAznxKO4ywkoiM8fouIaKQ2ohp+GWLunDkAgEmTJhWZRWGa8KEzD9OnswsKk3gGYebOnQscdxwmBSlzFWHu3LnAI49g0iOPFN8WxaDYb66X/t12s++sGxenvDw7d+5cACHya4UhMP1h9GlVGrq0w3zn+k2YgLkDBgDnnYdJP/0pMGECv583D/jwQ+CCC/Rplou3w0Xl8mtmfFL2Wb/xy31f7FipS9O0DLq4hn6+PFvMmB5mnUSMLP3HHaem08l0c+970zovFSb5mPRHfmfGsypJGcDZYNumzci3bZoHYK6pxC1xekSxskr4ak2KSilz0HKahi9mxVkqVKtSv/yL1QzkwoalaYqHZy2CoRgNqcxOU3aQT7YV7T7L8pVtV3s1Q6Y2x16bYdF5w/qlm2xULr/6jWGmWr0wx+Ji7EJL8TMpezFnmCplzhbhrRe/37L/3mfZ76BlIZKbVqkOguoO6vK4NJ1MeFL5AhgIYAKAV5Fv23QEgNYmiSucLxYtWkSLFi0yCWqGUrZgvP5BmNUkjMRv0bRp4dIfJYJOagbhFy1aRItU9mtRoxi7Jh10/SKXXlgTbCw8Gzq/VhgC01/MZCE7mOQVMMX3smdZvrIDTd60fRaJiwCmX4znFY7ddFQCe2WhovmVSNJnwxxbo2pXkzKa5J3pr4umTQuWV1h0JaDfK8essOykg7aVbMGukwF0cdVp+fOkbwCgIzJf0QFwIICTAbQxSVzhfBG6TV4YjWxorB84bYlfymvwnmREoCFW2reVYyAxE2DNoYsTsobYdeXm2RZpQysgMvr9tDPi1WZiOK9doM4+0GtHKKapsiH0lCPVpUv+PcTi4TpZHjL6ZAhD6xQNKppfiSK4yUlEVIoLQ2HXJB3poUK/vMKiKwE7I8oxKwzloWk6fgt2nQJRF1eWVqka4mwAYAGADgD2BbAGwN8A/NEkcYXzRWgTTDFaXdWAXayG2G9bQRKv4EqUFoa89k/OJBjONpAcYU+wZeXZsgiECUZJ9KsEVqLcoK967/r7aYm972WmDaKfNy2ZMkDwS2VcgZZZLLf4XjapyWCiMYoHFc2vRCHwbKnCUBRQ8Yk3DBkeXDecrwMjAeOaco5VLZ69CLsegiyU3TAqmUwe34wXfQMACzP/vw3gh5nnRaYZSJwv8gTCUipeNYCqBuTCVYV/Z9HFdycZVRxF+Vr0LROuximsE+lBtVAmcfzCm8aPTkNcVp6NTCBOjgCkRUn0q0wfiAoFTNO4/tqSfGHaKxTLwrrPon/mf1YgFgVnr4AtE6ythji+ObZUnk0ibwbYpTWiPy6zvTIgj36/BXS568E07WBlMONF3wDAIgBHAXgNwKEZv7dNM5A4X+SZDJRS8X6CqGyi8Mb36yy6+KpJRhU+A9/tnGqGO8GGpSEOqoUyieMXPjhDhz3BlpVni7p2TbUt730v+iUUxvSrzAZMFto6DbHpF7e85ZDlr6t32XMqlW8yQZRvMiGaTYjP5UBUmq0K51eikDTEpWoSw+LpIJpCUUMcxl3fFYrUwIE5pZtIk+ygrm7skfmbQhhDCsYfUy2xKt/C+KEJxOMzJ1+vyfweDOA3phlInKrAWaTati0UiFSdvhjIGl2VtulkIfut+riEKr1s8QJoiKuEQbMYOFDe/l4E0cIGHZSDThTeNEzjR6chjoZnFQhkj+bVSsq0nKZaiiCIkE+MhQvdOYQg2lDdQl5WDl1+snwA/alt0U+2gFW1r7etTSe+YqFa7JaeZ0XzK1FIuzqlahLDUvoEVDgRtfBzD3V1hWZOLoK0SanKJN3uUfhKKTNeNA1IzKh9goRXOF+CXwbo5ZdflhMYBhP5aXpl4YP4e9/rGkuSxssvv1xIf9CyVSqA/PYvtu6LDVtM+NLjhzrBii5UnlVA2V91Aom4ba/SEIuIeqegBBjzq58Qqvute+c1kVDlK0tDFd5U2Kmro5cvuiifX10nCsfeNhfDRgXdgqw0VDS/EgWcY1RImoY4QPqh0F+pAJhnL7pIP5b4ISoNcdB0ZOGj0hDnBc7YOpXocgVWaed02qZSGkGmllelqzuBKzakrhy6r8550FYAACAASURBVKj45R+UplKRBE1zXV1uu1X0E/+L/mHXkU5zH2RHIUj/5d9RTrDh8WxQ6Oqs1IE3jLKUCyZ9x29BYPKOKJ9/xHDeHSfVgRpVeNlOmWz8SqU4rtdEQsYX4lhfzvZJmIZYdLHyayUj7vmrkuZhkeei3qUREdeCiRGJQFyKoX8hsyq0DoFXbzKbYNkEI9N6mGhxvempbIPF336NKWpOPHFffvllejkMzUmQzpYETXOmTrLtL9N8iRNwqdufujoodjtQpfXS9TPDK2GKcaHzrASB+TWIUByORq8w7xDhS79sfNKNFX7aYR0N3jHNO75Ixhupn2z8U2xxvnzRRbnxyqsRFv38xt9yCzWm/a8QkfAqlYlfiapQQxpwjAid/qjNP8KEO8eGuetumK+Rn8m7InI3cUGZ9bIg4RUuB9lgAxR3SIdcckj+LBuQvdt3Irz5eycQ2QDuYwohzVsyWYZ2y0Sx265xIVMn2faX1ad3Ai2FaXR1UIqGOIhmNEdDVBNsuDwrQWB+DTIQhy0oqfIsIQ9f+nVCqKxMfgtZXb15tbZuWHc8cX+L5ZW1h2xsE7XAwriVAijVpUsunOtkfmLaqjTDgEl7mvY/iW9ULhJ+lYw9VWdDG5B/Q6e/kjTEdXXqQ3UR52vkZ/IuOMz4T/kC6K5zphlInBpegcgUMmFDpiH224qUPXvTMA0TRIDyILR7iMvX2cxgmGe2/cU2c7dXvR8j8Jq2eOvfrw6SsBAISUMcC89SkbdMiP9NwoYFVXolCGOBb5kw9Q+ajklYnXBuOl54FikpIHcrjsufXgFYp62WpFkyTNozZg1x2fhVsuBJ7C0LZRqPq25BEBApSG5y8pNvRIQg5xgjiByjzrtkgXglgBWZ/ys9v1eYZiBxaqIyDFvQWf0q009jEjSsLIyJZsaNq5t0DRo3r7NWEww1YXkLAlG7JNP0y3YC3Gdvmyl2JKQwHRB0YWVhop1gy8ezAqpicilmwHb5NUz6/RZppuU06b/F3qYipp3Zzcrb0XIXrq4tsesnpmMykZUC0/SLy7ey+LVYDXHQLe4wUKbt+6oYs0pAwRwr/vc+y6Db5Qu73wTZ6VbnXZpAHKFTE6WaYEzMD1QIsh0p01yI70zj6oQsg8at2i/VGS5OClavopbYO5nKTFnEZ1VYnzIYDwimaejNBOLgw9J4VkDRk4vJTkySoVrAFwuvZlWTpy/8xj0vr/iVy5um4Je3o+OGE5133HPNI4LsFBQLnaLCbzyWpZNJLeHOl5a8PhuED4Pugoax+1HKTpJK6VIODXmp/ToqvhDb39uesmdd2cLUEKvCqNrJdGEdhoY4LxDQDcCYzH2J4wGMN81A4nwJK+isxWzHyn4H0foFeeeXj2kZotA4RYEwBjmNv/RLhSoG9KtrVd3L0gsy2Jtof001xBEcqoucZwUU3V9VC5QyaYlKRtj86gptQRb4puFkv01tuVUmDkSsIfba/ItCsGpXRxRGo2xvnaIiiJlGftzQhdhy8mveIqaQNrWf951JvDDatnhbb6V/WTTkpdIeFV+I7a9rr3KPw0GVU8HKZ8aHvgGAiwG8DWAzgBcA7ALwvGkGEudL3KJFi2jRokX8w3QVL9NieN+bakTCRpCBt64un36D8LHQIytHkPAaLJo2LUe/d/D1M3FQaaH9tNN+2jk/zb+ubH7lDLClY+LKxrMZBOqvImSHOsLoz2XmByP6VdqMoBo32S6JN6zsOjNZHdfVye8ANS17Bln63XDiB4nEr9bJaIhq/JKlX4r2Mj9saLxKUfCrpm/U1VHeHPNCqq6wb2T+K6snSH+VhAmsLCxSu+jSKsO0aeY8+0JKqAtNWQpeJXAcE9v//lHTsnxZV0f0QoppzULg2QDii/Z3oMgB/X3yCk0gfhtAOwCLM78PBvBn0wwkLhjRpsKkn7ArplPulY+pUE8UvGxxaNTkWs5g4XWQbc36TdoqgVnlLxOQdZOzrh8GWWjJyhOyhjgSng1rcJZpGcPuv0nUMMv4VKY9FSEbN3QLN69m1vWThZOlr+vHfotgl3e8eev4Msi4WMwYEp0GOjRepSj4VdPmBa80GsIIFZShhCklDeP0M/2oXGWOGnllFHiEJUEUvpfFM00/QLww4JOXGS/6BgDeyPxfDKCt+2yagcTJIQx4U6bMoSlT5uT8hZWKcpdDpQnxPIurRtMFSSiygGo1I8lszpw5NGfOHPN0y6wRkyLESpszZQrNmTJFbzuk8Mtb4RJl0xD9pUXT9IkCLYppXFmbezRXmSBhT7Dh86xmtAnUX8V0ovooQ5n5wYh+lZY16CJMt3BzBUw/ra+oGJAdXjbdAcqEmzNnDs0RhXtREFVty+sEexmCzqxiHYXfH5LNrzLtbsbP9Xf7rHe8dH/rqs9gKNTC73tWsjDFQFeWKVP0PJul36sh9gtfBMo5XLl5TZkyh77fa0q2X6RSVECnbM5UDWOyZ288VVl0/kFYWFcOClEg/huArgCmA3gRwBMAnjTNQOLkyFuNpAhIKd7pqsQTAIW7RyYmMuVY5WTTlCSeRBviwExbQqWlUim+xknSfqq0lXO5RDMSWHNgSIssb5lCVBQY3OKF6SLhWU0HCNRfk7B4CxmR8atKgJa988bxk2J0M5tpG2VoTrn3ptfV5W6WcGdZ70zqpaGc/cZPkjMvT+L4FcBUAPMBzL/vvvu0GwNE6j4r38BSp+NCt5aS5SEdF33yCBMDB+p5NujGaynljUOzLMpYLq1e9le1i3cDV7d5KsbTyWIq/2I3eSThzfjINCAx06UAnAygNkg8j5NDqK2BA1M0cGAq985EQyxJh+rqpIoJ2bMqjC5cKcimKUk8iQJxYAUN6orOKzvBplLyfGXLPyheSbS3QTUcBVpn07ie/lfQ5hFpiEUXKc9mYNxfoxKGYxayS+JX07K7M48LPy2unxQThhSSCV9wQEumLRY1wn75mDBoMW2uyldWNr3mfjolnF/9NHKqPiurXpP5UDn+SuDtykG0h2EB0POst4x+KKW8cQxfQE7Gcru6V/hUtYt3mDGpK1mYsDXEPmmb8Z9RIKAGQF8AA1xnmoHE+SJMgbDcnS2osCVDEgXiEDqkcTqpVD6zmsQtVzuHsHUjCxvFxFo2njXur3EaJXoRYocpiV+DmAz4dSyZOYoqvCx+kfcS590K4x7o85puiKYeQehQvSumzXUqKa+GWCe4s3/F8iuRvs/q1gJiGN1vP78g68Ao4KchFvMudt4ppuxRz2Nu+gVKRwreNkTBrN5i1luEIxAD+DaAzwC8kzH+fxvAW6YZZNLI287xQxIEwmIbT7fy1Y3hJqv3aoDJKjGVSmW3c2RKr2I1Cn6DuEkapluCpojIZKJknvU4LSpSQxyicF42DXGQMGEKjD5IQbg3XKYd9pbNL8+oNMRBVull1BCXm1+J9H3WRIlf7DgYwtZ3KAjCs4FN7AzehRmnmPTFOTaM9CoAoQnEHwDoYZqggfNFEgTCYhs6yK6l6l0S6I8KJosEUUMsm1OLHWiL2UUuJp8gc3UUGuJy82yi+msMqqdQ6Q+rXMUIjkXmnerSJUe/q2UVta268pioI5OHiuZXosI+61Xa6xT5Jkp+lb9KCRHUX/dsUiZTnvXrnpWqIU6lUtSlS8o4X7Ee8pV34ZSn2PcB4oUmEL8AoLVpggbOt/D19fVUX18ftA5CRdB5JAjzq965fkHoT8pcElbHravLp19Wt2FoiE3T9csnpPoPe4ItmWeD7OpI+2s5O2apmtESEep4FXb5y1Af9QDVX355fp51dfl560wdKkjNlEHi+NXjfOHts37NEvSQnRd+GwSmafqVSaU08frX19fT5ZereVanfAkL5Z67RYG2vr6egNwc61U8yRRXsrbSmeWblMVvE0m1K+z3W2KhZcaLyhfA1Rn3AID/ALhO8LvaNAOJUxKdBBQjFJnuDnrjeNMMmoYsnbgQVjlkppJh0mgy0AVZ9QdNR7VLGYYrJ8/6opwdsxjGSSrCLn856kM2eKlsnoOaSCQTVcevfkqGYrSxIvwEJ9M0Vazu7V6qcdp07RzkkGCxKPfcrTo052VVmcmMGMakDk1NbvzSUg0bfr/dIUigxYwnlS+AOp0zzUDilES7mD17Ns2ePVsWNHIUs23uXVEVu33vpjF79mwCzOhPylwSVjm4I+fa38uspcJkoAtiFxY0HUXaYU2wZeNZEVJ+jUtDHANCGa9C3F+tq+M/0tcKySYvbMCyzJ49m2Yfemj298yBdUQDBxYs/rJfwnLLZlJGz8+8G1+ianf/dCuaX4nKP8cG3XFV+eve6cLIZIzJk9X06/LSlTFIuDg1xG77i3S6/70fw9TVqezKc1masjLI0jNpX126snKEpSG+1TSRgE5JrAvXvqeYlWip8GMEsRymjef3zpunaEMbNfxo8AsXBcQTwCb56r7fUdi3CsOo0stvE/0gIXuW5SUOIC4Amk7hTLBl41kRibIhjgGh0B9AXZTtP6rVVWZoly7+FPudeWEDqq5SmXvD3f7tqmVyB3hy6eaVzaCMrqZHfKeiPzThw5/+sATiWPiVKNk8W4zW0WQbX4QJ/UEUI6WGKzdc+nVWTTorJ9dfZkpRinlNqe81Ycx4UvkCWGiaSEBXIDB4CXAFItkunPhbh6ACnErDqxJ6vOFkaZlqCL1+5RSI/WytVOG8CFNDLNLvt5IU+4WsfUy3WsQ+qWJs0S7Jm7ZuYDH0D2OCjYxndUjy5FoOhEJ/AAbK9h9DDXFef/PREAPBykKUuzfc5YmZA+voBaTyxneAtbsAUR24bHWok/OEkL93h8hNo4AWbxoeBBJMyqchjoVfifzvIQ4TQRcqplpe2TsThRaRGc/qXoehIQ4TQfNxZSzVuVexHr1KHFk4r1OVq1S5zYRORRgznlS+AN4E0A1Ad5kzzUDiCgbBQkGmUEMss3PRIejKTCUEF7Pa0W3Lm/iFdSWKCVRCftCOHNZKGMinX7WAEPuQStCUDY6q32IaMkHCFZS9grEsXdUOgco/RA1xZDyrgxWIi6NfNoGYxgsC2Y6HybN5+rnx2p083Q/X1dURdemSP7mK5VHxhGnZRejSC1kwCUsgjpxfVfWg2oVVmaiZ+KnaSDcf6uZdUwHYJD2vX6kaYlOUSyAOLu+w0kkWz6t81Cl/vOFl9V8MLwYZkwz7qxlPKl8AewCsALBS4laYZiBxeYOjDO7qpZQBrthJQ5WOiXBVbN6FZdFriFMpnnDCyk9EsWmFVQaeVM00xO4E7DVnUIX3+sl+i/3Au3oWJ3zTycCkj4QoEEfGszpYgdj8kn8R3i1HXVjXvxg+U+14eBd+QdLO58F8EyeRLi993snWZKHtjeONKxO+iqEpAMISiCPnV5USR7ZlTqSuaxO/oDtmbn6yOKpy+KXppzTz0q9DGAJxWIoiPwTt5ywMF7Y/Uf7Y4Katm9NEHtfxotdfB50Argur8TPjSeULYJFpIgGdL0RmLVeHClvjXAr8mNVvxVbpMBWwVJOuKpzXzyR977MJk5u0jSRMGBNsLDxrBeLiJlfZToM3rHeCMem3JjsTosDhLq7FidBPeyvynWjzLxOGRTrFZ6/Qo7K5F9PwlkFWbjGtIMJ3gHCh8FWU/KpS5Lj14vZZ1XkJkzoxUQp4+5G3nXSKBF3/E/uQqlyq8hCZfanOb0gLqulMElylk1iPIu8Yal2l7aDiRVk6qvqRKaNUcVTt7fE340nli5gm17o6oquuWk1XXbW6aI2ImJZpGN2KiEjeqCbaP52/iuFWr15Nq1evlr+kaDXESYDb/i5UA7SMmWUw0RCr4nmfVYO032SgSjsjFEynhE+wOvj112qHCf2mE4zX3yvQmfRb0wWyTENsYookloXD5egHCs2LvIKxmL5OoycTpmV0+r1Tpa+CQbhQ+CpKfvVboLt9VrV7EBZU7aLrW6blCeJfWA5/nk2SkixsiGOWV6mkWniHsXNgEt8vjaBxgiicdMx6gcSvj2nCGlcAmRYk6KAuQnVYTwZVg/o1qq6MxTSyRQ4qDbhsclMJn0lYJATof2FNsGXj2TCRhLZKCvwWbiYLLK/Wz69+xfCiSZDJ4s+rTXJNmNxFu2jS5LUdlikIZGmJccQyyGjWpW/azwzCJZ5fTbWtMi2eyWJf9d7vnZ9iwSRPk7ilKLRU+QZ9Xwnw8rzoJwur85PNxTo+1M3ZOq2yn3mrLB9TpVNQBg7jVGwBvFqQ00+fRaefPktOtSau1y+oFlD0M5mYotIQz5o1i2bNMqO/GuFtf50WQ7WqTcJiI0D/C2WClbmoeFZEkP4qq5MktFUpMKVfN4Z4tbWyeDJNqAudZldmbiArj6kywstns2bNoqFDZwka40Inu71F1xfEtES6xXryapvFeCq6Q1o0Vxy/etvU22dNtXimWsIoUYyiS0Y/0HLnWLf9g2iD/aCbn2XhitECl1g2f/4zCZQNHM4WTwEKBUVzm0SdhjgMmK6KxN+qlY+fFjO3WjejvxpWqDKI9IvmITn7t0LNhkxL5H32O3wXI6KcYCPhWRFB+FUn8FUqgti8e38HERJVgq0YTwzjJ0h7/VSaVVVebhqpzK0wKmFYFG69ZfWjw43rjeMVkmV1JKM7pEVzRfCrag4iKuyzunmuFA2x33tT5ZHJQiaIVlk8uB20bNUA0YZc10+8/jqYpGOah59W2bQfCuPGdDLhP5NA2cDAZUHCK5xv5RZzSCdo59U1nnfA98bTDareiUAMI9Niio3rvit2gq0WiLeMqDReLnRtoap/Vb0VMwCGNGhGOcGGwrM6FLuArZYJJ5VKUZcuKd9wJhpiXTy/MLoFt05xYDr5edNz3cCBTL9oLiGaTLiLVZ2iQJe+SqGgMpNQhVflUQQqgl9lCyjXz0Qg9u7cis9BhNJitH2yedVkMagrjxjG9CBsqWNUFHNKGGm6CwLZAlLXF4pZ3BRTPlX+JppnhZ8Z/ylfKO5GDPMeYhWKEYiDCoc69b6JwKt6Lw7K3k4kmxBkmpOWriF276H2CrC5wSwXVjfpF9arfjAvdZuoBIQ1mUbGszoE5Vcvf4j+lQhXQxoUphoVMYxKC+IdR7z1qxNUVAtzFT+5zzntb45fxbLIFqB+W6heZYRKUyybvGXjuJ/QWyT/VgS/qnjM1eqLPOsnYMjaXloxkjjF1L9sIeOXp4mQFEQg1uVpiijmlDDSdMcskUaRf70oZXFTTPlU+evGSR8/M55UvgDSAFZn7kn03pUYyj3EKphciaIh3Oi9bIKQaUxkDSHTUhTRQNL0iIqjXwUTISOKVWwp8N5D7W0fWRl09WqqCYqxHsKaYCPjWR2CCsTeHRTRvxJhqiH2QrcoV4VXTfpegVYlLMryUi3MdQsWMY74ZdF8QVlfDq+fTIj3pqObvP3okiFODXE55ljVHOSnIdaNn7Lx1G+eVCHINOenITadbznf8iidophTSk3TbX/xrn9ve8viqvqB7L2pPGTyLqT0ShaIfw3+ks7dAI4B4Jgm6uN84WocwoTJZFvK6sdU26XS2oi/w6S/VLrDjKND/iJDTr/fJK97Z6KtiBqavMOaYGPh2aAmE6rJMs62KQXF7GgR6RfeqvDFLrL98vKLI0POLCKVEYo5jvuVOvGGCXFRK5tYxTRVZZNNuLIJ2Y+uEJB4fi11F9ZPq68LbxonSLiwUSzPVgNMdwi871S75aYL+7A1yAHlNTOe1L4EHADHApgBYDGA2wHsZ5q4wvniBz/YQBs2bDAJaoxSNYRBVzAmW0GqQSRM+hOuGc1CrIsNG+T0e5nPW5eqd17tWVzQ5B3KBEsx8ayqvbxwNXY6DYT3kIfrX4oAHbSvBg1vSn8xZVAJjn5xSqHZL77ISwDTD2zIvhMnSvd2Ce+zjo/F/2IYvzjiTRbFwrDeEs+vfnT49VnT/iAudLzhg2hsTRZjpczP3jBRyBimeev8wkxfF1ak3x13VWnqFtCyQ3mqBW+x2uOgcRR9r3SBOBsI6ArgUgAbAFximrjCSQkyqRDTgT5o5anC6CYYv/RMwnu1IUEmsrAF0qAMFXY5/AY7Vfvr3qnunoyijv00WrLyCAhtgnVdVDzrpdUPXsFFFFrc+hGFINkWt85UwARBhaRyLppU/VEmGJruUokmBqaLC9WOijeuGFZc4HiFZJkALPqLvCDmJd4k4y2fbGHr9StmIeHNxyRY2C5KfiUKJnDI6lC1kNX10yA8a6JhLnYHV9VvTRDW/Ba2xrTUtHRmRd76UsUX61G2QDX97ZeXCJP2UKRlxofKF0BHAOcAeALAKwC+D6C/acIalyVMxkR1dUSnnDKTZs6cmVcBqsnStEJMGNE7oHonGFk87wSjmkhkA4p34HDDzZyZo1+FsCdtmUZG1fmCbqfpIMvDS7+u3f3eyX5HIUjJBApv2aLWEEfNsyo6dP3VO9CKdSIKbu77StQQm/CrCn7CZ1ClgZumbKIzyc991o2hhX09f7yWCcNieVQTsCoPL22y30RqIbtYPtYFC8OVi19ldMnGWO+zjHe9aer6qV8/0/n7LdpM0xXjieUSZQwdwppng9ITRvo6uPR7BVuxXH5KKrH9dfxm0ram5TepM0VaZjypfAHsyGzhXAPgdACnic40A4nLEqZiItGG1it4mlRckApXTQReZtcJs6rBXQwrm4i8nckNY3oCNkzI6FN1vjA1xLI8ZAc+dO2neqc6hBeXhliTXlgTbKQ8q6JD11+9Ybt0yd8ZCVLHfiZ/4ufMdWUodgxRoRR7xCB1EDScmLZuLDPNS8Y/bEeco19c1LjPok2x6O8tk1g22U0yQXgqKL26sBK/RPOrCQ26MVYl5BamUZiPGE91q48uH9lvVX6mbSzLz+TgetDxScwj7LBhw23/YscKHR9GSVcJaZcsED8EYKbCPWiagcTlESZjBr7TMpX9XWoFB5kQdbdGiP66juQNKx4u0YXLTRbxG/yHUe+m+Xgh3jIhs02LugwxDFRhTbCR8qwKQfqr6S6PKm4x70227ILmJcKP/rCENhONmyodd4FvKPAZw7uA92qcvFpiMZ4sjPje+yxTVpiUj6j4+lX4J5pfTfqurM/q4ul2DFT+KkWQrD2DpKvKxw/5fcrsHuKgCFPrGyVM2l9Xr5VCpwAz3jENmBcJOL2YeCSZXOWMZn4C0g/i4GySnsmkq5qEVNt5ssFeNwlGce1c0PBhL0KCxPPeQ5zzNxMKgqIYgYlIfRDBRNPiaf9QJlidC5NnC+vBvL+62sJiUKkaYlPBoZgyBREmVPmUOr6K9Hv5VMxXtQXvPnsFZu+zyE+mZfaOxSZhDfwTza8mfVfWZ3V8YrqQ8o5/YWqI/RRJfhDDmGqIgyJOrW8QeDXERHKtbwCeULZnQmDGd6YB8yIBq4uJR5LJVach1IUxhU6AlcFEUFRNQirBWNbR9Kuv4AJx0EmtWG1bWOnr4sk0xLpFRKllLVZDrBIudBoQhYBQjgk2NJ71IpW5dsuPr0Rh2G3b3K6IXy7Rwy1j0LKUS0MsxlMJFSaLsGLGQx0Nbvu78F635t0hk6Wt2wmSPYchvJWAiuZXIrNFbNBFh0kcv3ZShS0Wqv4S1S5sQoXBPIiLWJksE3Q8EuOK/8NCSHVqxnemAfMiAWuKiUcKZtV1VpPGKVXAFZ9VE4ksjmxCU03uslWzbJIgKl5DHKQegoYPimLj8+Sp1174rWSjGJRkaeruTBX9dcJJBuWYYEPl2fx6SBFQOLh6J8qcjTwVLAyKGUTDFnyKLYtM2xIldNvQ4m+3j+nC+pVZNcmJi3/+BGyKiHhB4a1HmXZYRo9XoeCFjBZvOcVnP6FaRo8hKppficI9p1JMf/I++4UtFqr+G5VAXAmmBDwGywViP2WdDn6L52IRUp2a8Z1pwLxIJa5eZQMiUU5A3LFjB+3YsSP7TjdAivFNoVrRykwbxMFaVW7xWVZet5N5O5uKNpH+INDVQ9hbpVEBILr++hz9qnrXTfJBtBR+/rI+JitPkDQlKMcEG5nGaceOHXT99Tt8+cPrStUQR7H9X4yG2OXXcvGTya6XONbowpoKJaq25Xy4/cWy6cY52fgoCrsqntHvquWn5/XzGx8Ctl1F8ytR8XOMDEEEoKRoiE3pD1qGStAQp1I5+mVKP91ujh/Cor9UBZckjhnfKV8AbwN4S+LeBrDHNAOJ02obVIOlDH4DqAoq4cargVAJsqq4btoqgckrWJtov4MgqMY3iczrbQPTPhGEgUwmVa+fTogw1VzpihSGi5Jni4G3TdyBVrUzUkr6ol+5eKqUsH7QneDXLb50Oyeivy6MOD7pyiG2p+vXpUvOT/Q3MYmQTc5ef2/fkY2lsnRM8/D6yQDQdKpCfrUoRBKVRqVCpkwKa8EYVn2Vmo4kfskC8UCJGwTgiwCeNM1A4pQDjXtFz1133UV33XWXklidat5PKPSbML1puwd1/GzgTMqkGsxFpFL+9JcLUQnMfumeeCLT751YVXGDltP08JXMT9V//K4gUr1zD+eH4aLkWR2S0l9dhDmJmaR111130Yknlka/SgPrfW9ieqBLV5a2d9Hnt4PlzQ/g9pftAujiipphmVJE5ufVeut26WS8KionZGXy5qsqO1UIv6rGH5FndQusIP5+71Tvix3Tg8Tz+rtzjF/6SVQalYq6unz6vQtN1eLQZI4LS0lRTD9Svc88lyYQ5wUCRoA/KfkhgBcAXGGagcRpK5C3Uos7tW3yLshKRxw4VZpf03TdtGQDuNhhTOgPimKTimp17JeuaN9UyupVxZy6/P2YO8iWrR/TCrSVPLl6XZQ860UxNu9RopiJPGhaIlwb6lJgKqSK5fH2OZ0AqJvsVAdX/Rb6rnmJewi4ri7fhlgVVxSEvTwuCsjif9G8xvtOjCfSIRuXXft1nUbYwHRjOiWcX4WySiHOMUF3t4qdf1XvTfxkbRUkXiF/mR0qDFsgToKAwJUPKAAAIABJREFUzXVXeJOXjJdE6BaZYtqFeRXWZSmLGW+efggyx+oY9EAANwFYCuA/AL4NYJVpwp60pgKYD2D+fffdpxVwVIeqRBS7Qg2yUhEHYvG3LC8TxpF1JpUGJGyBuFjBNi4NsU4gDsJIKqbWVa2f8G3CtGJb6hCBhrgsPOtFMbeixIGoFnjeWxaKQTHCmUo49obxji9emCzsVfGA/AWRbPJTCftiWC/fiU4m3Mo0y14BXKXE8NMAG4x7iedXP1q8B9eD1EMcGmJZHzIRlFT8Y3rtWthjRlRjUNAyyG7y8pNnxMPQIvx4XVzEqsKp/FT+QWSTUDTEANIA/g1gf8FvhWnCGue7UojqBGhQqCYpv3BBw3g1MUnREMcFl35RK1QMggjPsvel5hsgflgTbFl41oukaYhViGqBF+V4FWQBr3un65M67bEufTe8SL/JgRw/DbSoCdaVrRiNUxj8TRXAr35IyhxrimLnYpMFQZB8S0VSNMQ6+ktRKpryaKka4iJgxDsOMRMWwHGcUwFMBnA0gKcBzALweyLaTxrBHPIMBUyYMAEAMG/evBKzqkxY+icAaFH0O6EkEhPPtsD2yoOlfwKAFkV/RfMr0CLbLA+W/gkAWhT9RjzbSvWCiP5GRGcBOBjAPADfBdDbcZx7HMf5cihFtLCwCA2WZy0sKgeWXy0skgWlhlga2HG6AzgDwFlENDGyUpUIx3GmEtGMuMtRCiwNyUEl01EJPFvJ9euiGmgAqoOOSqbB8mv5UA10WBrCRSCBuFLgOM58IhoddzlKgaUhOagWOpKKaqjfaqABqA46qoGGJKNa6rca6LA0hAulyYSFhYWFhYWFhYVFS4AViC0sLCwsLCwsLFo0qlUgToQ9SomwNCQH1UJHUlEN9VsNNADVQUc10JBkVEv9VgMdloYQUZU2xBYWFhYWFhYWFhamqFYNsYWFhYWFhYWFhYURrEBsYWFhYWFhYWHRomEFYgsLCwsLCwsLixYNKxBbWFhYWFhYWFi0aFiB2MLCwsLCwsLCokXDCsQWFhYWFhYWFhYtGlYgtrCwsLCwsLCwaNGwArGFhYWFhYWFhUWLRutyZ3jCCSfQ008/Xe5sKw4vLtqBzdvScRejLOjWqRXGj+wYdzHihBN3AXSwPOsPy68tCpZfqwCWZ1sUjHi27ALxZ5995htmzZo1AID+/ftHXZxEYs2aNXhv+XYccuDAuItSFmz4vDnvd0tv/6TBj2dbentZfm3Z7Z802DnWH5ZnW3b7q1B2gdgEU6ZMAQDMmzcv3oLEhClTpuCzLc2Y8fAzcRclFojtP2MGMGoUO4tkwvKr5Vcgv/3vuw8YORIYMyamQlloYXnW8izA7U8E3HorcO65wMCWsT5QIpECsQXQpqZwVVet6NZJbspOBFx5JfC1rwGPPVbmQllYBIDl1xwaG4ErrgAmTgSeaZnyhkUFwPIsY/ly4IYbgB07WDBuybACcULRZa8anDK+U9zFiBVbtwJ79gD//W/cJbGw0MPyaw4rVwJNTcALLzAPd+4cd4ksLApheZaxYgX/f+21eMuRBNhbJiwSi/Xr+f+aNcC6dfGWxcLCwgzvvcf/GxuBZ5+NtywWFhZ6uALx66/zQrYlwwrEFomFKxADVktsYVEpWLaM/++1F/CPf8RbFgsLCz1cgXjHDuCdd+ItS9xIpMnE9773vbiLECss/Uy/KBC/9hpw6qkxFahC4ThODYD5AD4iopMcx9kPwCwA3QEsBDCFiBpKzcf2V0u/iGXLgL33Bk44AfjXv4DmZqCmJqbCWUhh+6yl38WKFUCnTsC2bcCrrwKHHx5jwWKGQ0RlzXD06NE0f/78suZpUZm4917gW98C+vcHBg8GqvhAdCT3mjqOczWA0QA6ZwTiRwE8TkSzHMe5F8CbRHSPXzqWZy2CIJUCfrLkVLQ/82sYc+9FeOkl4ItfjLtUoSLR9xBbfrUIgiOOAHr3BhYu5EXsww/HXaJIYMSziTSZWLZsGZa5+24tEJZ+pt/VEJ90EvDGG9a+KQgcx+kH4KsAfp/57QCYCOAvmSAPA/h6GHnZ/mrpF+lfvqwJX9z0BA5f/yzatAFmz46xcBZS2D5r6V+2bBmI+JaJIUOAo46yB+sSaTIxbdo0AC33jkRLP9M/bNg8dO/O2qV77gGWLAFGjIi5cJWDOwH8EIB7jLoHgM+JyF1WrAWwbxgZ2f5q6QeY/q1bAfr0U7QCofaTNUil2I749ttjLqRFHmyftfQDwOOPM88OHgz06wc88QSwcSPQo0fMBYwJidQQW1gAbEPcuzcwdiz/tgfrzOA4zkkA1hPRAtFbElRpL+U4zlTHceY7jjN/w4YNoZfRojqxbBmwLz7iH2vX4uSTgfp64P334y2XhYVFIdwDdYMHs4YYaNlaYisQWyQWn34K9OrFzLr33i2bUQNiHICTHcf5EHyIbiJYY9zVcRx3V6gfAOVldkQ0g4hGE9Honj17Rl1eiyrBe+8Bfd1u9dFH+NqJ/OEDe9uEhUXyIArEo0fz4deWPM9agdgieVi3DliyBOvXs0DsOMCRR1oNsSmI6Doi6kdEgwBMBvA8Ef0PgBcAfCMT7HwAT8RURIsqxbJlwL5ORiBubsag9p9i+HBrR2xhkUS4AvF++wEdOwKHHcY3TbRUWIHYInnYsgXYuBGtP16DXr3Ya+xYYOlS4PPP4y1aheMaAFc7jvMB2Kb4gZjLY1FlWLYMOLTLRzmPNWtw8snAf/4DbNoUX7ksLCwKsWIF0LMnX7sGsNnE66/zVYktEYk8VHfDDTfEXYRY0eLpHzIEWL8e/7vlOfTufQEA4Atf4HeLFwMTJsRWtIoDEc0DMC/zvALAmLDzaPH91dKffV6zBhjcfh3gLlzXrsWxx47FLbcw706cGE8ZLfJh+6ylHwBuu43NJVwceSRw993Au+8Cw4fHVLgYkUiBeNKkSXEXIVa0ePrbtgUAfIK52NHrAgB8LQwAfPhhPGWyUKPF91dLf/Z561agT/NHzLDLlwNr1qD3Qfxu48aYCmhRANtnLf0AMHUqC8Eu3IN1r77aMgXiRJpMLF68GIsXL467GLGhxdP/ySdYDGAS5qJXT74IoX9/tiVetUodr6V/djIutPj+aunP0r91K7B3wzo2RmzXDli7NnuFkxWIkwPbZy39b7yxGKtX52uIhwzhA+xB7YifeAK45ZZwyxgHEqkhvuqqqwC03DsCWzz9q1YBcDAPn2LQjncADEPbtkDfvmoN8Qsv8HbswoXAyJFlLKyF7a+WfgBM/7ZtQLdd64B9j+WLTdesyQrEn30WYyEt8mD7rKV/926guXlenkDsOGyeuGCBOq4MDzwAPP008L3v8Tq4UpFIDbFFC0dTE/Z07AoA6Lfsuaz3oEFqgfiNN/j/6tXRFs3CwkIOIqBhyy503LOZV6/9+wNr1qC2lg/tWA2xhUVysGsX/xcFYoAVSkuXArt3m6e1ahXQ2AgsWhRe+eKAFYgtkofmZjS07oj3sT+6Lpib9dYJxG+/zf/tSXYLi3iwcyfQhzJXrvXtyxritWsB8JevrIbYwiI5cAVemUDc1BTMBNE1Zaz0K9usQGyRLDQ1Aek0mqg1Xmg1Ca3/M4+XngAGDuRT7E1NhdGWLOH/VgtlYREPtm0TPsqx776sIV63Dmhqwtj2b2HjZ8oPI1pYWJQZu3YBbdowq4oYMYL/m5pYb9nCDrACsYVFuNi+HQDQSDVY0G0SnO3bs/YQgwbx/YjrPN9Xa2riLR7ACsQWFnFh61bhs82uyURzM3DddZi19HD0Wf16vAW0sLDIYvdunlNravL9Bw9mEydT8wdXO7zXXpX/lbtEHqq79dZb4y5CFjt2AG3bAq1DqKnt27nTiNi5E6itzU8/SfSXHVu34lYAf+55Jt7udCywyQHmzgWOPhqDBnGQDz8EBgzIRfngA2DPHn62JhPlR4vur7D0u/Rv3SpoiF2TCQD4+c8BAHtvWApgbAwltPDC9llL/0UXITunimjVCjj88OAC8SmnAH/8I1tJuaxfaUikhvjoo4/G0UcfHXcx0NwMHHIIcPPNpae1aBHQtWvO1hXgQyjDhxemnxT6Y8G2bTgaQDuMRPt9uwMHHAC8+SYA5AnEItw6ramxGuI40KL7Kyz9Lv2uQNzcrgPQpQtriAGgWzeknVbouXV5vAW1yML2WUv/+vVHF9gPuxg5kqfddNo/LVcgPuss/l/JZhPGArHjOA86jrPecZwlgl93x3HmOI7zfuZ/tzAK9corr+CVV14JI6mS8PrrfGvBnDmlp/XWWyxgi53l44/504ne9JNCfyzYuhWvAFi8cS1/tnnoUKC+HkBOK+wViJcs4VXtiBFWQxwHWnR/haXfpX/bNjaZaOzZl+9v2n9/YNgwYMYMbOkyAH33rHCPA1jEDNtnWzb9Tz/9CjZvfkUpEI8YwbvjH3zgn9bq1bzL/eUv85VrlSwQBzEEeAjA7wA8IvhdC+A5IrrNcZxrM7+vKbVQ119/PYD47wh88kn+v3Ahb8lnPqBWFDKHrfM0xO6zN/2k0B8Ltm7F9QAWb/0DpvX6LtBnKDdEUxPatm2NffaRa4j3358PB5h8yY6I52uLcNCi+yss/S793/zmPAzAOlCfvvyiY8fsILf9hnsx+PMV2LQJ6N07rpJauLB9tmXTf8MNTP/gwfOk7927/BcvBg48UJ/WqlWsrGrbFhg1qrLtiI01xET0IgCv/u0UAA9nnh8G8PWQypUI/OtfvPJpaMju2hvh5puBMWPy/T7KnDVZsiTn5z43NJif6Kx6bNsGgA/V9eoF4OCD+ZaJ5bzdOmhQ4dfqlixh05MePcw0xDffnPtEpYWFRThwTSacfvsWvGvYdzAGY4U1aVKgnDuwFhbuHcT77Sd/f8ghfK7JxI541Sq+AQrgeXXBgtyZnkrD/2fvusOjqN7umU0PCb1DAgQiLdRQQjOUIFWxoSCWTxTEig0VULBgQxAVUAER7OYnHQXBAKL0LqGFEkogAQIkpLfd8/3x7mxJdjcb0pM5zzNPMjN3ZufO3PLet5y3sD7E9UjGAYDxb93CP1LZQGysNIbx42W/IKueTZukUWRlmY9ZaohJ8//e3vL/7t2Ff+YKgaQkAIAeLmaXCcDkNpGbizg9Xcw6QUFAzZrO+RBv2iQLEGosUBo0FBmSk4hGuAQX/4Z5zhmaBaAeriLhQnIpPFm5wFIAg3MdUy2wgQA2Gfc1aCg0VIFVFWRzw8MDaNvWOUVdboE4K6v8JugokaA6RVHGK4qyT1GUffHx8SXxk7cGozAGSBpCABg3TiImnRWISRF0DQazEAyY/79+HbhyRf4/cgTo1UtM/eXZzFCkMGqIc+AqptWWLeW4kVetaVPxWdLr5fCxY+bgxFq1REBWV7+2oH6fjAxh+NCgQUPRICv+JryRDle/vAKxa6A4K2YcP1vSj1UuUBktsBpKD1lZ4jZYowaEt3TJEmuBBeI2kZ9gm5EBXL5sFohDQuRveZVnCisQX1EUpQEAGP9etVWI5EKSXUh2qVOnTr43vXixYC4KRYIDB0TFaPzhdetEUG3XDuje3XkNblwckJAg/1ua9i9eFMIEQAQyvV6EuaAgaUSahtiI3BriatWEwslCQ5yTY+YiVt1OVA0x4NhtIjYWSEyU/7XMWRo0FA4pKeb+prssndKWy4RnUHMAgOF0dIk9WwVAhbXAaihdqDFLSmqK8KWNHSt+nhYScMeOoryLi7N/n5gY+asKxA0bij9xeQ2sKyy77hoAjwH4yPh3daGfCEDDhp/hzBlRFvr6FsUdncCBAyKlbt2K7DYdsHEjMGqUrKJCQoDly4H4eCA/ed4yaE417WdmyrWjRgGnTkmZpk1Fk9muHdCggdz/6lWgbl3gs88+K65aln0kJ+MTVw90y/lcBGJA3CaMGmK1450/L6xOkZHSsVu0sE7fnDv7jgrL73Ptmn2TkQbnUanbKyp3/ZcuBSIjP8PGjcDBmRZJOXKhWkfREOvOaQJxcUBRlPEAxgOAvyVJux1U5jYLaPX38/sMPhnxQGio+EVMny5a4j59gN9+A4YMsQqsa9DA9n1UpZ/lPNqjB1BeCTwKQrv2C4CdAFoqinJRUZQnIILwQEVRTgEYaNwvNBITOwLoWKBc2oVGtHGg3r8f27eLMD50qBxSzQDOaHEtg+ZUgVjVZnbqJBHWR45YazZz379jx47oqOZPrGxISkJrj2oAOpoXH61aiYaYzMNFfOSIBAC4uJg1xI78iC2/j6YhLhpU6vaKyl3/CxcAoCOqV+8IzxsWSTlywbtRDSSgOrxiNS7iAsApCyxQcCtsZW6zgFZ/rxt18NvZJ4GoKGDtWuDtt8XP4bbbgDvvBH7+GR06SFlVafzHH0Dv3qLUUyH931ogDgkRzXEuD4xygYKwTIwm2YCkG8nGJBeTvE5yAMlA499Cs8CSwOnTEQAirISXYocqEB84gPXrJcd3WJgc6txZBK7cfjF6PTBwILBwoflYZKSspvz8zEKb2jAaNxYBODJSNkURYS44WO6vCsQRERGIiIgotqqWaSQnYz1c4esbATc347HWrcWVIi7Oios4IQHYt0/eKSA+xIBjl4nISOEsBkRrX9y4807RolVkVOr2ispdf1nsR2D9+ghUSbSvIQaAGLcA+MZrGuICQLXAAkVogQUqd5sFtPrXvfgx/ku7AGzYYNb8NWgA/PMP0LUrMGkSqvnoERAA7N0LvP46MHw4sH078Ouv5vucPy/zqWVmuoED5e/qImutJYcyl6kuIQFIT58BYIaVebvYoQrEx45h3z9p6NrVnGbZ21tSGeYWiNetk6zCuQXioCBrNgRVIFZ9ko8elUQdAQFC1entDbRvb77/jBkzMGPGjOKqadlGUhJmZyQhJ8ei/q1ayd/jx+HlBdSvLwvbe+4RTf7TT8tpVSDOT0PcubP8X9wa4kuXgN9/l60io1K3V1Tu+otAPAM//jgDvimxSHarAXh52Sx72bs5aiRqArEtlKQFFqjcbRao3PVPTgYOZK/BDMBsnlbh4wNMmiQde+NGdOwIrFoFzJwJTJggcorlOuL8eVn/mpRXEHaK1q3F86K8ocwJxJaUWiWuIW7YEDAYoD/wX5520r27ZK5T2Q0A4PPP5e/+/RJpqQbKtWsnJgTVv0blIFY1xOnpwMaNUk5FSEje+1dKJCUhBy5wd7c4lot6rUkT4Mcfga1bRfuqcgrnF1Snfp/evUUjX9wCsarxtzQxlRQURfFTFGWLoijHFUU5qijKRONxjdtUQ5FBdQfLygKqp8Ui0cu2dhgArlcLQN20c9ogZwMlZYEtMly/DjzyiDmCXEO5QWws4I4sGFzcZCLMjeHDgdq1gW+/xaBBQNWqwM8/A199BQweLAFzqalSVE3KkRsjR4qyWWXUKi8oswKxjw9KTkOclCQd/L77AABBWfvRvbt1kZAQWVmpzuKRkcJnq+bv/nOdAZcXrEJGBk0a4osXhRHh4kUJDqxa1SwEJyebTf2ACNzJySW8CCiLSE5GDl2tVpxo0EBengX1GgB88AEwerS5mLe3pI600hBv324Kkz1zRmhi2reX/n6rAvF33wkdX35QNf6nT5cK53EOgFdItgYQAuBZRVHaQOM21VCEUAXizEygduYlJPnaiWYFkFQnAO7MMmsINJRf/PWXaCUqaaa38gwRiDNBN3fbBdzdZbGzejXG3XMNCQnmeTYsTPJk/fuv7FtyEFti5EiZ81asKJ46FBfKnECsalVr1RIfz6t2wwiKEGeN3Jh9+iDNpw4640AeDfHw4bISevBBcST/4guxDM6bJ/LapaV/odGz96A/NqNdOxHa9HoRhi9eNPvYtGljvqelhnjwYDE7LF5crDUt+0hKQjZzaYgVxRxYB+CFF4BPPwXesCHK1axpoSE2GIBBg4Bp0wCYF1jt2olAfKs+xIsXy3bzpuNyqkCclmYWHEoKJONIHjD+nwzgOIBG0LhNNRQRUlJMtOHIygLq5sQitZp9DXFGQ2GaMLmnaSi/UM1e+Zi/tm8Xl1QLin8NpQxVQwwPOwIxIDRs2dlQfvnZFHMDiHXVw0PcJvR6CZ6zJRC3bStTdnlzmyhzAvG5c6LFr1ZN9ktES6wO0M2b41TVYHR33Q8/P+siNWuKz3BqKjBkiCyOH3lEBKuhQ4Hre+UeQTiKNm1gxYZgKRD7+IjvMGCtIa5XT2jZliwRrXJlBZOT8wrEgBX1Ws+ewEsviZycG7VqWWiIL1+WD7Z3LwDRvquBjLeqIc7JkUA+0nRbh+XatpX90nCbUKEoSlMAnQDshsZtqqGIYMlPmpkB1GccMmraF4hz/IWLeNv30Q77joZygNOn5W8+A9sPP8g4uHlzCTyTBqegCsQ6Dw/7hYKCgC5dRPNjYd708pJkYhER0v9zcmwLxIoiWuKtW0tIqVlEKJMCcfPmC/DllwsA5HIhKKzd2WCwfVwViAMCsD09GK30R6Fk5E131ratmABOnhTT+wsvyPFhw4DaGcJQ3b3qcXh7W/DlRutxMYZWvLhBQaINVhN1qJg4UbQuffoswIIFCwpT0/KLpCSMwFA89liu+rdqJT05H1WDlYZY/a5HjwIZGYiMBJo3F9eKWxWIIyPNmfAcZeNRyz3yiOzbmzcmTiz4MxQEiqL4AFgO4EWSTutpCpJdcsGCStxeUXnrr1o9/P0XoFr2DLhCj+y69l0m6nXxQw5c8PeSaAwcaJ3aXkPJotBtVh3QVMHYDtQArLJG6FBZ+ywAxF40YBH0WHj3XY4Ljh0r0f+50tWFhUn+sn37ZN8el//IkSJylSe3iTIpEL+Nn9Hr2QdRpzbNGuKDB8UR91ZVbTt3inp2//6856KjgRo1cF1fHREJneFCvV3V9IABkkRj5kyz9m/AAKCJIgJxezfRYvr5AQqIoa8F4Z24cWjcyCzMT5wIfPSRdWQmIPRrvXoB4eEt0aJFy1urZ3lGdjaUjAx4oCnat89Vf9XXRM2pbQdWGmLVFSYnBzh8GEeOmLXyBRaIjx4FfH1xfI20vxo1HPNSq+fuv19MTLaa7bVr4npTXFAUxQ0iDP9EUh2Wijy7ZMuWLdGyZSVsr0ZU1vqLQEz079oQ9bK9AQD6evY1xGMecwX8m+Ch7mdw86YE3QAytDtihrHEwYNaHFdRoNBt1gmXibNnJW7DxaXsCcSVtc8CQNK56wiCHi1VAcYeRo+WoJxvv7U6rNLRLlkif+0JxEFBQMuW5cttokwKxNmXFmPtf/9hUPPTZg3xypVi/j506NZuPHeuqOzmz897LjoaCAjAnj3AfgTLMVuCsxF33SXMJCqqVgXaVhNutSYZ4ufq4QEE17uIOtdP4AkuxqDT80zl+/cHXn7Z9r1ffBE4e3Ytpk1bW7D6VQQYHRL/w3mcOpWr/nfcISuGceNERW8HNjXEALJ2HcCpU2a/7Tp1ZBK2ZzTIg/37gZQU3Ny0D3XqSLbLXbvsGy127ZKsgwEBopW2NW8UZ7puRVEUAIsBHCf5qcWpIuc2Xbt2LdaurYTt1YjKWv/YWGA0fsFdK2uhBaSJKY3sC8SKArjeFoAm+mh4eko+gNhYCVg2uvk7REKClP3gg6KqQQVHZKT46qmBORaw22avXBFtzpYt9u+bmCir+Ro1JEAyLc1msU2b5O/jjwtNpprmtywg3z67f7+wTlXAKPfsC5exFsDa/IJbq1cH7r0X+P57K//Azp3l1B9/yL49gVh1m/j77/LjNlHqAvHbb5tXj4mJgE/SJXybegmzAdzpJck5DAaYC91Kr7p+XdS6Hh5AeHjeaKizZ4FmzbBrF3BR8Qdr1nIoENtCExd5Lt/UKyYVRlgNucdRtEHPZS+bQzMd4O67AQ+P2fjyy9kF+v0KAaNAvBf/YsWKXPX39JRv6O5uJiC2AVVDTAKIjsZNn0ZIcquJPz/YD4PBWkOs10ubcwrGdpdx/CxCQmRivnbNfnzQ7t3CHKIo4hpjS4YvToEYQC8AjwDoryjKIeM2FMXAbTp79mzMnl0J26sRlbX+sbFAN9cD+NyQjSv4CQDg4m/fZQIA0Lw5XM5FIywMWLNGLCRZWfaHxvh482Lyzz+lbAGH5sqLHTtEYN24Mc8pu232998l6OXHH5GTI+6AlsofACY3iXX6QbJ/xnb2wYgICTh//nnZVwXk3EhPF47bKlXER9XLS8ZxW5lqP/hAkh3lVkTcvClju61kEL/9Jue2bjUfy7fP7twpTrIvvoi/txDt21cgPvm4OMwGMNtGu8iDd9+VjxEaKh0WovHv31/mz1q15LvZg+o2sWpV0Tx6caNUBeLEROCdd4APP5T9c+eAATD2GkVBl6RNSE0FLhxJEpJe4NYE4u+/l5H0yy9lNfvzz+ZzBoMIxAEB2LULCGqnQOkSnFf9d+GCmBCGDZPtvffM50hUT7mImKpGs74x+Kub6wHoocMAbEJW4wAR5NTr1W34cCt7kquraC9v3nTSZXrmTGDbtoK/k7IIo3+wHi553EkAyFI0PFzYJsaOtfmCataUT52WBmSfjMahlOb4zzUYLVP24447pCMDIhADgOG99/NqQzZsAGbNsj5mbHe+16JNAjFgW6hNSJBHVMsEBsqcYdJG//knMG0adu0kfH3zeSe3CJLbSCok25PsaNzWlWluUw3lCnFxQGuPaEDRQQFggAJ3v3qOLwoIAK5dw30Dk3DuHPDZZ+I6duRIXj3FxYsS19Otm6x/VYXe4cOlQmNY/qBymG7f7vw169fL3z//xKxPiHXrZChUDwNA5hFZofyaNAQAkHU0r/nLYBABOCxMrHJ169p2mzh5UsbJBQtEeHrhBeCZZ8TKZ8s77tdfRTDNvYD65hsRoL/6Ku81X3wh5/r3F4HaKaug+u42bcLnA9YgMlKmhfIOEnC7fll28kSu20Dz5iILBQWJ/DJPLN2q24Q97bCKdu3E4OCM7H1LVCSIO324AAAgAElEQVSpqQUw8+aPUhWIVRn333/lXZw7B4QhAnR1A+rWhf+pzdBBjyv/2yrLEZ2u4AIxCSxaJL3u8ceBjh0ltZw6osbGAllZMDQVl4mQEAgf8dGjwMcfS5n0dFHdrl0ruv9Dh4AZM8x0EPHxUDIz4fe4MWehkR6sTfp+HEMbXEF9pP+8StLdXb1qvf3zD5BrperpKY+Xr5lBrwemTJHRoCLAqPU1wBWurnbK9O8vi4Bly+RvLlhmq9OfikY0AuA3Ihgts45gw5pMkyBcuzbgimzU+mIa8Oqr5huQ4rcybZr1rGtsdwGIRvfu4j/u7W07sE5t15YCcWamRdP98EPgvffQY9tME491ZUBSkij5f/vNodeLhnKC2FjpD4Zq1RGH+jiCIFStaa/jGmGk2BnWWkwrmZnA9OnS1SwXlwkJQkUZHy+Kk6+/FqHMy0v6ti0aw6tX7SorKyfUGIp8BOKbN4U1KTstW/iFa9cGYmOxbNph3H23hG+MG2desGxeJBriVi+IhnjzgrwC8eHDYkELCxMrWViYCMSWQ+qKFeIFd+mSfNulS2XKnT0baNZMlLSWSEoyezB8auEElpNjjsXYtMk6MVNsrFT/1VeBBx4Apk4VVqj8csPknDqLS94tcAyt8VWVV9C+ZWaFYAtMSABq5xjpYZwRiAGhwNqyRZR3zz8PhIdjwAA5lZ9ArH77zZvzeec7d8rkbUOVfPKkMHzlQVqaPMBnn+U5de2a6D0LunAuVYFYHQCzs6UhnztLEYirVwdq1oRrcgI64SCUzREyEvbuXXCBePt20diOGydfZ9w4EWhVu5uxlV90D0BiolGIGTdOONCmTJFl4YQJck14uPjSzJghakh1wFGfqU8fccs4fhwg4Re/H/sRDA8PoHqP1lLJvXutt8ceE6HYIuTa01P+2nD9soaaHq8sOWcVBsYVouJmI3uOJV5+2fx9/vrL6pQqECfEZcDzeizOIgB1hwRLI7PwB6tdG/DHBSgGA3DggLk9bNsmC5r0dIwfcQWjRhkpgywE4q5dRZPftattDfG//0pT69pV9lU2kVOnICvanTuh9/bBtIwpuL/6X3lvUEHx6acSZPjAA6YcOBrKMWIvEY0yo6Hz9sJJtEQw9qNq1XwuMgrEdZKjcfvtEpD8/POi61CTHgGyHo2KEm1gnz6yn5gIPPWUnD98OO+tx44FBg4smrpVCKjz0+nTDrUrn38ubDgTu+4AkpKgn/YOAGCE+zosWCCCalwc8Mor8o3id5zCDR8/TPm8Hm561sWlv0/lodFTtcGq4BQWJu7JqhtEVBQwZowoFg4dksWPJXr0EBnJUqDZs0f2Q0LEeq8SXKxYIQbct94S4dhSplq+XK4ZO1YEpK+/lmfLhxwDNw+fw39pgTg14VPUTzmDV9znVgiBODYWqI/LoKKznaXOHqpUkRfdqRMwaRICG6WhVy/g9tvzvzQsTARxu+FfBoOYBnJybKr4x46VmJ08Ls9btsjq2EbU3kcfSfuydJNxBqUqEO/aBdx2m3AOr1sHpB84joaIg65mDfHaBvBIg02oeXAT2KcP0KJFwYW/hQuFnUJVxY0ZI8L1okWyb2zlyw4EQFGAfv0g0sw334i+f8QIcbl4+21xcQDypBI2PVPTplKhEyeAuDh4J13BAXRG48a2OXMBSGtJS7NSNXq5ZsMbaVZprG1C/d0KJhDr8hOI1e/Ttq0IxurAD3P65rRj5wAAmY0C4N0nb6BknToi3JpgbA9cuMh0KPm/aGzeLAvjnHPyjv0Qg6qesnjp3l2i3jMyIJ16zRqcePtXnPvoVzzd45DJHcJKIP73XyA7G3+P/QHH0AZhi0Y5+XLKP86fF5/Cp56SCbEy822Xd5BAVuw1eOckA16ecHcHcuCWvwtQc+EiRnQ01q0TwUbN4KkqMq9fl8D2hx8Wg9CkSdLH3N1FKAOE9skSKSmyNj571jwcHjsmxystzp0zs/NYrjZyYccOcWlocXo9suGKCdsexn50xlP+61G3rizsJ00SStp77wWC3E+haucWAIAqHQLRxv00Hn9ctP0qIiJkmlTpRlXBWE3oMHasTMOrVpk5+i0REiLC28WL5mPqFPntt6KQULXCc+aIaDB9ukzBy5aZr1m2TKaJ1q1l2njqKeD110WXdMOBs5jX5bM4h2YY8vlgYOhQjIx6DynRV4vSOl8qiI0FGiDOfpY6R3BxkZcdEwNl9ixs2ybGVAAyd9sJ0rP89jahklV37Cid2EKe2bNHxoWcHJO3hhmq2nj3bivKKNLcBubMKVgVQbJEt+DgYJKkwUDWqkU+/jg5ciTZsCG5sN0XJMAL27fzwoULZFAQrzdqRwI8Pf5jcvp0UlHIzEw6hRs3SE9PcsIE6+NPPEG6u5O7dpFvvUWDTsf6tbJ41125rj99Wh7y7rtJvd58PCGBBMiPP5b9L+S5eeWKVKZ5c3LNGhJgT2xjaKiDZ0xIIHU68q23TIfO9BvIvahjur1d/O9/8ruenvJCyzu++YYEeF/ITvn++eH0abJKFavve+SIvJKtr/9BAvxg+HZ5NzVqkOPHm8qlppLj8bUU7t+f9PUlL1xgjpsHI9Bfjv/wAy9fJtv4J5MAT+hayfFTp0iSK1bI7s6dJFeulB3jpvfxJZOTSUrT8fIiX3qJ5CuvkO7ufG5sKjv6nKKhVi2yhPtgQTe1z9rDhQsXnPped9xBdu9OLlkir+nkyXwvKRdwtv4VCTdvkt2wS8brxYvZvv0FAk4OzTVrkk8/TV64IGMmyWeeIX18yJwccsYMaR+RkVJcryfbtZNhmCSbNCFHj7a+5fLl5u4XHi5Dv4cHOW1akVXZEqXeJx1twcHBZEqKvIxp02Sue/VVqwqobVavJ6tVk6Exo2V77qnSlwD5v5ZTaXBxkfmJZHo62co4/GVWq20eSx97jOm1GhIg77+fTEoiMzJkvHv+eeuXdttt5LBh5Kefyn1+/NH+C9671/wtVQwdSrZpI/8/+qgM/evXS7m5c+X4pEmkm5t8/9hYERfeftv63hkZZGDgBdavf4GJiTZ+PDFR5o6an8j+iRPU61z4IV7nxYv2n7k8YMkScgtCebJ9t1sfs+67j/T2JmNiZP/oUdLfX/r19es2L2nXjgwLs3EiOZls0IDs1k3mc4B87z3T6VGjyKpVycGDZQpPSTGeMBjIZs1kMADIn34yXbN7txxq3Vq+f8zSCNLJvlNqGuIzZ0QTEBIiPj2xsUDD4xGI9W4Ov5494efnB4SFoeYl4QP+8mSYeGeTzufB/fFHUS2MH299fOZMWbredx+wezdSavjh8nW3vEkSmjeXVfby5bDKX1i9OlC/vrWG2MND1I6tW4uaYvt2UKfDIXS0uQK2ulfXrubl08WLCNi6CV0Qj2vH88ktrK6kMjKcJ/IsyzBqiOs2ayXfPz80by4NyMJep2qIr+0R7a9faICoBjp3ttIQe3sDt7lGI8fFXbT/ycnAfffBJTsTc2q9L4Wio1GvHrBmvrznLYZQOW7USHfvLruTJwNbp25EiuKD3jWO4sr8ZdClJIuLDaTptGhh1BBv2gT06oV/93ujbs8WUCqAHc7Pz8+p7xUbK0xGKv1nVFQxP1gJwdn6lxROnZJwh3Xr7DJiFRqq/zAA+HXvjmbN/ODp6aRbYkCA0AEEBgKPPgpAsk+mpIhBb+5cMaGrjDA6nWiJfhIiC7Rvn1dDvHatDKVeXqLxXL9eNJaVNiOe6m/XqpVEJubSEKttNipKfIMHtLwIj6jD6Dh5CBYtAgbNGQJFrze5pHl6yjtd93Mi3G9eM5u9AgPheT0Wn72favIJXrBAwm7UwCsVqi/plClCXfrQQ/Yfv0MH+U3Vj5gUDbEal/HSS+J99uCD8t3/7//k+MiR4h23erVY+Ek5ZgkPD+Cnn/xw9aqfVfiICUbTrMG/qey3bIlr3YbhMXyH6JPl26x16ZK4TDRr0eTWx6xPPhE1/+TJwqnWs6fIIImJMpfaQFiYGEfTc+c7+/BD8cf5/HOZz/v1E4JjgwExMeINMW4c8Oab4nbx3XfG606elHl40iTxf7SI+vztNwnUXbEC8HTNge7lF+EsSk0gVs0fISEy+LkgB71z/kZ00wEIDw9HeHi4Sdee5lUTn/3dEXGuxg/ojIsAjcF0wcHi92KJmjXlbd24AWzciOMZAWjXzugukRs+PtbCsIpWrUxsEoiJgckvonVrMZ+Hh0Np1Qp3PlgFQ4fm86wDBohtICkJWLIE4QYDwgHz/e3B8j1UALcJJklQ3cWk9fL9nUFwsPBtGn2wVYE46VA00uCFDnfUs1kOAFq6nUV8labim96mDbB3L/agK0JeDJEFk1FYbe4u7/asn1EgNh5v2FDSeB87BjSK2oS9XqGYta4N6j19r9xv4ULTbwUGQhY4hw4hs88AREYaB/d8nS7LPkz9NR/ExorLxG23yX5FCaxztv4lAVL8aO+6Szy8PsqPVG/kSJmMCogLF8wCcfj+/QDCTenq80Xz5tIYXFzE3EmiTx/ZfeYZ8TV97TXrS3x9ZRELiLAUFWV0VYLMzb//LoqVbt1EeDYyRNn0Na4UMC7aVxxshozOPcUkrb4wmNusKnD2SRVKB7cRQ/Hkk0DVgd2FZ9hC0GjaFBjSwhhA10JcJlTBeOLwM9iyRRY1EyfKtwwNtX6ksDARiDw9xZfXrhshRKDp0sUsJ5w6JdN1jx6y37Ej0LevTJnjx8s0Dcg1TZuKUPTbbzIMq14jloiODsedd4bjm2/yhKGA0fLu3Fs1Mx3TPzYWDXAZWWscJ4Yq64iNBRoiDsvTkm99zGrWTOJ4fvxR8gM0aiTyy1NPCZPXsWN5LgkLkwWq5bosbuc5ZH88GwfbjsHk1SF4+23gxt1jZX795x/Mm2eMcb8vBj3d9qJrV4mfMxhgdpcYPhwYNEgoSQwGkPLdw8JERPsq+Bs0vFEALmlnVclFtanm1+eeE5NHTo6ouR9ruZMEuPrh/zE0NJShoaFif3F1Zdqw++nqSn746LE86nG72Cn344IF9sv8+CMJcBGe4Dff5H9LKzz9tNiaDAayVy+yb185fvCg2Xb38MPO3WvzZim/ciXp789Qb2+GApzewMGzk2K6UBS5dvXqAlag7CHjuVeYAm8GBBi/vzMID5f6HzhgOlSlCrkCd/Oorq2pfdkqd9QrmHvrDJadzz4jAT7tupBXr5Ls00c2kly0SK49c0bMj6+9Zv0M58/L+TlzzMfmzJFjhw6RJF9/nXzI5VcS4P4vdxEg//jDVLrUzayOtvxcJkz91QHS0+V1zJgh+7VqWXmwlGs4U3+HWLrU5IZTWJw7J+95yhSyRw8xVdpFbKwU7tKFhw5Je9y61do7zB5mzSIX43Hm1GvA0NBQ9ukTqlrX80dkpNT5yy/l96OjScqfffvIEyccX656in35pTzz3Lmy/+uv5OTJpIuLmFk9POT4tWtOPpfzKPU+6WgLDg42vZR6iOP4ukZ3rm3bTBVQ2+yTT4op2nDPvWTjxtaudw8+SNavb90gfv5Z7nXkiOzv3y/7y5aRFA+YESNsT32JieI28euvzr3kSZNkuM3IkOZi+bMkuWkTGRBgttyrePVV0tVVpsbp023fW22zDRuKq4fVc74jY/eij8wNJzMli5dRl0db3ePcw5dRPDA8lQQY2qxZ4caspCRxWejf3+RWw/h4kYkGDcrjwpmcLN/kjTdkPzubDK/7HNPhwQC3C3R3l+/lVyuVWd5VmTX6EVarRr7Zb5tMFu7uXDPnNAFy7VqSAweKTwRpkuO4ezf37JF/v/2WZEICs2vU5haEkk72nVLVEHftag50fKRBBAxQwL4WalpfX2DZMnh9+j7uvx/4YqVzGuLTp4Hzby6SyMjRo+0XHDMGHweHY1H11xyab2yidWuxNV25Is+jmh9uu8289A0Odu5ePXqIre+tt0T10qQJDNChdvxx0AZtyPHjRg1ITIzZrlgBNMQZ8clIQlWn2WAAiCsEYOUOUauWaK8SawSYA2ltlPPLjsY5RaLeE0aOx0tu86Af8yjq1IGYdS1ZRBRFvnHTpnmzcaiM85Y2wkceEducMVgvMBAI1W9Cmns1vLVK2oXqclEZEGdk+mloTGTWsmXFcZkoFNLSxN57C1paW1A1MPffL1tkJOwH5xrdtAwHDyGkYzqGDROt3sSJ+dMV/fcf0Mo9Gi4tpP/odKY46PwRFCTsOur4ePAgAFE8BQebXWrsoWtX6Y7PPCNa8Oefl+Fz8GCgVy/RGCclAU8+KeUrpZb43DlkuXghzace9rj2BABsn7k9z3fdtQvo3S0LSsRfomK3VNsOHSrRZ5b+KadOSRk1ONIqYliC81atkjip3KhWTfq8s1STPXqIQe/AAXGdqFrVHM8OSMDlmTN5g/JGjpQgLNpwl7CETie/YWx+JiQfPosk+MK/Y03TMfcqblhV9VHcFrW2/KRds4GsCwXgIHYEX19xGY2IMHf82rUlsnHDhjw8aT4+8q5Vz9DPP85A2NWfcKXnPTiT5YfMTJFrqjf0xrdpo6APX4Z7b36Ld7YPEEuFmxuG/T0JjRsDX85MEfoI1fQ+aJC0yfXr8dtvEnA5YgSAd9+Fa+J1/Nw1Ly2bPZSKQJyeLhQcqj8QAISkRuCYe0d0DKttXXjECOC22/Dqq8CVVB+kuFaH4dwFh/d/45kk1N70K6I6j4ajsOf584E39j+AYS/dBi+vAlaiVSv5e/SoOOaoArG3t5mcz1mB2NNTzPZHjogfcp060Lt7o0XOccvgSRPeeUfcn/XnY+Q33NwqhECcdT0JyfC1nZTDHpo3l5HWQtCtWYMIQDTYLMB+uYQE+OYk4JReyqza4IXPsp/F+Oc95HxAgHzXjAx5t/Xry3sOCMgrEEdECFejZW74WrVEIvnxRyAtDT17AncoEfgruy82bnZFv35mirjKANXtXxOIzbh0CUg8aFx02crtbQNnzlhZvvNgxw7RA7RrJxm9AHNCC0D8Lk2UU8bZSafPwfhO+7Brl7AfzZsHvP++4+c4fBhooYs2C0a3gnbtRCOSWyLJB02bStvZvdu8HT8u3VudUzw97TNSVAqcPYsY16bo2UtBxOG6iPVugfg1O8xR9wkJMJyJxpgjk/F2/DMSQzFkiPU9Bhkz0VkKN6dOiQSqcoP6+srYZ9l+Dx4U/1J1mzPH9grr779tpyi8eRP48kv0aCcUIbt2yda9u23vRQBy/3nzgMmT0XXFZMyvOhmP+v9tNSTbQqdO0qcsc0LoT53FWTTDbS2tfTp2tXocrswxO7OXQzCuiARi9R65/V6efVaUgi+/nCeAISxMpt/t24GDb69GTSTAf/pY0/mWLeU7X7tzLDwN6fgWT0DXtYushqZMgW71SnwydAtc/t0iKyW1vdauDXTrBq5bZ3KXqBl3VIIRnngCd03r6HSVSkUgPnhQVnAmDVlqKqoc2oGgiWF2iZ6Dg0WAjc7xw5H1Meb+NW+eFfF4ejrQeMsPqII0PL5jXB7/IBW//y6D//Dh4uRfYKhL1S1bRCVh6aCucrx0dP5DmLSLjz0GKAoMXt5ohRM2tTuRkYCSkwXlymURvhs1qhACsT7hFjTENgLmAqpegy9SULVjgP1yRu3vsXTxE1u3Tl6jqkhGs2YyyJ4/b20ByC0Qk6IhHjAg7+Cgstnfey9av/8wmvIsRnwRhuxsI7dxJUJuDfFtt4ny6VaSE1UUjBgBvHqPMZOEEw7VN26IcnXGDPtlduyQcdX15DEEPj8YExqtNfnTpqSIVi04GMjKJPjXX9jmLqkbP757J7p3F9llzBgxVtmT0bOygNNHM1E746KJV/iW4OUligW7BKX2ERgo/sLqps4btWqJkDN0qHThevUqp4ZYf/osojKbokcPeScN7u+FULcdmPUJkTPrM+DwYeguxuAlfIqOkT/IKkPlx1JRr54oar76SlZSgKymVK2wisBA8yorOVle/syZQjw+a5YIR7kzGBkMQkjet6/4naqIiZHffPZZ1P/6bTRpIj6+kZFm/2Gb2LVLTAWzZkGZ8ymeTpmJRVmP5puBQ52iLRdN7rHncEHXFLljzlzatcF+t+7CP5efCaUMwmAA3K4XMClHQeHuLnx4p05JUJaFNj0sTF7b8OHA48oS6Bv5QxnQ3+pyb29g6upuuNZ1CJLveVQW7bVrSxtq0gQjd7yI0T5rkar4ID24t/nCoUOBvXuRci4ez3TeJe2qenVgxgwTW64zKBWBWE1mYBKIt22T0NDcHTIXJkwA3AL8oD8fI8ndbtwwS7XGDrnn+xN4N2cyrrXshZTWXU1mQ0vs3y9mm06dJBWk3axojtCokdgBVInbsveMGiVZ8QqSl3fkSOnxzz4LAFCqeKMpziPmRKpVscxM0Y40Qix0ILLq+clvF5NAHBHhRIKQIgJv3oKGGBAp9vBhaUMAWrqJwNr49lyTdXCwuZxRqI1MC0BamqSWtLIYqhN9dHRegTgxUUJeAbEQXLmSN6QaENby4cNFffXvvxLdMWJEAStXMWBLQwxUnMC6goKUZuETL+2Q585ZE7nawPr1oh1escL2+ZQUmdjHVwsXv4INGzDr+v/hxN+XsWGDGCzU2N0TK49DiYvDkqyHcLNOc3gelOgqnQ746IVYjMbPWLPKNulqVBTQKOccdGDhBGJABuECaojzw19/STIJwDYjRWWA4ew5nEUzkxCp9O6FGtnxmHN5FFwnvQTUro0LTfrAS8lEcnymKAhszVcffiimjE8+kf1Tp/IKxCYKHUhu5MuXRUDNzJSxUaezzv0MyCQcHy8rmWefFbaAAwdEKLhwQYSpL77APUGnsH69CHMOBeIlS0SaunEDyMyE8ssvcL8ck6/mQY23N63JSFRPOIvE6s3y5K0ICAAWZI+VMX/fPof3LYuIjwfqGopQQ2wPgwYBK1fCFDluNAV27SpNzCcxBv1zNsJl7GO2k4MoCmrvWQffFd+ZLRGensAnn8DlyGGMTluMvzgA0973MF8zZAgUEnOUVzD8035iLtqxA6hXz2HwZh4462xcVFtwcDAfeEDo40x49VXxnjeSzMXHxzM+Pt6mL7dh/FO86V6LALn9FSP5pLu7RI/ExjKuekteRR2mn7zACxeE37hxY/LSJbn+3DmJE/D3l5iSQqFLF3NQ23//FfJmZsTHx/PcvMUkwO9ePGB17r//5OdmjfiHBLjuxQ3kQw+RTZsW2e+riImRAJU77ijyW9tEXP0OXIW7eOmS/e9vE2qghzGAbfUo4/7Ro/bLzZxJAqyKRNPhlSstyqoBR3PnCufiiy/KcZV8eP9+2VeD586fv/WKl4FAHEdbfkF1jvqritdfl26qxlocPcp8uUjLC5ypf25cuSL1/7358zQF4eZur7nw4INkFSSzEWJ4ZtfVPOc3bSI9kM5sL18hfN6yhXp3D67CXWyEGDZCDOe8In+3DRWyXz+c543hD5P16snHWb+erF2bBDg78Eubz/HDD+TdMPaDXbtuqf4mzJ4t97matz5FgVdfleC67OwivW2p90lHW3DHjiTAVzHTHOioErQD/KHuS7wad5kDBsSzbVsnavvgg0IsrE4+n3xiff799+X44cPSyR991Pp8z54yV1rinXdk7oyLI599Vq5XFNLPT+4TF0f6+vJM2ztN3ePGDTvPl5IiPPL/93/mYxkZwoubm7A6KYl86SXGHz3K+Ph4GgxknTqSD4GkBIYB/CZoDnPjl1/Iqkik3tMrb26DcoADB8j3MJUGnY7xly/fep91Frt3k3XrStTmP/+QFC7+FZ3foylIvSAwGCTQHeCPfb6mTkfu2EGmpZFPjtXzCurIfUNCbI0nTvWdUplc/f3JBx6weNSOHc0sDfnByNrer3sqv9Y9zRwvH/L330mdjoZq1ZgNF77R429T8YMHhfC9Y0cR8Nq2lUBIy2jVW8YjjzD/3nqLMA5gSwZaM2qoAZUXPvqJBHhni2M0vPa6sJE7Ex5eALzxhrl6x44V6a3NOH/etBC6WjWA4e5jCn6PqCh5yMWLZV8doFNT7ZebMIEZvrKwGjxYXl9SkkVZg0ESnvzf/8k1n34qxw8dkv3ffpP9YcPIwMCCP7M1Sn0SdbTlJxA7g0cesV4EZ2RIPprHH5fg96ysQv9EuYJKgnO5y1BmKu7MuyKzRmYm2cA3mTdda5o75bhxZFoa09JkvpkwgRwBI6PAxo1y4SefmMvn2i5Xu41Vq5L6eUa2hyeekL/t2/OsXx8mowoTDkTneZZJk8hZukk0qBQAhYHKsLNhQ+HuYwfff0+rtUZWllk5YgunTzuV46jU+6SjLbhNGxLgi41/Mz+xXk+OGcN/R88nQG7ZInLKk0/mW1fRInl6kkFB8jJXrbI+r9J+BAWJ8iD3C1YzrVy+bD4WEiLJGEh54Z99Jpk3LK/96CMSYBg2slUrB8/33Xdyf6PQZcJzz8lqyHJunjRJyk6dajp0xx1kp07yf84uyQiy9J5cdaQ54cPFPg/KAtLGfHsr3aGIF2t2sXYtuQhPMLN2g5L5QVKE3pYtZaH066/yzgICyH79bu1+kZFkv35MOn2F/v7CWtJOcrfx97BPqR/3lEjIeeFU3ynxztq+fbCVfKGuyCyzkyxZsoRLliyx/UKMjf/6rihGuwVyg9swYSwydp6JmMOvvrK+ZN060XR6e4vgs2mTw1fuPFTBq0qVIs0Ut2TJEi5ZuJDZcOEvLd60OvfGG1KHnPelvlWQzD+GziMBrv46tsAy8X//2VbOpKbKArtvXxlTimVBnJQkq5OxY0mSNz1q86fqTzv+/rag14uG4JlnhN+rc2cxATgqd8cdvNmqKwERzAYMsHHfNm2EWsZSAL55U/Y//liW3B4eouEoHEp9EnW05ScQO/O9BgwQGjBLGOdtAjIpOZuAsqyhwO2VZmNFerNWjPIPIwFmv28/NWVEBPkAhLZvfp3p/LXD1ogAAB2tSURBVK3RRKrC65svJpve4x/VHxKaInWW1etF0F60yLQt6LaIr1RbxHtb/CfWH0uqyKdkQjm4+jxvwpdXA3vmyT51xx3kgSq9TR/0VupvwvXr8rsffnhr1+eDY0amzieflKGhXz+ZB+Li8pZVFaBr1+Z721Lvk4624ObNSYDvjtiX58HT0qR5NG68hMASkw4hX7z5prmN5LZkHDhgPmcxj5ugUrN9953sx8c75kRTkZFBQ7MAHlXactzjDqTG0FCyRYu8c7D6XPPny/6pUzJ56nRcUr06lxgr/9prcjgzk7w8V4T75dPzWnxVUeXPMcZV1j7z+9XrZe3p5mZapzqFpCRRFEyZ4lz5wmDBAvJ3DGVmUKfC9dmC4vp1k2aXY8bI3x9+KPRtN26UW9WqJYatfOBU3ynxztq8uQjEO3YYH1Plh9250/TkDnk9VY2CMf/rVO9P2aIFGX/VwIVTzxGQBW1uLFwoixS1TxYJVPO5w+VrwaHW/1KVQP5Z1ZokcdgwI7fos8/SUL06GzYk78RqEmBX7HZmMDchNVUmh8BA6eyWWLhQqvb33yKvensXvRLc9CNeXmRCAjMVD/7U+LVb43W9/XYxE48dS4fattBQ0U60aMGEwQ+axvHZs22UHT7cPNDv2mU+XquWkFc2bSr+OMb0s4VAqU+ijrai4CFu3Vposy1xef0Bbgu/qHqvcMyYIjdyFAgGg8yh27aJMcFZhIaGsnfv0AJZ/d9/n1Sgp8HDg1EjJjEO9XjlzidM57OyaJUmduJEcrnuPurr1eeU13Po4kImL5ax8+V6P7J3b/Lv9WnUV/GRGdkBVN5eQCzXzMmRRaIFQaxeT46v9iuzFDem1/PnzT93mM751ctkls6DfPllU/0LxWkaFCT9t5gwebLUtUULmizzFgpCE4xU5JJi3TFKvU862jrUb0wC/H6ObQLmqVNJIJRAaH5eOmYkJ4v/oaLIysISSUny4po0sS0J6vWiUR01SvZ/Eusmd+/O/3eNObkTP5xv+7ya7veDD2yf79iRVMevESPEXDx3LkMBhhpJun/5RW5x6BB54gkZjLatu5nnVgaD6FOmPGn0dzIK/9evy1Thi5tcX3M0B+Avdujg3BiitjlFsaKJLhZMn07uQ2fqhwwtfJ8tKNLT5fsDQhKe23p7i9i0ybHFxwJO9Z0SD6pLTRX2KlPyuIgIIRjs0sW5G6jBTcaoifu/DkNMDHD3PQq+39oEbdvCJlOFGvBvzBRaNFCp14opbev1eq3hl2zNRXzkiJF6OCYGijH15oI/5PcDPWPwxx/O33/LFmFGOXUKuPtuM50TKbSoHTpIXNjEiVLum2+Krm4AJJNbnTpCDbJkCdyZCaXaLWZuCw6WaM1vvwWmTpUK2ULnzhJBcf48XAPNAUE2swlaBgxZfuOAAGDZMokUW75cyDc1OERcnDmgDjk5wNSpqDc0GL3m3I9JkyQW56efbPOXlhS2bpXm0bu3MOhduuT8tWfOSAZTy77qCGfPAu1qxUHJzES9HgE4iduQfUSCT7KyJBazWTMJ9jx+HFi2NAXD8Ad0I+/HXfe4QK8Hfsm+H9n1G+P2K//DQw8Boel/QpeaItH7DtCzZ67/XVyEwseCIFanA5RRD6IntyP2igu8Bt+OhKmz8Fu4AfWvHIKbITOfKKcCYORICTpVqUiKGO+/D4wdK3HXs2fL0PDllxKEaAmVwzlXluNyh5zULCTBF53Dato8/+yzEjzs4mKewvKFj4/kzX3zTXOgkwpfX6Fq+u472OQv1emEImvDBun769YJc4Azc/499wB9+6LarGnmQGZLLF0q97c3sY8dKwF8c+ZIPucpUyS1naurBP/BzDRx8CCQGXUW11ETzTvlnYcURYb+w5frSoTY+vU4cUJkmQ0bgDUP/ITBN37BRpch6H16KYKDAUfJ4PR6mWe7dhWZZexYG6mNixCxsUBD3WXoGtQvvh+xB09PGeBnzRIWCjXtZCHRv7/FvFIUcFZyLqrNxyeYH/vPIxs0kM3NjbzrLitR3uHqRU15BYjDtsFgcmECxEWoxJCVJelXjCb/ooJa/z39X2cm3HjtspiLVGv9Bx9QnJ6GDpULjLacxe3m0N/f2nJ0+LAoS1euFBOD5blnnhHN7w8/yH3vvVfKqXEu3y42yKp68WL27SteCLn9nY4eNd9/82YHGr6TJ8Vssn8/4+PJzbPFnHX4yc+Z06GzaBcA/q/357e2elWdqwcPNqc/dFQOYPZXCwmIV4RNjxc1YM7V1fqeDz4oxwuc3tAuSl2r5GhzRkPcu3dobsu6CamSHEms4gaDWVPQoQNVc5HBIJaPoKBb9z5KTi6c4mH6dHGf+fZbeayvv3buutDQUHp6hhJwPrZywADyqdZbqfrP/urzBBM86lK/ag0TPOvxSSxk40YGVqlCNmok2loC5NatNBhE8dW6Nbmn14vMgDtjjiTKeFC7dr5OidnZ4uWl08mYYg9JSeRff5GrliZwlet9JMC5uhf4WTOjWsuowi60tkn1a5g799bvkQ/0evL4cfl/xw75uS++MJ83GOQ9AzIl5WPyLvU+6Whr416Nkbp2Dq0tTZqE0s8v1GElixSqJfjff6WNjilArMihQ9JYJ060Pp6TIxa6IUPsX3vtmpiGAbHoGbXboY0aMVRRyPh45uTIPDhxInnUfzAP6DrbHYPuuUdcvThtGqnT8dHh11m9OrlnDyVwsG1bMkxcoL5pPJ2AgRMm5FWqkyblN5ctE00nIEGgxYHsbLKpXw5zFBdyypSS1xCXPpzqOyXeWXW6zkz0biCj+bhxkr91zx6rJ8/3Y9UxRhM+9JDp0CefiJ+whedFyWDp0iJlmCDN9d///BISYOSKkyTNA/maNZRB5amn5AJjANjhgS8TEL9zUkw53t40LRbUzqde0qSJeS2imq3VrX59MmPj31RNYWtW6QkIW4CKiAiRFS2vszxvhWeekQL+/nzojnjOx9NMgydr4DrfbfS16QYrRyy5tc6alES+9Vb+fh3Hj5sfNiKCTZs6WEStXm2qvxV272YeR/XCodQnUUebMwJxnTqhbNXKtjCrWjW/+45ilgfId98VCbZ6dVPuVDU2xgl/MJsYMkSEGlsuU85g4EARNA0GifsYNsy563r0CDWaoMX8ahebN0sQQGIimzUjv+y2RCp86hR/7vgxCTDZsxbTITmHU0c+ymZN9PT1JRP63S0KBOPCTLU6D/QxDgojRsjfmTOdeubBg8muXZ2rH0n+s9XA+brnSIA5gS2FDcCIIplcg4LMqdKLAzEx0uaMkm7PnrIQVtcOavb1O+6Qv7njs3Kh1Pukoy1I8eT2OtZKptwocYHoxg0zZREgDbggGD9eJht1VUOS8yR2xhTfYQ8jR+YpF9qlC0MB8vPPSYoX3e23k+ertGJE9fvs3YmvvCLxhYYdEhU7WvmFkydTNE/q/TIzTcHYe9s+RjdkslOnvNnZe/cWGV3VtUyYQBNzQkFgMOSvCAgPJ+vA6Ooxd64mENvZSryzVoM4/OeJVLVAvh+rc2e5R66IABPFTDmHWv+o73eRALe/vpqkOMUD5LnjafLPjBnmiwIDmTp8JAGJ9yJNcYZcu1Z8IwMCpBOSZtorSy3Y6dMSX3PwoNEt9qGHqAqPhvV/csIE2V2wQEgwqlWTBfHevXLNuHF570lSWCSqViV79KDe3YOb0I9p7lV5fdjD/OknsrruJtN0IrmvH7e8eDurXi9+ZAAZHc2EBAfBXJGRUk59acWHUp9EHW35CcR9+oRSpwslkJe9JSpKFOkA+Xf4ZfG/7t7dLIm88YbMAtHRzMwkGzfU8+Eep803SEwkr1zh9et5I7gNBrOf7fXrMt8CEtRs6RPvDLtQTo453pIUbZGHh4kAxSHatg01CcTPPWejgF7P1EnTTRSNhubN2VW3j5t6vSV1z8riuvHCDpEJN84cc4iGKVNJgCkfz+OVX4zqI4vIm6wskUkV6JlYVXxGGRLi2Dpigfj4gtNORh9JZU7zQPktC5qgIumvKg2X6hCYliYDUmFw+rR8QL1eooMB8kuhkltpJOMID5ei6z4+zIHYwD2zttIF2fzoI4d3LvU+6WjrBB13dM+lTc2FUhGIevemyWG2oJRfV67IHDJ0qHT8t96iaQWTH0VNVJRY+yxW66GhoQz18SHbtycNBj79NFnV18B0xZNrW9lX086fLz8bG5PDZM9a/EF5RMagl14STfQ1o9+2wSBtGuDVDgPoXz2Rvr5CyEHKnAlYkAtQdDr+/jJ+ORuUd+EC2auX6McsYw5yIySEHO5nZkjSBOIyIhC3QFVm123g0KyXmprKVEdLHlUbUjju1zILtf43ziWSAP8ZKhHYzz8vspz+xEmaVW5G9O9PhoSwQweJG8vOlgnzvTY/m6SUTz+Vy479coi7+r3Oj/Aab054TcJsX3tNfDGSk+V+qqlp/HgRYu67j9nZooXrpDvEL6q8zrlVXmPiU+br9a++xv81e40zlde4eUI4t24VTUvWwiVU1S4/hRnt0RZqmEWLhA6GAP+dtjH/719Y9O4t0lN+fDcpKfKcubksix6lPok62vITiJcvTyWQanbnMUKlFlO3xFFPSZuy1PJcvCg2amPAza6+r5MAN09cJbNCq1bMqtOAzapdZ3CwNTXe3LnyGSMjzZ4wn3wiGhw1fmfDBpl/bQW/W0Jl01N5kVUTpoN1uwkvvJBKd/dU9uola/Xc2DNBOMWPd3uU3LCB2Q0aMwPuvFknwMQffmLdGeqhMLzDDDF1GwzkoEFi4vHzE36hXH1C7c+XH3tNAlMt32txYft2EeItLCRF0l9PnJAP1bWr0AK1aSMawYJylao4eFCub9FChBVArBGBgaRez5wc+TckOIuGVydZNdRdXqH8vzvsROqI+q7U+6SjLRjgsfF5eXQtUexjrC2orEzdu9/a9SqFYGio/B079pb5GlNTU5mqusTt38+FC8l6iCMB/j5knt3r1q+XS/74g/zV9SEmetQRf4jatU2WLissXUq6ujKzZRDv63ia3kjhS+NTOOq+TPr65nVZ2rjBQG+kcOqLKTL/OKjfunUyNfv4SPcfNswo8+fylVEty6uf+VP+2batdL5/6cKpvlMkHRDAYABRAE4DeMNR2WCAhqnWVGIFxrx5ztszyzEMBjLSpT3j3eoz7fQl9u0rKz3TbL15s7nwY4+RjRtz8mQREhYtIh+B0QZdvz556RITE8lW3ud506M2cxQXpiueIj2om6r5MRjMs+3hw2IncnUlL19m8tHzvOFam9lwod4j1/WenjR4epp4VUciXG7h04P6lq14M9FAX19yVdAU80rfiM+fPMwoBPK/P2KK/8V+8YU4gzmDIUPyWCKKASU6YRakv5JmgfjKFZEzjh+3do2YMEF8Utu3t6ZWu/de4TpdvZrc/Ue8tBFbDAhGTQqnTKFBp2O24so41OPJfmJyyIYLwz0epouL+N5mZMhaxt9fLlsa8hUP1B9C/7rp1Gdk8UiLEYxDPWY39OPUtitNss7CheafTEmx1v5+aaTijTbS7mZlWTEC5sWePVLZI0fYo4doad58U/qeuqYkSYPewNMebXgAHanAwPBw8t+V8VyLYfKD/fubykauPcvsLIsXGxMjWjFFEUE0F/R6sfwwI8OxeqioceFC8ZCnrlghLx2Q+BA3N/KFF6SxPf64MBU0aCDjkSOOvsxMaYx165odgwcPNtMJ/PAD+fDDTK1ajzdQnQQYXnMCn+u8nfzqK2a4ejMVXsyqVY9J3Qbw0I5UWYilpcnCpKz3V4DJPzqxkitpqPR+77xza9dnZpqpQt59t/BUpzduiBmof3/u25LEEIjkuPnVP+xeolLZd+5MjoEx+EYl7F+3zvZFERHSjy0WXenwYPiABdblrl41a9HVrWrVPDyA2dlm5pT27c0KcIDc9eh8MXVZ+OOPHClrwfSvlkihwlpeyidKRiAG4ALgDIAAAO4A/gPQxl75YIA8e9bhk8+fP5/z59uhWakEsKz/hjlHmIwqPF6rJ+vXzBQi9aVLqfoemvDmm6ROx3+3ZBMge3ruZ7riSUPXbiKt9OxJ3rzJ83WCmYiqbO0SldffV3UknjlTfLzVlbzqdzt9OhkcTEPVqozbap9TJuVGJhOCejHbswr/fFx8Rpd2mG2SsffuzXuNwWAWtCrh9y/JybVA/ZUUgXj71my+5DaXH2MS38NUvvP4WRoM8r2qVZvPDh3mm6zeVy5m8eqbn3MmJnFzrzfFkvOeMTuRLZ6nrCwJEgVIPz+mrP+H2RDn9K8xnh+6TyMBbn3mVwIS06hqhO8NPMxMuJEA17ebJBMlwJ+Vh3i2ShteRw1+/kYsn7w9ih9gMk/ePYnJz0zigmqTOL7uSuGjTU3lj8GfsmftKKs5dvRomVsOH871vGlpYtcEqO8cTDfdFxwV/CL/e2kJAYPVOnX/B6KV2fjI9+zdW2S8hx8WV4crM5faFHStsHWrFR1aWUSR9tfoaBnLYmMl41mVKuIDBsgi9p575P8uXcT5/733rOMGDAaxWKnq/atXRQCLi7NeRbm4MHv0w1zq+RRHQBZN06bJLZbPOMbP8Ty/xf+RAOdgIhs1NDDpUWNGtbLeXwFT1k57KLUxdvlyx5Gc+SEqqkgSCZjqv3gx6eJCfYdOfFWZRQI8/Kt9LrqMDHNy2jtDrsqOTicLL0fuSlFRouGeOZNHHpvJfTUl8I5TpkibjYoimzcnPT2Z8dpbnFFtJj+pO5P6Tp3l/kZXn4sXzZS+lnzHOVl6/tTwVRJgTt36UuDll3n2jJ46nXQJfvihHE9J0ebYYhSIewDYYLE/GcBke+WDPKrm++SV0L/FCrnrv+YRodFYgHH84fVI0ZoA1qGrRgfjnI2b2NdnL8/Bnzdr+MuEoEb4GrUlw7CWgMy1VjAYREOsrk4tNaO3324+7gzZcWysaHMAZru4sxbi6eoqcnlB618JUJITbIH6K0m08m/FHa6iuchx96Re0fEGqnPZfT8z4rNIeqML2zbtwuO/RbIbdvGifw8SYCq8aNDpJMNLrVqOo8EPHxaHdONkl/7RZ7zeMoRrf7rJs1GZktVKUbij/1S2RSQ7u0fyzqaHmd62My+jLn/Bg/Jbrq7k6NF89FEyEFFMgydzOnSmwceH2XBhKryYpngxA2LFWFPjUaa1kAxcaS5VhGIiMpKMjGTsxkj2qyPbwR8ieXKFbNdGS3BZ/Bjphx1RTQJ0AK7BcH7y0AFT2f01+jNW15AZSZm8ccOc7MvoOlwhUGz91TJhSP/+ZlPwsmXCievlJQJJ06biG3PokDmA6oknbN9z6VL5CEbS1xMnxJ94zRqzZj8rS5R9K1eSZ4Y+R4OicJuLjH/zXV8gy3h/DQbyFTor4RhrBav6r1snCy9jW0u45NiVwM9Piq5cSRmXVMG2IMjKMgfd3HmnjI+1a5tYAf40ejdMejqZN0OFDz9qxCT2qRHJrl6RXPthpGmc4uHDpnb/lcuzvHtoJq89JCnh9zW7jx10hxn3V6SkC/X1zVv/yoESE4jvB/CNxf4jAObZK9+mQfN8n7wSfiwr5K6/wUBu6PSaeXJQhVtLRERYnU+HB5P+tshUpGpNpk/n8OEio9ickJOTZcKoVs3apqym1sovu5Altm8n3dxoeOghE8lEfgHBtupfCVCSE2yB+iuNE2yy4sMrcyQyXH/qDE/X7GJqa6HGTd2/CV+ORLjIJCdPCnUDIBxet4rUVHPSlVzbrJ7LWd/7JvV+/mJWv3bNlHVsTV8jh2CvXrxxOIatWonM/Ofv2Tw9agoJ8ArqcDR+YkxAH5v3t7V9hacIkL/iAYYC7FO3PjlnDrMUtzxlN99hzsJ28aIoKQuf7bvsoFj764AB4iRpz6q4c6dZ62vU/PKjj4ouw0tKChkQQL2rG19y+ZxNmxjIst5fXV3zrVYlHGOtkKf+Bw4wwasBL7g0yffagQPFcyMnh2KhUBQZ5woKg0GCLgAZEHK5MqiZ1F2Qzfl4Ov9xadYszp5lMO4a+CI+pR6KdZmgINv1r/hwqr+53jqDsQmKjWO0KqAo4wGMBwB/f/8i+MnKBUUBBuz+EEfnDUCbxklQFORlVO/XTwjPU1ORnAxcq9cWzUJbm89/+CFw//1AcDCWJgDx8ZIgJQ98fIBt24Br14AqVczHR40CWrcG2rd3/sF79gQiI6E0bowvPIEnnzSToGsoNeTbX4Fcfda3AVIjdqJetyYAAF2LAPif34Z9s/5CdnIG0ldOFz7+d97BpUvAIY/uGFXXDwMGAKgWCOzcKRllnE2+Ywve3sDixcDjj8MQexnnzkniCqVxIzzRugcGXwJ0tXYL8X+tWmhfC9i7Fwhq+xJwMATo1g01XF2xfbvkf2jb1hUY9j5O3TsSJ5Ia4sHadVGr/wPAvxslC40Frl6VxBsqDO6eqNNhEH5zBXSZS5H67mlUaeALvPgiYtsMQeyGSFNZnYcbek0ebNpv1AjYswdITLz1V1GpsHw5cOMG0LSp7fMhIcB//0mWIb1exqi2bYvu96tUAf79F7q0NIzPaYHJtYru1k6iwP01oEGD4n6miodOncDDkdDHJOVbdMkSaWouLgBeeQUYNgwIDCz4byoKMHkyMGiQZPyoXt3q9FdfqQmzXAHOx7aoB9C12TV4eNi4V7NmQHAwXiLQoSOQkKAAeAlbzoaiW51o+PoYy7VrV/DnrERQyDx9q2A3UJQeAN4mOci4PxkASH5oq3yXLl24b98+h/fs27cvAODvv/8u1LOVV2j17wugUtXf1qRXPD9UwP4K5N9nK+H3soJW/74AKlX9y3V/BSrlN7OCVv++ACpV/Z3qs0WRunkvgEBFUZopiuIOYBSANUVwXw0aNBQ9tP6qQUP5gdZfNWgoIRRaQwwAiqIMBfAZJCL2W5LvOygbD+B8oX/UMWoDuFbMv1Hc0OpQdlDc9bhGcnD+xYoGBemvxvLF3WcrQjupCHUAKkY9tP6q9VdnUBHqodXBOTjVZ4tEIC5rUBRlH8lCOCyWPrQ6lB1UlHqUVVSE91sR6gBUjHpUhDqUZVSU91sR6qHVoWhRFC4TGjRo0KBBgwYNGjSUW2gCsQYNGjRo0KBBg4ZKjYoqEC8s7QcoAmh1KDuoKPUoq6gI77ci1AGoGPWoCHUoy6go77ci1EOrQxGiQvoQa9CgQYMGDRo0aNDgLCqqhliDBg0aNGjQoEGDBqdQYQViRVFGKopyVFEUg6IoZSKC0VkoijJYUZQoRVFOK4ryRmk/T0GhKMq3iqJcVRTlSGk/y61CURQ/RVG2KIpy3NiOJpb2M1VkaP21dFHe+6zWX0sWWn8tXZT3/gqUzT5bYQViAEcA3Avgn9J+kIJAURQXAPMBDAHQBsBoRVHalO5TFRhLAZQYT2cxIQfAKyRbAwgB8Gw5/A7lCVp/LV0sRfnus1p/LVlo/bV0sRTlu78CZbDPVliBmORxklGl/Ry3gG4ATpOMJpkF4FcAI0r5mQoEkv8AuFHaz1EYkIwjecD4fzKA4wAale5TVVxo/bV0Ud77rNZfSxZafy1dlPf+CpTNPlthBeJyjEYAYiz2L0Ib2EsViqI0BdAJwO7SfRINZRBafy1j0PqrBgfQ+msZRFnps66l+eOFhaIoEQDq2zg1leTqkn6eIoJi45hGBVJKUBTFB8ByAC+STCrt5ynP0PqrhuKG1l+LDlp/1VASKEt9tlwLxCTDSvsZigEXAfhZ7DcGEFtKz1KpoSiKG6Sj/kRyRWk/T3mH1l81FCe0/lq00PqrhuJGWeuzmstE2cNeAIGKojRTFMUdwCgAa0r5mSodFEVRACwGcJzkp6X9PBrKLLT+Wgag9VcNTkLrr2UEZbHPVliBWFGUexRFuQigB4A/FEXZUNrP5AxI5gB4DsAGiJP5/0geLd2nKhgURfkFwE4ALRVFuagoyhOl/Uy3gF4AHgHQX1GUQ8ZtaGk/VEWF1l9LFxWgz2r9tQSh9dfSRQXor0AZ7LNapjoNGjRo0KBBgwYNlRoVVkOsQYMGDRo0aNCgQYMz0ARiDRo0aNCgQYMGDZUamkCsQYMGDRo0aNCgoVJDE4g1aNCgQYMGDRo0/H+7dSAAAAAAIMjfepCLojUhBgBgTYgBAFgTYgAA1oQYAIC1APA8pkCK1wRPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spike raster and histogram - Fig 3b\n",
    "bin_counts = 250\n",
    "unit_count = len(alm_insert_thal_stim_unit)\n",
    "fig3, axs = plt.subplots(4, unit_count, figsize=(12, 6))\n",
    "for u_idx, unit_spiketimes in enumerate(alm_insert_thal_stim_unit):\n",
    "    plot_spike_raster_and_histogram(unit_spiketimes['contra_ctrl'], unit_spiketimes['ipsi_ctrl'], \n",
    "                                    axes = (axs.flatten()[u_idx + unit_count*0], axs.flatten()[u_idx + unit_count*1]),\n",
    "                                    ax_title='ALM-Thal-ctrl', bin_counts=bin_counts)\n",
    "    plot_spike_raster_and_histogram(unit_spiketimes['contra_stim'], unit_spiketimes['ipsi_stim'], \n",
    "                                    axes = (axs.flatten()[u_idx + unit_count*2], axs.flatten()[u_idx + unit_count*3]),\n",
    "                                    ax_title='ALM-Thal-stim', bin_counts=bin_counts, shade_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 units with Thalamus insert and ALM photostim\n",
    "s1 = (thal_insert_alm_stim & {'subject_id': 'anm00260487', 'session_time': '2017-06-27 09:36:39'}).fetch1('KEY')  #20141007\n",
    "s2 = (thal_insert_alm_stim & {'subject_id': 'anm00234218', 'session_time': '2017-06-27 09:35:21'}).fetch1('KEY')  #20140617\n",
    "s3 = (thal_insert_alm_stim & {'subject_id': 'anm00260487', 'session_time': '2017-06-27 09:36:48'}).fetch1('KEY')  #20141043\n",
    "thal_insert_alm_stim_unit = [make_unit_spiketimes(unit=unit_no, sess=session_no)\n",
    "                             for unit_no, session_no in zip((5, 3, 8), (s1, s2, s3))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spike raster and histogram - Fig 6b\n",
    "bin_counts = 200\n",
    "unit_count = len(thal_insert_alm_stim_unit)\n",
    "fig3, axs = plt.subplots(4, unit_count, figsize=(12, 6))\n",
    "for u_idx, unit_spiketimes in enumerate(thal_insert_alm_stim_unit):\n",
    "    plot_spike_raster_and_histogram(unit_spiketimes['contra_ctrl'], unit_spiketimes['ipsi_ctrl'], \n",
    "                                    axes = (axs.flatten()[u_idx + unit_count*0], axs.flatten()[u_idx + unit_count*1]),\n",
    "                                    ax_title='Thal-ALM-ctrl', bin_counts=bin_counts)\n",
    "    plot_spike_raster_and_histogram(unit_spiketimes['contra_stim'], unit_spiketimes['ipsi_stim'], \n",
    "                                    axes = (axs.flatten()[u_idx + unit_count*2], axs.flatten()[u_idx + unit_count*3]),\n",
    "                                    ax_title='Thal-ALM-stim', bin_counts=bin_counts, shade_bar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population - Fig 3e and 6e\n",
    "Similarly, for the entire population, instead of picking a few representative units for plotting, here we will query all units from all sessions constrained by:\n",
    "1. The recording/stimulation brain regions\n",
    "    + ALM recording with photostim at Thalamus - only pyramidal neurons\n",
    "    + ALM recording with photostim at ALM - only pyramidal neurons\n",
    "    + Thalamus recording with photostim at Thalamus - filter to VM/VAL neurons only\n",
    "    + Thalamus recording with photostim at ALM - select all thalamus neurons\n",
    "2. The trial-condition\n",
    "    + good trial\n",
    "    + with/without photostim\n",
    "\n",
    "The routines for plotting the spike histogram is analogous to that above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trial_seg_setting': 0,\n",
       " 'event': 'pole_out',\n",
       " 'pre_stim_duration': Decimal('1.50'),\n",
       " 'post_stim_duration': Decimal('3.00')}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_param_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get trial-segmented spiketimes for all units in all specifed sessions\n",
    "def extract_segmented_spiketimes_histogram(units, trial_cond, seg_param_key, time_range=(-1.5, 3), bin_counts=1000):\n",
    "    unit_keys = units.fetch('KEY')\n",
    "    print(f'Found {len(unit_keys)} units')     \n",
    "    \n",
    "    def make_spike_histogram():\n",
    "        for idx, unit_key in tqdm(enumerate(unit_keys)):\n",
    "            trial_spikes = (extracellular.TrialSegmentedUnitSpikeTimes & unit_key &\n",
    "                                         ((acquisition.TrialSet.Trial & trial_cond)\n",
    "                                          & (stimulation.TrialPhotoStimInfo & trial_cond)))\n",
    "            # Verify if a unit has at least 5 trials, if not then exclude\n",
    "            if len(trial_spikes) > 5:   \n",
    "                trial_spikes = trial_spikes.fetch('segmented_spike_times')\n",
    "                segmented_spike_times = np.hstack([spks for spks in trial_spikes])\n",
    "                if segmented_spike_times.size > 0:\n",
    "                    spk_counts, spk_edges = np.histogram(segmented_spike_times, bins=bin_counts, range=time_range)\n",
    "                    yield spk_counts / np.diff(spk_edges) / len(trial_spikes), spk_edges[1:]\n",
    "     \n",
    "    output = {'data': np.vstack([x[0] for x in make_spike_histogram()]),\n",
    "              'timestamps': next(make_spike_histogram())[1]}    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get segmented spike times for population - ALM/thal-insert, thal-stim\n",
    "stim_trial =  {'trial_is_good': True, 'trial_stim_present': True, 'photo_stim_period': 'delay'}\n",
    "ctrl_trial =  {'trial_is_good': True, 'trial_stim_present': False}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply selection of putative pyramidal neurons, or VM/VAL neurons only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract matched units\n",
    "alm_insert_thal_stim_units = extracellular.UnitSpikeTimes & alm_insert_thal_stim & 'unit_cell_type = \"wide width spiking\"'\n",
    "alm_insert_alm_stim_units = extracellular.UnitSpikeTimes & alm_insert_alm_stim & 'unit_cell_type = \"wide width spiking\"'\n",
    "thal_insert_thal_stim_units = extracellular.UnitSpikeTimes & thal_insert_thal_stim & 'unit_cell_type = \"wide width spiking\"'\n",
    "thal_insert_alm_stim_units =  extracellular.UnitSpikeTimes * extracellular.VMVALUnit & thal_insert_alm_stim & 'in_vmval = True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 645 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "645it [00:25, 24.49it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 645 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "645it [00:16, 39.07it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 127 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "127it [00:04, 25.57it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 127 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "127it [00:03, 41.83it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 179 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "179it [00:08, 21.72it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 179 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "179it [00:04, 36.26it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 165 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "165it [00:06, 25.00it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 165 units\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "165it [00:04, 39.92it/s]\n",
      "0it [00:00, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "population_spikes = {name_k: {'ctrl': extract_segmented_spiketimes_histogram(sess_k, ctrl_trial, seg_param_key,\n",
    "                                                               (-0.2, 0.5), 500),\n",
    "                          'stim': extract_segmented_spiketimes_histogram(sess_k, stim_trial, seg_param_key,\n",
    "                                                               (-0.2, 0.5), 500)}\n",
    "                 for name_k, sess_k in zip(('alm_neurons-thal_stim', 'thal_neurons-thal_stim', \n",
    "                                            'VMVAL_neurons-alm_stim', 'alm_neurons-alm_stim'),\n",
    "                                           (alm_insert_thal_stim_units, thal_insert_thal_stim_units, \n",
    "                                            thal_insert_alm_stim_units, alm_insert_alm_stim_units))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig36, axs = plt.subplots(2, 2, figsize=(6, 6))\n",
    "fig36.subplots_adjust(hspace=0.5)\n",
    "fig36.subplots_adjust(wspace=0.4)\n",
    "for s, ax in zip(population_spikes.keys(), axs.flatten()):\n",
    "    plot_with_sem(population_spikes[s]['stim']['data'],\n",
    "                  population_spikes[s]['ctrl']['data'],\n",
    "                  population_spikes[s]['ctrl']['timestamps'], ax)\n",
    "    ax.set_xlim(-0.02, 0.04);\n",
    "    ax.set_title(s)\n",
    "    ax.set_xlabel('Time from delay (s)')"
   ]
  }
 ],
 "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
}